Sunil_Valaparla_Dissertation_Final

MRI AND MRS OF SKELETAL MUSCLE LIPIDS IN TYPE2 DIABETES MELLITUS
A
DISSERTATION
Presented to the Faculty of
The University of Texas Health Science Center at San Antonio
Graduate School of Biomedical Sciences
in Partial Fulfillment
of the Requirements
for the Degree of
DOCTOR OF PHILOSOPHY
By
Sunil Kumar Valaparla, M.S.
San Antonio, Texas
December 2014

Sunil Kumar Valaparla
APPROVED:
_____________________________
Geoffrey D. Clarke, PhD, Supervising Professor
_____________________________
Timothy Q. Duong, PhD
_____________________________
Beth A. Goins, PhD
_____________________________
Muhammad Abdul-Ghani, MD, PhD
_____________________________
Amalia Gastaldelli, PhD
_______________
Date
_____________________________
David S. Weiss, PhD
Dean, Graduate School of Biomedical Sciences

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DEDICATION
“Trust in the Lord with all thine heart, and lean not unto thine own understanding. In all thy
ways acknowledge Him, and He shall direct thy paths.”
To my parents Dr. Veeraiah and Mary Rani, my siblings Asha Deepa and Hans Manohar, to my
beloved Mercedes Garcia, for their love and support along this entire time, and to my friends.
Thank you for everything.

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ACKNOWLEDGMENTS
I thank my parents for their love and support. Their dedication to their children and
unconditional love was a motivation to strive for my dreams.
In first place, I would like to thank my advisor, Dr. Geoffrey Clarke, for his guidance,
effort, patience and support that he has shown during the entire duration of this project, for all
the time and dedication he invested on this endeavor. I owe my deepest gratitude to Dr. Timothy
Q. Duong for all his support, since I started the program and his valuable feedback provided
through all these years. I want to express my sincere gratitude to Dr. Beth Goins, Dr. Qi ―Chris‖
Peng, Dr. Muhammad Abdul-Ghani, Dr. Ralph DeFronzo and Dr. Amalia Gastaldelli for all the
support and guidance during these five years, and the time they spent discussing the entire
project. Also I would like to thank Dr. Jinqi Li, Dr. Feng Gao, Dr. Daniele Giuseppe and Crystal
Franklin, for training me and providing valuable feedback.
Moreover, I would like to thank Loretta Edwards, Mary Retchless and Edie Kenney-Perez for
truly caring about students and for coming to work every day with the conviction of doing a
good job and provide outstanding service to the students. Finally I want to express my gratitude
to all my professors, friends and personnel of the Research Imaging Institute and The University
of Texas Health Science Center at San Antonio.

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Publication No.____________________
Sunil Kumar Valaparla, Ph.D.
The University of Texas Health Science Center at San Antonio
Graduate School of Biomedical Sciences
Supervising Professor: Geoffrey D. Clarke, PhD
ABSTRACT
Type2 diabetes mellitus (T2DM) and insulin resistance (IR) are characterized by excessive lipid
accumulations in skeletal muscle. Fat deposits, appearing as intramyocellular lipid (IMCL)
droplets within myocytes, provide a source of rapidly available energy during physical activity
but also may promote IR if the lipids are not adequately oxidized. Non-invasive localized proton
MR spectroscopy (1
H-MRS) has been widely used to study in vivo skeletal muscle lipid
metabolism; however inconsistencies in quantitating extramyocellular lipids (EMCL) limit the
accuracy of IMCL concentrations. The goal of this proposal is to develop and apply diffusion
tensor MR imaging (DTI) to improve 1
H-MRS quantitation of skeletal muscle lipids in T2DM
subjects. To achieve this goal, the following aims were proposed: Aim 1: Optimize the b-value

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for diffusion tensor imaging and fiber tractography of in vivo vastus lateralis (VL) muscle. This
study reported diffusion indices and fiber tractography assessment in the VL muscle within the
b-value range 400-800 s mm-2
. Superior quality fiber tracts were observed with optimal b-value
of 600 s mm-2
at 3T. Aim 2: Characterize the intramyocellular lipids and its dependence on fiber
orientation in skeletal muscles in lean normal glucose tolerant (NGT) subjects by DTI and 1
H-
MRS: comparative study of muscle fat fraction with two-point Dixon MRI. The results of this
study demonstrated differences in the VL, soleus (SO) and tibialis anterior (TA) muscles with
regard to muscle fiber orientation, IMCL, EMCL concentration levels and fat-water ratios in 12
NGT subjects. Aim 3: Determine the effects of age, adiposity and insulin sensitivity on skeletal
muscle lipids in lean non-diabetic controls and type2 diabetic subjects. The improved spatial
resolution and lipid post-processing using pennation angle data incorporated to correct for
chemical shift across different muscle spectra suggest that IMCL and EMCL observed in SO, VL
and TA are significantly different between lean healthy NGT and T2DM subjects. This study
also showed that IMCL content within SO, VL and TA muscles of T2DM subjects strongly
correlates positively with age, BMI across all subjects, with % body fat and fasting plasma FFA
and triglycerides measurements. Also, a significant negative correlation was observed between
IMCL content and insulin sensitivity.

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TABLE OF CONTENTS
Page
Title ............................................................................................................................................ i
Approval.................................................................................................................................... ii
Dedication................................................................................................................................. iii
Acknowledgments..................................................................................................................... iv
Abstract ......................................................................................................................................v
Table of Contents..................................................................................................................... vii
List of Figures........................................................................................................................... xi
List of Tables .......................................................................................................................... xiii
I. Introduction .............................................................................................................................1
A. TYPE 2 DIABETES MELLITUS.............................................................................................2
1. Obesity........................................................................................................................4
2. Insulin Resistance and Its Implications ........................................................................5
3. Fatty Acids and Insulin Resistance...............................................................................7
4. Triglyceride in Skeletal Muscle .................................................................................10
a. Increased Intracellular Triglycerides and Insulin Resistance...................................10
b. Physiological role of Intramyocellular Lipids in Skeletal Muscle ...........................13
B. SKELETAL MUSCLE LIPIDS AND PROTON MR SPECTROSCOPY (1
H-MRS).........................18
C. DIFFUSION TENSOR IMAGING IN SKELETAL MUSCLE .......................................................24
D. TWO-POINT DIXON WATER-FAT MRI.............................................................................26

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E. SPECIFIC AIMS ...............................................................................................................27
II. Optimization of diffusion tensor imaging and deterministic fiber tractography of human
vastus lateralis muscle in vivo ...................................................................................................28
A. INTRODUCTION...............................................................................................................29
B. METHODS ......................................................................................................................31
1. MRI Protocol.............................................................................................................31
2. Data Analysis ............................................................................................................32
3. Fiber Tractography ....................................................................................................33
4. Statistical Analysis ....................................................................................................37
C. RESULTS ........................................................................................................................37
D. DISCUSSION ...................................................................................................................40
III. Effect of voxel size, echo time and fiber orientation on vastus lateralis muscle lipid
measurements assessed by 1
H-MRS and two-point Dixon MRI .................................................47
A. INTRODUCTION...............................................................................................................48
B. METHODS ......................................................................................................................50
1. MRI Protocol.............................................................................................................50
2. Data Analysis ............................................................................................................54
a. Diffusion Tensor Post-Processing ..........................................................................54
b. 1
H-MRS Spectral Analysis.....................................................................................56
c. Two-point Dixon MRI ...........................................................................................57
C. RESULTS ........................................................................................................................58
D. DISCUSSION ...................................................................................................................62

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E. CONCLUSION..................................................................................................................67
IV. Variation of intramyocellular lipids and its dependence on fiber orientation in distinct human
skeletal muscles characterized by DTI and 1
H-MRS: comparative study of muscle fat fraction
with two-point Dixon MRI........................................................................................................68
A. INTRODUCTION...............................................................................................................69
B. METHODS ......................................................................................................................72
1. MRI Protocol.............................................................................................................72
2. Data Analysis ............................................................................................................76
a. Diffusion Tensor Imaging ......................................................................................76
b. 1
H-MRS Spectral Analysis.....................................................................................77
c. Two-point Water-fat Dixon MRI............................................................................80
C. RESULTS ........................................................................................................................81
D. DISCUSSION ...................................................................................................................82
E. CONCLUSION..................................................................................................................91
V. Skeletal muscle lipids in lean non-diabetic controls and type2 diabetic subjects: effects of age,
adiposity and insulin sensitivity.................................................................................................92
A. INTRODUCTION...............................................................................................................93
B. METHODS ......................................................................................................................94
1. MRI Protocol.............................................................................................................94
2. Metabolic Analysis ....................................................................................................98
3. Data Analysis ..........................................................................................................100
4. Statistical Analysis ..................................................................................................102

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C. RESULTS ......................................................................................................................103
D. DISCUSSION .................................................................................................................116
VI. Future Directions ..............................................................................................................123
VII. Conclusion and Significance ............................................................................................126
APPENDIX A.........................................................................................................................130
APPENDIX B.........................................................................................................................133
APPENDIX C.........................................................................................................................141
APPENDIX D.........................................................................................................................145
LITERATURE CITED ...........................................................................................................150
VITA ......................................................................................................................................157

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LIST OF FIGURES
Figure 1. Consequences of increased adipose tissue mass on skeletal muscle metabolism............9
Figure 2. Relationship between mean free fatty acid and intramyocellular triglyceride ..............12
Figure 3. 1
H-MRS high-resolution spectra of vegetable oil........................................................19
Figure 4. 1
H-MRS high resolution spectra of tibialis anterior (TA) muscle ................................21
Figure 5. Cross-sectional, diffusion-weighted images of the thigh mid-section..........................34
Figure 6. Cross-sectional T1W and diffusion indices images.....................................................35
Figure 7. Muscle fiber tracts in the vastus lateralis muscle (VLM).............................................36
Figure 8. Scatter plots of diffusion indices in VLM ...................................................................39
Figure 9. Tractography of fiber tracks as a function of b-value in VLM.....................................41
Figure 10. Relationship between SNR and fiber density index...................................................43
Figure 11. 1
H-MRS of vastus lateralis at different voxel sizes and echo times ...........................52
Figure 12. 1
H-MRS lipid peak fitting using AMARES algorithm ..............................................53
Figure 13. Variation in pennation angle and lipid estimates using 1
H-MRS and MRI.................60
Figure 14. Scatter plots and Bland-Altmann analysis of fat fractions .........................................63
Figure 15. Schematic representation of muscle fibers ................................................................70
Figure 16. 1H-MRS long TE STEAM acquisition at 3T in vastus lateralis (VL) ........................74
Figure 17. 1H-MRS acquisition in tibialis anterior (TA) and soleus (SO) ..................................75
Figure 18. T1 and DWI of thigh and lower leg in healthy subject ..............................................78
Figure 19. Lipid concentrations and fat fractions in VL, TA and SO muscles ............................84
Figure 20. Regression and Bland-Altman analysis of fat fractions from VL...............................85
Figure 21. Regression and Bland-Altman analysis of fat fractions from SO and TA ..................86
Figure 22. 1
H-MR muscle spectra from a 54 year old type 2 diabetic subject.............................96

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Figure 23. Two-point Dixon images from thigh and lower leg...................................................97
Figure 24. Pennation angle and chemical shift between healthy and T2DM.............................104
Figure 25. IMCL, EMCL and total lipid in SO, VL and TA between healthy and T2DM.........107
Figure 26. Mean fat fractions in SO, VL and TA between healthy and T2DM.........................108
Figure 27. Scatter plots and Bland Altman plots of fat fraction in SO, VL and TA ..................110
Figure 28. Association between IMCL, EMCL and fat fraction with age .................................111
Figure 29. Association between IMCL, EMCL and fat fraction with BMI ...............................112
Figure 30. Relationship between IMCL and % body fat...........................................................113
Figure 31. Relationship between plasma FFA, triglycerides and IMCL ...................................114
Figure 32. Relationship between insulin sensitivity and IMCL ................................................115
Figure 33. Sample size for IMCL concentrations between healthy and T2DM.........................119
Figure 34. Sample size for fat fractions between healthy and T2DM .......................................120
Figure 35. Sample size estimation for IMCL with and without pennation angle correction ......121

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LIST OF TABLES
Table 1. Descriptive statistics for fiber tractography measurements at different b-values ...........42
Table 2. Comparison of lipid and fat fraction at different voxel size and echo times at 3T .........61
Table 3. Comparison of estimated bias and SD in fat fraction measurements.............................64
Table 4. Lipids, pennation angle and fat fraction estimates in SO, VL and TA muscles .............83
Table 5. Comparison of measured IMCL with estimates observed in previous studies...............89
Table 6. Baseline Characteristics of T2DM study population.....................................................99
Table 7. Lipid and fat fraction measurements between healthy and T2DM ..............................106

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A. Type 2 Diabetes Mellitus
Type2 diabetes mellitus (T2DM) has become a worldwide health problem and an important
cause of morbidity and mortality [1]. Through lifelong vascular complications, T2DM leads to
excessive rates of myocardial infarction, stroke, renal failure, blindness, and amputations.
T2DM imposes a substantial burden on the economy of the U.S. in the form of increased medical
and indirect costs, reduced productivity at work and at home, reduced labor force participation
from chronic disability, and premature mortality. The estimated total economic cost [2] of
diagnosed diabetes in 2012 is $245 billion, a 41% increase from previous estimate of $174
billion in 2007.
T2DM results from disorders of insulin action and insulin secretion, either of which may be the
predominant feature and both of which are usually present when the disease becomes clinically
manifest. T2DM is preceded by insulin resistance (IR) and impaired glucose tolerance (IGT).
Once IR is pronounced, the likelihood of T2DM development depends on the ability of β-cells to
adequately compensate by increasing insulin secretion [3]. T2DM may be asymptomatic for
many years, and approximately 50% of cases in the United States and Europe, and probably
more in less developed countries, remain undiagnosed.
Large numbers of cross-sectional studies [4] have firmly established the metabolic characteristics
of people with T2DM. Compared with non-diabetic (normal glucose tolerant – NGT) subjects,
people with T2DM, on average, (a) are more obese, particularly centrally; (b) have abnormal
insulin secretory function; (c) are insulin resistant in all three insulin-responsive tissues (i.e.,
adipose tissue, skeletal muscle, and liver) and (d) have an excess rate of endogenous glucose
production. Although some people with T2DM may be relatively lean or insulin sensitive, this is
uncommon. These average metabolic characteristics of people with T2DM have been found

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consistently by many different investigators in divergent populations and are present in most
patients to one degree or another. The relationships between obesity, insulin secretion, insulin
resistance, and endogenous glucose production are considerably different in NGT subjects. In
NGT, increasing glycemia (glucose concentrations in blood) is not correlated with fasting rates
of endogenous glucose production, but is positively correlated with increasing obesity (and
central obesity), increasing severity of insulin resistance, and increasing hyperinsulinemia
(excess levels of insulin circulating in the blood than expected relative to the level of glucose) in
response to oral nutrients.
Several characteristics of people with T2DM are observed in non-diabetic subjects as well, but
which characteristic is a pre-diabetic abnormality cannot be determined from cross-sectional
studies alone. Such conclusions can be drawn only from prospective studies [4] in which non-
diabetic subjects are metabolically characterized and followed over time to determine who does
and does not acquire T2DM. A large numbers of prospective studies have been established to
study which metabolic abnormalities are pre-diabetic and which are not. The available
prospective data have uniformly identified obesity and insulin resistance as major risk factors for
T2DM among non-diabetic subjects with normal glucose tolerance.
In some, but not all studies, abnormal insulin secretory function was also predictive of T2DM in
people with normal glucose tolerance. Thus, insulin resistance and insulin secretory dysfunction
are metabolic abnormalities that can be identified in pre-diabetic subjects’ years before they are
formally diagnosed with T2DM. These abnormalities worsen as glucose tolerance deteriorates
from normal to impaired glucose tolerance and, finally, to T2DM. No simple metabolic defect is
likely to explain the cause of T2DM in large numbers of people. A complete understanding of
the causes of T2DM will require a better knowledge of the environmental and molecular genetic

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determinants of both insulin action and insulin secretory function, and, equally important, a
better knowledge of how they interact over time. Non-invasive diagnostic methods, such as
magnetic resonance imaging and spectroscopy, can help investigators better understand the
pathophysiology of this condition.
1. Obesity
Obesity develops as a result of an imbalance between energy intake and energy expenditure
resulting in accumulation of triglyceride deposits. The high prevalence of obesity in T2DM
patients indicates that obesity may be of pathophysiologic importance in subjects who
genetically are prone to develop hyperglycemia [4]. Briefly, increased food intake or reduced fat
oxidation, specifically in skeletal muscle, results in obesity, which manifests as an accumulation
of triglyceride in fat cells (also called adipocytes or lipocytes). Thereafter, when the fat cells are
―filled up‖ with triglycerides, then the triglyceride uptake decreases and plasma triglyceride and
plasma free fatty acid (FFA) concentrations increase. Initially this occurs only postprandially, but
subsequently also in the fasting state due to increased lipolysis from the big fat deposits. In other
words, adipose tissue works like a sink for fat deposition but when the sink is full, it will
overflow. Elevation of plasma lipids results in an increase in uptake of FFAs resulting in
triglyceride accumulation in other tissues such as liver and muscle cells and later on in β-cells.
The accumulation of triglycerides and long-chain acyl-coenzyme A (LC-CoA) inhibits insulin
action in skeletal muscle and liver, resulting in insulin resistance and, secondary to that,
hyperinsulinemia. As long as β-cells can overcome insulin resistance by an overproduction of
insulin, then glucose metabolism can be kept normal, but in time insulin secretion will
deteriorate in about 10% of the obese subjects, probably due to genetic susceptibility and the
development of glucose intolerance. Genetic variables may play a role in the development of

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both obesity and hyperglycemia in obese subjects. Characteristically, all obese subjects consume
too many calories (or metabolize too little) and tend to lead a sedentary lifestyle. Metabolism of
ingested lipids interferes with glucose metabolism in the muscles and liver, may potentiate
defects in insulin sensitivity, and may induce T2DM in subjects with genetic defects in insulin
secretion. Furthermore, lipid accumulation in tissues, together with abnormalities in glucose and
lipid metabolism, is associated with cardiovascular disease and non-alcoholic fatty liver disease.
Presently, therefore, the preferred mode of treatment and prevention is to reduce the intake of
saturated fat, especially in genetically predisposed subjects, and to encourage physical activity
with the aim of achieving a normal body weight.
2. Insulin Resistance and Its Implications
Insulin resistance is a pre-onset condition in which the glucose builds up in the blood instead of
being absorbed in the cells, due to ineffective action of insulin leading to T2DM. The underlying
premise is that T2DM results from a failure on the part of the pancreatic β-cell to compensate
adequately for the defect in insulin action in insulin-resistant individuals. However, the ability to
maintain the degree of compensatory hyperinsulinemia necessary to prevent loss of glucose
tolerance in insulin-resistant persons does not represent an unqualified victory. In other words,
irrespective of the degree of β-cell compensation, the more insulin resistant the individual, the
more perilous the outlook.
The ability of insulin to stimulate in vivo glucose disposal has been extensively studied for more
than 30 years, and there is abundant evidence that this action of insulin is markedly decreased in
patients with T2DM. Since the major site of glucose disposal in these infusion studies is the
muscle, it seems reasonable to conclude that the vast majority of patients with T2DM have a
defect in insulin-stimulated glucose utilization by muscle. It should be emphasized that this

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abnormality in insulin action on muscle in patients with T2DM does not depend on whether or
not the patient is obese.
Resistance to insulin-mediated glucose disposal by muscle can be seen in a significant proportion
of glucose-tolerant individuals and in non-diabetic first-degree relatives of patients with T2DM,
as well as in true pre-diabetes, that is, non-diabetic persons who subsequently develop T2DM.
As long as insulin-resistant persons are capable of increasing their insulin secretory response,
gross decompensation of glucose homeostasis can be prevented. When the insulin secretory
response declines to the point at which circulating plasma FFA levels become significantly
elevated, the plasma glucose concentration increases precipitously, primarily because hepatic
glucose production (HGP) is no longer suppressed by the expanded plasma glucose pool. Thus,
hyperglycemia occurs in T2DM when the liver continues to secrete relatively normal amounts of
glucose into the enlarged plasma glucose pool of insulin-resistant individuals.
Obesity per se can lead to a decrease in insulin-mediated glucose uptake, whereas weight loss in
obese persons is associated with enhanced in vivo insulin action. However, obesity is not the
only environmental change that can modulate insulin resistance; the level of habitual physical
activity is also as potent as obesity in this regard. Furthermore, it is known that exercise training
can enhance insulin sensitivity, lower plasma triglycerides (TG) and insulin concentrations,
lower blood pressure, and increase high-density lipoprotein cholesterol (HDL-C) concentrations.
The fact that the various components of the insulin resistance syndrome are not limited to obese
individuals and can occur in both obese and non-obese, hyperinsulinemic (and presumably
insulin-resistant) subjects is not meant to diminish the impact that variations in weight or
regional fat distribution and level of physical activity have on resistance to insulin-mediated
glucose disposal. Indeed, the changes associated with the insulin resistance syndrome are

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accentuated when persons become heavier or less active. On the other hand, it is necessary to
emphasize that insulin resistance, and the consequent manifestations of this defect do not depend
solely on obesity or a sedentary lifestyle, and that obesity does not equal insulin resistance.
3. Fatty Acids and Insulin Resistance
Excessive accumulations of body fat and dietary fat intake are both associated with insulin
resistance. The fact that insulin resistance increases with weight gain and decreases with weight
loss is reflective of the fact that fat accumulation is not only associated with insulin resistance
but is a cause of insulin resistance. A likely mechanism involves the release of one or more
messengers originating from the adipose tissue (or from ingested fat) that inhibit insulin action
on skeletal muscle and/or the liver.
Multiple pathways may lead to the condition of insulin resistance and several candidates for such
a role have been proposed, including leptin, tumor necrosis factor - α (TNFα), resistin, and free
fatty acids (FFA). Well-known contributors are adipose tissue-derived cytokines. These so-called
adipocytokines exert a variety of effects on skeletal muscle, and are thereby able to impair
insulin responsiveness. As a consequence of adipocytokines as well as inflammatory mediators,
general systemic as well as adipose tissue lipolysis is altered, leading to elevated levels of FFA.
Enhanced availability of liberated as well as dietary FFA has been shown to result in increased
amounts of ectopic lipid stores in non-adipose tissues. These lipids, together with their
metabolites, are able to contribute to the development of insulin resistance . However, the precise
mechanisms by which ectopic lipid stores develop and how they affect insulin signaling are still
under investigation.
Besides FFA, endocannabinoids (EC) have also been described as another class of lipid-derived
mediators that are able to contribute to the pathogenesis of obesity and insulin resistance. The EC

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system is an important modulator of energy homeostasis and has been shown to be deregulated
in obesity and T2DM. Under normal conditions, the level of FFA increases during fasting,
whereas in the diet fed state, lipolysis in adipose tissue is suppressed by insulin.
However, obesity is characterized by an inadequate insulin action in the fed state that resembles
conditions of a normal fasted state, resulting in the release of FFA into the circulation. In such
states of lipid oversupply, storage of available FFA no longer can be accomplished by adipose
tissue. Instead, FFA is also stored in other non-adipose tissues not intended for long term lipid
storage like skeletal muscle, liver, heart, or pancreas. As a consequence, increased amounts of
ectopic lipid stores are found in obese, insulin resistant and T2DM patients compared to non-
diabetic individuals (Figure 1).
Several studies performed in obese individuals have demonstrated a correlation between the
amount of ectopic lipid stores found inside skeletal muscle cells, referred to as intramyocellular
lipids (IMCL), and parameters of lipid oversupply like body mass index (BMI), waist-to-hip
ratio, central adiposity, and percent body fat. Whereas small amounts of intracellular
triglycerides represent an important energy source especially for skeletal and cardiac muscle in
periods of low glucose supply, increasing amounts of ectopic lipid deposits have been linked to
impaired organ function, a condition known as lipotoxicity.
Consequently, there is an association between the amount of IMCL and impairment of skeletal
muscle function marked by IR. This association has been further supported by additional studies
performed in non-obese, non-diabetic humans as well as in lean offspring of T2DM patients. In
these studies the amount of IMCL was identified as a main predictor of IR in muscle, as well as
whole body insulin resistance. Subsequent studies have shown that a reduction of IMCL content
results in improved insulin sensitivity.

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Figure 1. Consequences of increased adipose tissue mass on skeletal muscle metabolism
Energy overload due to high caloric intake and reduced physical activity leads to an increase of adipose
tissue mass, enhanced release of free fatty acids, and changes in adipocyte secretion profile. Adipocyte-
derived factors like FFA’s, TNFα, interleukin 6 (IL-6), monocyte chemoattractant protein-1 (MCP-1), and
endocannabinoids (EC), among others, disturb the metabolism of muscle cells. As a consequence,
intramyocellular lipids (IMCL) accumulate, insulin signaling is disturbed, and glucose uptake is impaired.
(Taube et al. Am J Physiol Endocrinol Metab 297: E1004–E1012, 2009).

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4. Triglyceride in Skeletal Muscle
Muscle is the greatest sink for insulin-responsive glucose disposal and represents approximately
80% of glucose flux. Defects in insulin action in muscle have been localized to glucose uptake
and phosphorylation, as well as its disposal via oxidation and storage as glycogen. All these
defects have been recapitulated with high-dose lipid infusions for several hours, providing
compelling evidence that lipid excess plays an etiologic role in muscle insulin resistance.
Under normal circumstances fatty acids are the preferred metabolic fuel for the myocyte, and in
the postprandial state and during moderate exercise can account for 60% to 80% of substrate
utilization. Fatty acids from circulating triglycerides are taken up by myocytes via directional
transport and are in equilibrium with an intramyocellular pool of triglycerides. These
intramyocellular lipid stores are thought to be highly metabolically active with a rapid turnover.
a. Increased Intracellular Triglycerides and Insulin Resistance
The classic formulation of the glucose-fatty acid (Randle) cycle posited that excessive fatty acid
oxidation inhibited glucose oxidation by increases in long chain acetyl-CoA (LC-CoA), which
fed back to decrease glucose uptake. Although glucose oxidation is rapidly decreased in the
presence of excessive fatty acids, glucose uptake and its conversion to glycogen is not affected
for several hours, calling into question whether this as the primary mechanism. Other
mechanisms of a more chronic duration likely account for impaired glucose uptake and storage.
Triglycerides within myocytes (IMTG – intramyocellular lipids), although part of a
metabolically active pool, are themselves rather inert and are not thought to be the primary cause
of insulin resistance. These triglycerides form oil droplets located in proximity to mitochondria
and provide fuel for oxidation. Nevertheless, they are in equilibrium with other, more active,
lipid species, particularly LC-CoA. Triglycerides are formed from LC-CoA, the activated form

11
of intracellular free fatty acids when esterified to glycerol 3-phosphate generated from
glycolysis. When triglycerides undergo lipolysis (or when fatty acids enter the cell de novo), they
are esterified with CoA and carnitine for transport into the mitochondria for oxidation.
LC-CoA is increased in insulin-resistant animals as well as humans and is reduced by either
weight loss or leptin treatment. Data are accumulating which suggest that LC-CoA, as well as
glycerolipid products derived from LC-CoA, particularly di-glyceride (DAG) and ceramide, have
direct and potent adverse effects on insulin signaling and insulin-responsive genes. LC-CoA can
directly inhibit hexokinase IV, the first enzyme of muscle intracellular glucose metabolism. LC-
CoA also has been shown to interfere with the insulin-signaling cascade in a manner that blocks
normal signal transduction.
Virkamaki et al. studied at a group of men matched for BMI, age, and physical fitness but
stratified according to high or low levels of IMCL [5]. Although LC-CoA was not measured,
subjects in the high IMCL group were more insulin resistant during euglycemic clamp and were
found to have decreased insulin tyrosine phosphorylation of the insulin receptor as well as
impaired IRS-1–associated phosphatidylinositol-3 kinase activity. It is clear that for lipids to
accumulate, supply of substrate into muscle cells must exceed disposal. As mentioned above, in
subjects who had IMCL concentrations measured, the best predictors of IMCL concentration
were 24-hour levels of free fatty acids, glucose, and insulin (Figure 2). Thus, nutrient excess, as
part of the western lifestyle or associated with poorly controlled diabetes, can result in excessive
lipid supply. Over time these lipids accumulate in metabolically active tissues such as muscle,
and probably also liver and pancreatic β-cells.

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Figure 2. Relationship between mean free fatty acid and intramyocellular triglyceride
[Stein DT, Szczepaniak LS, Garg A, et al. Diabetes 1997; 46(suppl 1):A929]

13
b. Physiological role of Intramyocellular Lipids in Skeletal Muscle
Early isotope studies [6-8] indicated that during exercise not all of the oxidized fat could be
accounted for by oxidation of plasma FFA, leading to the suggestion that the lipid droplets inside
the muscle cells serve as important fuel during exercise. Especially during prolonged exercise,
IMCL is thought to be an important fuel source, and the oxidation of IMCL might have a sparing
effect on glycogen oxidation. Data from biochemical analysis are equivocal on the effect of acute
exercise, with some research groups reporting a decrease in IMCL content, whereas others did
not. This discrepancy may be due to the high variability of the biochemical IMCL determination,
especially in untrained subjects, where more adipose tissue can contaminate the IMCL
determination. In contrast to biochemical analysis, Electron Microscopy (EM) data showed that
IMCL decreased by 42% in the gastrocnemius muscle after completion of a marathon race, and
IMCL were nearly depleted after a 100-km run in seven well-trained subjects [9]. These results
are in line with more recent 1
H-MRS results, showing a consistent decrease in IMCL content
after acute exercise. Only a high-intensity interval protocol reported no decrease of IMCL
content after exercise when 1
H-MRS was employed. The latter is not surprising because during
high-intensity interval protocols, carbohydrate is the main fuel oxidized. In summary, there is
little doubt that IMCL are, indeed, oxidized during endurance exercise and can serve as a readily
available energy source during exercise.
As discussed above, during exercise, both intramyocellular glycogen and lipid stores are used as
energy sources, dependent on the exercise intensity, and both substrate stores are replenished in
the recovery phase post-exercise. With endurance training, glycogen levels are elevated, which
promotes fatigue resistance. Analogously to glycogen, it might be expected that IMCL content is
increased in the endurance-trained state, too. Some biochemical analysis studies of IMCLs,

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reported increased IMCL content after 4 to 6 weeks of training, whereas others reported no
change after 12 weeks of training at high intensity or even a decrease in IMCL content [10].
These equivocal results are probably again due to the high variability of the biochemical and/or
training methods.
EM revealed 2.5 times higher IMCL content in the vastus lateralis of well-trained orienteers and
2.5 times higher IMCL content in the gastrocnemius muscle of elite rowers compared with
controls [7]. With 6 to 8 weeks of endurance training, IMCL content increased up to 3.4 times in
the vastus lateralis muscle.
Although almost all studies using EM and involving endurance training reported increased IMCL
content after training, the change did not always reach statistical significance. An increased
IMCL content in trained subjects limited to type 1 fibers has been reported in some studies [11,
12], whereas a fiber-type-specific increase in IMCL content in types IIA and IIB fibers was
reported by others, and a higher IMCL content in trained cross-country runners was limited to
type IIA fibers [13].
Similar to the results from EM, Oil Red O staining studies report large differences (50% to 70%)
between trained and untrained subjects and increased IMCL content (+12%) after a 12-week
training period in older subjects [6]. EM and histological data are in line with 1
H-MRS data,
which report higher IMCL content in trained subjects, compared with untrained. Recent studies
have shown that with 1
H-MRS a 2-week training program in young sedentary male subjects
resulted in a 42% increase in IMCL content, thereby confirming results obtained with EM and
Oil Red O [14]. Taken together, independent of the methodology used, there is unequivocal
evidence that IMCL content is increased on endurance training, again consistent with IMCL
being an energy source for physical activity. The increase in IMCL may partly be explained by

15
the observation that endurance training also increases the relative amount of type 1 - oxidative
muscle fibers. [15]
IMCL functions as a rapidly available energy source to deliver fuel for the mitochondrial ATP
creation necessary for muscle contraction. As with glycogen, the levels of these intramuscular
substrate stores are influenced by the diet, at least in the recovery phase after exercise. Many
investigators have also examined the effect of high-fat diets per se [16], on IMCL content. With
biochemical methods, triglyceride content in skeletal muscle has been reported to increase by
36% to 90% after high-fat feeding periods ranging from 24 hours to 7 weeks. Similarly, data
from EM showed a 130% increase in IMCL content after 5 weeks of a high-fat diet. Using 1
H-
MRS, the effect of high-fat diets (55% to 60% fat) has been investigated after 2 to 3 days, and
after 1 week. In all three studies, an increase (between 48% and 56%) in IMCL content was
reported. The reason for this increase in IMCLs from a high-fat diet is not yet clear. A high-fat
diet increases fat oxidation, which is not due to an increased oxidation of FFA. This suggests that
increased IMCL (and/or very low-density lipoprotein) oxidation occurs following a high-fat diet,
suggesting that the increase in IMCLs drives increased IMCL oxidation. In summary, a high-fat
diet increases IMCL stores, which may simply be due to a positive fat balance when changing to
a high-fat diet. In physically inactive humans who consume a high-energy, high-fat diet, a
positive energy and fat balance may occur chronically, resulting in fat accumulation in adipose
tissue and probably also in skeletal muscle. Other conditions with high FFA availability have
also led to increases in IMCL content. The acute elevation of plasma FFA by infusions has
resulted in increased IMCL content [17]. Also, during fasting, lipolysis is stimulated, and plasma
FFA concentrations are elevated. Interestingly, it also has been reported that 72 hours of fasting
increased IMCL content. The increase in IMCL under conditions of high FA availability can

16
simply be the due to a higher supply of fat to the muscle. Alternatively, non-active muscle could
act as a buffer for elevated plasma FFA by taking them up from the circulation. IMCL could
serve as an energy source when lipid supply is decreasing. However, in our westernized society
with a surplus of dietary energy available, periods of low lipid supply are scarce, resulting in
continued high levels of IMCL in non-active muscle tissues.
The development of insulin resistance coincides with the accumulation of IMCL. With
biochemical methods, a correlation of triglyceride content with insulin resistance in Pima Indians
was reported, and a similar correlation was shown in sedentary subjects with histochemical
methods and with 1
H-MRS. Interestingly, IMCL content has been described as an early marker
for the development of insulin resistance. Furthermore, histochemically determined high IMCL
contents were related to high waist-to-hip ratios and high FFA plasma concentrations. IMCL
content is a marker of insulin resistance in diabetic and healthy physically non-active subjects,
and the accumulation of IMCL has been shown to be an early phenomenon in the development
of diabetes [9, 12].
High IMCL content, thus, seems to be associated with the development of insulin resistance,
which seems to be inconsistent with the observations of endurance training leading to an increase
in IMCL content. This conundrum has been described as the training paradox in the literature.
However, as reviewed above, IMCLs can be increased for two different reasons: a functional
increase, whereby IMCL serves as a rapidly available energy source; and a pathophysiological
increase, whereby increased IMCL is due to a continuous oversupply of fat. In the former
condition, the increase in IMCLs can only be functional if at the same time the capacity to
liberate these IMCLs and rapidly divert them to oxidation is also increased. Indeed, gene
expression of a key component of FA transport (carnitine palmitoyltransferase) and the fat

17
oxidative capacity are also increased in the trained state. For sedentary subjects, however, the
increased IMCL content most likely is not accompanied by a strongly increased fat oxidation
capacity. In summary, although the accumulation of IMCL coincides with the development of
insulin resistance, the relationship is most likely indirect. Various intermediates of fat
metabolism have been named as candidates to be the culprits of decreasing insulin action. As the
intermediates accumulate in situations with high fat availability and low fat oxidation, the
balance between availability and oxidation may be crucial.
Most studies examining IMCL content have been limited to a few muscle groups. With 1
H-MRS
especially, the muscles of the calf have been extensively studied. In the calf, the highest fat
contents have been found in the medial part of the soleus muscle and lower values (by a factor of
2 to 3) in the tibialis anterior, tibialis posterior and gastrocnemius muscle. This corresponds well
to the different fiber- type distribution of these muscles and their substrate use [15]. The soleus
muscle is a more oxidative muscle relying more on fat oxidation than the tibialis anterior and
gastrocnemius muscles. Likewise, the soleus muscle has a high percentage (~88%), whereas the
gastrocnemius muscle has a lower percentage (~50%) of type 1 fibers. The fiber composition of
tibialis anterior muscle lies in between (~70% of type 1 fibers). EM and histochemistry (Oil Red
O) studies suggested that oxidative type 1 fibers were characterized by a higher fat content than
glycolytic type 2 fibers. In addition, oxidative type 1 fibers contain more mitochondria,
suggesting that the IMCL droplets are an important source of energy for mitochondrial oxidation
in these fibers [7, 13]. In summary, IMCL content is dependent on the fiber-type composition of
muscles, with oxidative muscle groups being characterized by higher IMCL contents.

18
B. Skeletal Muscle Lipids and Proton MR Spectroscopy (1
H-MRS)
Single-voxel proton magnetic resonance spectroscopy (1
H-MRS) has evolved as a powerful
method to noninvasively assess muscular lipid stores and to specifically measure IMCL [18-20].
In 1
H-MRS, the static magnetic field (B0) produces a net equilibrium magnetization in the
protons contained in fat and water molecules. The protons will produce a signal when, following
the application of magnetic field pulses (B1) oriented perpendicular to B0 and having the
appropriate radiofrequency, will create a net transverse magnetization composed of the proton
spins.
This process produces a signal at the same radiofrequency as the B1 field, which transiently
decays and can be detected by sensitive receiver coils. The frequency of the wave emitted
provides unique information about the identity of the protons, and the chemical compounds to
which they are attached (e.g., water or triglyceride methylene -CH2 chains). As the net nuclear
magnetization realigns (a process termed T1 relaxation) back to the equilibrium state along the
B0 magnetic field, energy that is again stored and may be released by application of another B1
field pulse.
The strength of the proton signal is directly proportional to the concentration of the chemical
compound within the specific volume studied. 1
H-MRS spectra of triglycerides has numerous
resonances, but complexity is reduced in vivo to the strongest resonance components, particularly
the methylene (-CH2) resonance at 1.3 ppm. The singular advantage of –CH2 resonance is that
these protons coresonate from most positions along the acyl-chain and thus combine to form an
amplified signal (Figure 3).
Typical triglyceride concentrations detected by the methylene resonances range from 1 ~ 20
mmol/kg wet weight.

19
Figure 3. 1
H-MRS high-resolution spectra of vegetable oil
Proton nuclear magnetic resonance high-resolution spectra of vegetable oil exhibiting a mixture of
saturated, mono, and polyunsaturated fatty acids in triglycerides. Methylene protons labeled in the B
position combine to resonate at 1.3 and 1.5 ppm. Note how unsaturated fatty acids will have a lower
density of methylene protons. In vivo spectra cannot easily resolve other resonances (A, C–F).

20
Other resonances origination from triglycerides are much weaker (fewer coresonating protons)
and are poorly resolved due to magnetic susceptibility shift from these minor resonances causing
spectral overlap.
Schick [20] and Boesch et al [14, 17] proposed using 1
H-MRS for measurement of skeletal
muscle lipids, particularly IMCL and EMCL, suggesting that the resonance frequency of
triglycerides contained within spherical IMCL droplets is shifted approximately 0.2 ppm from
triglycerides within asymmetrical adipocytes (EMCL), thus allowing the two pools to be
discriminated (Figure 4).
Several lines of evidence are available to support that this method specifically measures the
IMCL pool. In 1
H-MRS measurements of skeletal muscle, IMCL signal scales linearly as volume
size is increased with other intracellular metabolites such as creatine and water, whereas EMCL
increases disproportionately. EMCL signals are strongly dependent on the location of the voxel
due to their discrete distribution within the muscle. While the absolute levels of IMCL may
depend on the type of muscle (fiber type, mechanical properties), IMCL is evenly distributed in
skeletal muscle.
Therefore, shifting of the voxel within a muscle would not change IMCL signals. EMCL, on the
other hand, is concentrated in distinct structures such as subcutaneous fat and fibrotic structures
along muscle fibers with adipocytes. In other words, while IMCL is largely independent of the
choice of the voxel position in a specific muscle, the amount of EMCL can vary considerably
even for tiny shifts of the voxel position by a millimeter or less.
For quantification purposes, the IMCL-CH2 (1.3 ppm) methylene resonance is typically
measured against intramuscular water or creatine. Both exhibit relatively little intra- and even
inter-individual variability.

21
Figure 4. 1
H-MRS high resolution spectra of tibialis anterior (TA) muscle
Axial magnetic resonance image of human leg at the calf, VOI (TA). 1
H-MR spectrum of a PRESS
acquisition using a single voxel at TR = 3000 msec, TE = 20 msec. Two different, orientation-dependent
effects make the spectrum of skeletal muscle unique in comparison to other tissues. In the region of 1–2
ppm, bulk magnetic susceptibility effects lead to a shift of the extramyocellular lipid (EMCL) resonances
relative to the chemically very similar intramyocellular lipids (IMCL). These shifts are not a result of the
chemical nature but have their origin in the spatial arrangement of the lipids. A physically totally different
effect in muscular spectra can be seen in resonances in the region 2.5–4 ppm. Residual dipolar coupling
leads to a splitting of resonances, e.g. of creatine -CH2 at 3.96 ppm (Cr2). TMA – trimethylammonium
(3.2 ppm) containing compounds; Cr3 creatine–CH3 (3.02 ppm) can also be observed.

22
Absolute quantification may be determined after correction for the T1 and T2 relaxation (decay)
times of both the IMCL-CH2 and water resonance signals. Finally, the corrected lipid-to-water
ratio produces a measurement of intramyocellular triglyceride by 1
H-MRS, given knowledge of
triglyceride fatty acid composition (Figure 3 and Appendix C).
The use of 1
H-MRS for measurement of IMCL was first reported at 1.5-T magnet field strengths
and most studies [18, 21-23] have continued to be performed at this field strength due to the
widespread availability of these clinical systems. Studies of the biophysics of the magnetic
susceptibility shift effect have clarified that this measurement is prone to artifact, particularly at
lower field strengths, unless extreme caution is taken for proper experimental setup.
To date, 1
H-MRS muscle lipid data have been obtained from calf muscles (tibialis anterior,
tibialis posterior, soleus) and thigh (vastus lateralis). The resolution between the IMCL and
EMCL methylene peaks are generally much better from voxels within tibialis anterior compared
with soleus, tibialis posterior and vastus lateralis.
This circumstance is attributed to tibialis anterior muscle fibers being, on average, more
uniformly oriented longitudinally along the long axis of the leg and tending to line up parallel
with the magnetic field. In contrast, soleus and vastus lateralis muscle fibers are much more
heterogeneous in their fiber orientation, which is important because the difference in magnetic
susceptibility shift is only maintained as long as muscle fibers maintain a roughly parallel
orientation to the magnetic field.
As fiber angles become more oblique, the magnetic susceptibility difference decreases, and at
approximately 55° to the magnetic field (referred to as the magic angle), the two resonances
completely overlap [24-27]. The implication of this is that as voxel size increases, or when large
fat deposits are contained within the voxel, the probability of spectral overlap between EMCL

23
and IMCL becomes high. This cross contamination of the IMCL resonance cannot be rescued by
any form of data post-processing because the magnetic susceptibilities are in fact identical under
such circumstances.
Successful data acquisition requires meticulous attention to fiber orientation in locations with
minimum amounts of adipocyte presence (EMCL). Not surprisingly, the best resolved spectra are
typically acquired from lean, athletic individuals who possess uniformly oriented muscle fibers
and lesser amounts of adipocyte stores [28]. Additional approaches for enhancing data quality
may be realized by moving to higher field strengths (3T and 7T), which improves overall
spectral resolution by increasing the chemical shift, susceptibility effect and signal-to-noise ratio
(SNR) [29-32].
At 3 Tesla adequate SNR is achievable from voxels much smaller than at lower field (e.g., 250
μL vs. 2–10 mL). This capability, coupled with a spectroscopic imaging approach—acquiring
data from a matrix covering the entire cross-section of the leg—allows for acquisition of multiple
small voxels simultaneously. Only the best resolved spectra are chosen for unambiguous
quantification of IMCL, which necessarily biases against voxels containing larger amounts of
EMCL. Using such an approach, intra-subject coefficients of variation of multiple voxel
measurements of IMCL in normal volunteers of 7% to 12% were observed.
Using this general approach, Hwang and colleagues [33] redefined downward normal values of
IMCL within calf muscles of healthy, mostly sedentary volunteers. In a recent study, IMCL
concentrations were found to be 1.6 ± 0.9 mmol/kg within the tibialis anterior, 2.8 ± 1.3,
mmol/kg in the tibialis posterior and 4.8 ± 1.6 mmol/kg in the soleus. In contrast, EMCL was
found to be about 25 to 30 mmol/kg in all three muscles. The differences in IMCL content are
consistent with the known fiber makeup of these muscles. The soleus is rich in oxidative (type I)

24
fibers, the tibialis anterior in glycolytic (type IIA and IIB) fibers, with the tibialis posterior being
intermediate in fiber content.
These IMCL concentrations and IMCL/EMCL ratio were about 50% of those reported
previously by several groups working with similar subjects but with larger voxel sizes and lower
field strengths, whereas the EMCL concentrations were quite similar.
Despite the coefficient of variation being similar (6% to 15%), these results support the notion
that some overestimation of IMCL has occurred in previous reports, albeit less than that
observed due to biopsy. Even minor amounts of contamination may alter results; adipocytes may
be present between muscle fiber bundles and may not be visible despite microscopic dissection.
C. Diffusion Tensor Imaging in Skeletal Muscle
Skeletal muscle is an important structured tissue and the architectural structure and physiological
function of the peripheral skeletal muscle has been studied [34-38]. Diffusion tensor imaging
(DTI), a promising non-invasive method can be used to provide a wealth of information on the
morphology, microstructure and function of skeletal muscle [39-41]. DTI sensitizes the MRI
signal to water diffusion through motion-sensitizing gradients along different directions. Cells in
muscles have elongated structures, which present regularly oriented barriers to water diffusion.
The effect of cell membranes on diffusion has a directional dependence, which gives rise to an
anisotropy in diffusion.
The anisotropic diffusion properties are indicated by a tensor quantity instead of a scalar
quantity. DTI can help to characterize physiological properties, tissue microstructure and
architectural organization of skeletal muscle. It has been demonstrated that DTI can differentiate
between functionally different muscles in the same region of the body on the basis of their
diffusion properties [42]. Compared with conventional MRI, there are several important values

25
to quantitatively detect in the DTI of skeletal muscle based on the DTI images, such as three
eigenvalues (λ1, λ2, λ3), fractional anisotropy (FA), and apparent diffusion coefficient (ADC) (see
Appendix B). The three eigenvalues describe the magnitude of the diffusion coefficient in three
orthogonal directions. Generally, the λ1 value represents the diffusive transport along the long
axis of the muscle fibers, and the λ2 and the λ3 values correspond to orthogonal water diffusion to
the three-dimensional direction of λ1 in the muscle fibers [43, 44].
DTI-based fiber tractography is extensively used to reconstruct skeletal muscle fibers based on
the anisotropic diffusion of water within muscle tissue [45-47]. Water diffusion can be detected
using a tensor model (see Appendix B) by measuring water diffusion in six or more non-
collinear directions. Because water diffuses most readily along the longitudinal axis of the
muscle fibers, DT-MRI muscle fiber tractography is based on the preferential diffusion of water.
These data are used to reconstruct and render the path and orientation of muscle fibers through
computer modeling. Fiber tractography offers a global approach to evaluating muscle anatomy
and provides an appropriate and more reliable approach to measuring muscle pennation angles
(i.e. the obliquity between the muscles fibers and the main axis of the muscle) than ultrasound
[48, 49].
Fiber tractography can potentially be applied to provide 3D architecture of skeletal muscle fibers
and investigate human muscle structure-function relationships. Budzik et al.,[45] reconstructed
3D muscular architecture using a fiber tracking technique, and directly measured the diffusion
values over the whole thigh region, investigating the architectural differences between non-
pennate and pennate muscles.
Furthemore, the pennation angle can also be estimated from tractography data, as reported by
Kan et al., [46] in a DTI-based tractography assessment study of the quadriceps mechanism in

26
lateral patellar dislocation. A significantly lower pennation angle was found in the vastus
lateralis oblique muscle and a significantly higher pennation angle in the vastus medialis muscle
in patients when compared with volunteers, which was consistent with the lateral displacement
of the patella. Heemskerk et al., [46] used DTI fiber tracking of mouse muscle to measure the
physiologic cross-sectional area (PCSA), pennation angle and fiber length directly. DTI and fiber
tractography can be used to study the physiological properties and tissue microstructure of
skeletal muscle. In the context of the present study, DTI was used to estimate the pennation
angles which were utilized further to evaluate the pennation angles’ impact on lipid chemical
shift in various skeletal muscles.
D. Two-Point Dixon Water-Fat MRI
Chemical shift based water-fat separation methods such as Dixon MRI provide high-resolution
three-dimensional imaging of muscle fat composition by using the phase difference between
water and fat components [50]. Dixon suggested in 1984 that in-phase and out-of phase gradient-
echo images could be combined to create images of just fat or water [51]. As such, they can give
a quantitative measure of the signal fraction of both water and fat. Traditional two-point Dixon
imaging has been directly correlated to fat levels from muscle biopsy [52]. Calibrated phantoms
[53, 54] have provided support for the accuracy of the fat fraction (FF) measurements.
Additionally, validation studies have been performed in muscle to compare directly
measurements made by spectroscopic imaging to standard 1
H-MRS measurements [55, 56] and
found good agreement and strong correlation.
Measuring fat composition in muscle tissue presents several potential challenges to quantitative
imaging. Different models [57-60] have been proposed to take into account the spectral
complexity of fat and other confounding factors when using fat and water separation algorithms

27
such as the difference in T1 and T2* values, effect of flip angles and chemical composition for
fat and muscle. In addition, low SNR images may affect the accuracy of the results, especially
when one of the species is predominant (e.g, low FF, low water fraction). Details on image
formation using two-point Dixon MRI utilized in this study are presented in Appendix D.
E. Specific Aims
Type 2 diabetes mellitus (T2DM) and insulin resistance are characterized by excessive lipid
accumulation in skeletal muscle tissue.
Non-invasive proton single-voxel MR spectroscopy (1
H-MRS) has been widely used to study the
time course of in vivo skeletal muscle lipid metabolism; however inconsistencies in quantitating
extramyocellular lipids (EMCL) limit the accuracy of these IMCL concentration measurements.
Discerning the relationship between IR and IMCL is hampered by the lack of consistent in vivo
intracellular metabolic data. Most 1
H-MRS studies of IMCL in T2DM have been conducted in
the muscles of the lower leg (soleus and tibialis anterior) even though the vastus lateralis muscle,
which is known to have a different lipid metabolism profile, is the preferred site for obtaining
muscle biopsies.
The goal of this proposal is to develop and apply diffusion tensor MR imaging (DTI) to improve
1
H-MRS quantitation of VL m. lipids in T2DM subjects and total lipids measured from in vivo
Dixon two-point fat-water MRI. To achieve this goal, the following specific aims are proposed:
Aim 1. Optimize the b-value for diffusion tensor imaging and fiber tractography of in vivo vastus
lateralis muscle Aim 2. Evaluate the influence of fiber orientation, muscle group, echo time and
voxel size on the IMCL estimation in normal glucose tolerant (NGT) subjects using 1
H-MRS and
DTI Aim 3. Measure the difference in IMCL concentrations in soleus, vastus lateralis and tibialis
anterior muscles in NGT and T2DM subjects.

28
II. Optimization of diffusion tensor imaging and deterministic fiber tractography of human
vastus lateralis muscle in vivo

29
A. Introduction
Diffusion tensor imaging (DTI) is widely used to study the structural characteristics and
architectural organization in the skeletal muscle, which plays a critical physiological role in
energy metabolism and thermoregulation.[41-43, 61] Evaluation of muscle structure-functional
relationship can be used to provide new biomarkers for assessment of neuromuscular diseases
and also to study healthy muscle function and physiology.[42, 47]
DTI and fiber tractography measures in the skeletal muscle, such as fractional anisotropy (FA),
and the mean numbers and lengths of reconstructive fiber tracts are affected by MRI hardware
and software configurations. Factors affecting muscle image quality often include measurement
noise, blurring effects of T2 decay, sensitivity to artifacts and local susceptibility effects. These
factors are dependent, in large measure, on selected acquisition sequences and scanning
parameters.[62] Therefore, optimizing acquisition parameters is an essential step for improving
image quality and accuracy of muscle tractography.
It is important in DTI to preserve the extent of diffusion weighting while achieving a sufficient
signal-to-noise (SNR) for post-processing. The b-value is the primary user-defined parameter
that determines the sensitivity of the imaging sequence to molecular diffusion of water. High b-
values increase diffusion weighting within muscle tissue, but at the expense of lower SNR.
Typically lower b-values are used in muscle DTI, compared to those of around 1000 s mm-2
,
which are used in brain studies.
A range of b-values (400 - 900 s mm-2
) and varying gradient directions have been used in several
recent skeletal muscle DTI studies. [2] DTI has been used to estimate eigenvalues and vectors,
ADC, and FA. Fiber tractography analysis is used to evaluate fiber tracts and measure pennation
angles.[39, 45, 46, 63-65] Saupe et al.,[66] has qualitatively and quantitatively studied the lower

30
leg muscles at 1.5T and estimated an optimum b-value of 625 s mm-2
for DTI and fiber tract
assessment. Qualitative analysis was assessed by evaluating the appearance of continuous fibers,
fiber track order and organization. Quantitative measures included the number of fibers, the
length and the apparent density of muscle fiber bundles.
Recent studies [40, 67] evaluated the effects of SNR on diffusion tensor indices and fiber tracts
using numerical simulations and in vivo validation of human calf muscles. These studies
evaluated the effect of low SNR, and the dependence of the diffusion indices on the b-value,
suggesting an optimal b-value for muscle DTI should be a value that provides lower error
estimation in tensor fitting, potentially improving fitting accuracy, and also balancing the effects
of higher DTI noise observed at higher b-values. Diffusion tensor studies in muscle, observed at
higher b-values with insufficient SNR, is undesirable since weaker diffusion weighted signals
and their derived measures are prone to increased systematic bias caused by higher background
noise levels.
The vastus lateralis muscle (VLM) is the muscle most commonly studied using muscle biopsies
in clinical research studies related to aging, obesity, sports medicine and exercise physiology.
Intrinsic diffusion properties of the VLM obtained through non-invasive DTI and fiber
tractography can provide useful information on its underlying architecture and can be utilized to
further understand the physiology of its intra and extra-cellular compartments. At present, the
optimal b-value for DTI and fiber tractography in VLM is not clearly established, especially at 3
Tesla. Evaluation of VLM diffusion tensors, derived indices at different b-value acquisitions and
their dependence on SNR are necessary in order to assess the accuracy and precision of these
measurements. The purpose of this study was to assess systematically the factors contributing to
the optimal b-value for diffusion tensor imaging and fiber tractography of human VLM at 3.0 T.

31
B. Methods
Thirteen healthy subjects (four women, nine men; mean age: 29.85 ± 6.96 years; range, 18-50
years; BMI: 24.58 ± 3.89) were included in this prospective study. Informed consent was
obtained from all study subjects following institutional review board (IRB) approval. The
exclusion criteria included the typical contraindications for MRI (e.g. pacemakers and other
potentially dangerous implanted devices), musculoskeletal disorders, muscle anomalies, a history
of prior surgeries and muscle trauma that necessitated medical attention within the previous 6
months.
1. MRI Protocol
MRI was performed using a 3.0 T MRI Siemens Tim Trio scanner (Siemens Healthcare,
Malvern, PA) and 4-channel large flexible wrap-around array coil. Subjects were oriented in a
supine position for imaging with the subjects’ legs in a relaxed state to avoid mechanical
compression of the anterior thigh muscles. The coil was centered on the right mid-thigh region
over the VLM with the inferior aspect of the coil positioned at the level of the patella. Gradient-
echo T1W localizer scans were acquired to facilitate proper DTI slice positioning in the mid-
thigh region.
DTI was acquired using a single-shot spin-echo echo-planar imaging (SS-EPI) in 30 non-
collinear directions of diffusion sensitization (TR/TE = 4500/65–90 ms, depending on the b-
value, FOV = 250 × 250 mm, matrix size = 128 × 128, flip angle = 90°, slice thickness = 5 mm,
13 slices, NSA = 1, EPI factor = 128, BW = 1502 Hz/pixel, echo-spacing = 0.73 ms, acquisition
time = 2 min 24 s per b-value acquisition). Five acquisitions with different b-values of 400, 500,
600, 700 and 800 s mm-2
were acquired for each subject. These b-values were chosen based on
previous studies using simulations [40, 67] and in vivo muscle DTI optimization [66]. Three

32
corresponding b = 0 s mm-2
T2-weighted (T2W) images were acquired. Multiple T2W images
were obtained to boost SNR to acceptable levels and considerably improve the estimation of
diffusion tensor and provide relatively unbiased diffusion measurements[62]. Parallel imaging
(GRAPPA) was used with an acceleration factor of R = 2 to allow shortening of the effective TE
with concomitant reduction of magnetic susceptibility artifact. SPAIR fat suppression was used
to reduce chemical shift artifacts. An axial T1-weighted turbo spin echo (TSE) sequence was
also acquired (TR/TE = 700/25 ms, FOV = 250 mm, voxel size = 0.7 × 0.5 × 5 mm, 13 slices,
TSE factor = 4; NSA = 1, GRAPPA = 2) and used as an anatomic reference for defining tissue
planes and for region-of-interest (ROI) analysis within the VLM.
2. Data Analysis
DTI datasets were processed using the FDT (FMRIB’s Diffusion Toolbox) from the FMRIB [68]
Software Library (FSL). The quality of the diffusion weighted (DW) images were visually
inspected for motion and distortion artifacts, followed by 12 parameter (translation, rotation,
scale, and shear) affine transformation, corrected for both motion and eddy current effects using
the baseline b = 0 s mm-2
images. Noise correction was applied to the signal intensity S of each
voxel Scor = (S2
– R2
)1/2
where R is the mean signal of an ROI containing only background noise.
To avoid artificially bright pixels in regions outside the image, a binary mask, defined by a
polygon tracing the outer contours of the thigh, was created from the anatomical reference with
only pixels containing tissue of the mid-thigh included. For the pixels outside the polygon, the
intensity values were set to zero.
A positive definite symmetric 3 × 3 diffusion tensor was estimated on a voxel-by-voxel basis
from the baseline and DW images using the single symmetric Gaussian displacement distribution
tensor model. The effective diffusion tensor was derived from [Sb = S(b = 0) × exp(-bijDij)] by

33
least-squares fitting to a multivariate linear regression model, where Sb is the signal intensity in
the presence of the diffusion gradients and S(b = 0) is the baseline image. The diffusion tensor was
diagonalized to yield the eigenvalues (λ1, λ2, λ3), and derived ADC and FA for each pixel.
Anatomical reference images were registered to the DW images and derived diffusion indices
using 3D rigid-body transformation. Diffusion index maps were visualized using the Mango
image analysis software (http://ric.uthscsa.edu/mango/) and standardized regions-of-interest
(ROI = 100 mm2
) were placed on the VLM in the center slice of the image to estimate
eigenvalues, ADC and FA. Signal-to-noise ratio (SNR) was determined for each b-value in the
DW images, in which signal was measured as the mean value of the standardized ROI on the
VLM. Noise was defined as the average standard deviation (SD) of signal intensity in four ROI’s
placed at artifact-free locations in the background. Figure 5 shows the de-noised center slice of b
= 0 s mm-2
images and different b-value DTI acquisitions from the thigh. Figure 6 depicts the
anatomical T1 and b = 600 s mm-2
processed diffusion indices maps obtained at b = 600 s mm-2
.
3. Fiber Tractography
Fiber Tracking was performed using Diffusion Toolkit and TrackVis 0.5.2 based on fiber
assignment by a continuous tracking (FACT) method. Minimum FA threshold was set to 0.2 and
maximum angular threshold to 55o. 3D Fiber tracks were drawn automatically from a seed ROI
(500 mm3
) on T1-weighted (T1W) reference images over the center slice and transferred onto the
generated fiber track images (Figure 7). For fiber tracking effect evaluation, mean numbers of
fibers, mean lengths of reconstructed fibers and fiber density index were evaluated as in previous
studies.[66, 69, 70]. The length of each fiber tract was determined within the 3D seed ROI along
the 13 slices and only a fraction of fibers reached the muscle border within the 65mm length of
image slices.

34
Figure 5. Cross-sectional, diffusion-weighted images of the thigh mid-section
These cross-sectional, diffusion-weighted images were obtained from the thigh mid-section of a 28 year-
old male healthy subject. A. T2-weighted image (b = 0 s mm-2
). Center slice of the thigh at different b-
values B. 400 s mm-2
C. 500 s mm-2
D. 600 s mm-2
E. 700 s mm-2
F. 800 s mm-2

35
Figure 6. Cross-sectional T1W and diffusion indices images
A. These T1W cross-sectional images are from the mid-section of the thigh. The traced region delineates
the boundaries of the VLM. The white box depicts the ROI (100 mm2
) used for data analysis. The post-
processed DTI data set was obtained with b = 600 s mm-2
, displaying B. the ADC map, C. the FA, D. the
primary eigenvalue map λ1, E. the secondary eigenvalue map λ2, and F. the tertiary eigenvalue map λ3.

36
Figure 7. Muscle fiber tracts in the vastus lateralis muscle (VLM)
A. Estimated fiber tracts are shown in relation to a high resolution T1-weighted axial image obtained from
a 26 year-old female volunteer on the center slice of the thigh with the traced region (red) drawn to define
the VLM. B. The estimated fiber tracts, obtained using b- = 600 s mm-2
, are co-registered onto the
anatomical T1W image, depicting well-organized fiber bundles across the VL muscle C. The ROI that was
used for estimating fiber density index (FDi) and number of tracts is depicted in red (500 mm3
), drawn
over the VL. The angular variation of muscle fibers within the ROI can also be appreciated. The color
orientation of the muscle fibers is, XYZ: RGB.

37
4. Statistical Analysis
All quantitative measurements were reported as mean ± SD. Student’s t-test was used to evaluate
significant differences in diffusion tensor measures and fiber tractographic estimates across
different b-value acquisitions. Diffusion simulation model input values (λ1 = 2.0 mm2
s-1
, λ2 =
1.6 mm2
s-1
, λ3 = 1.4 mm2
s-1
, ADC = 1.6 mm2
s-1
and FA = 0.2) were defined[40] and diffusion
tensor measures were simulated as a function of b-value under the assumptions of pure Gaussian
diffusion and mono-exponential T2 decay. Scatter plots were used to compare the diffusion
measures in VLM with the muscle simulations and in vivo data observed in previous studies[40].
Qualitative analysis on fiber tract images was performed by two well-experienced
musculoskeletal radiologists and a senior MR physicist based on blinded scoring on five
independent criteria (tract length, uniformity within ROI, broken tracts, fiber density and
divergence from the ROI boundary). Each criteria was scored on a scale 0 to 2 (0 – worst image
quality, 2 – best image quality) and provided a summed rank between 1-to-10 for individual tract
images as described in previous study[66]. Kruskal-Wallis one-way ANOVA rank sum test was
used to evaluate significant differences in image quality rank data across different b-value
acquisitions. Fleiss’ Kappa was also used to measure the reliability of agreement between
independent raters for each b-value. Statistical analysis was performed using R 2.15.0 with p <
0.05 considered to be statistically significant.
C. Results
As expected, maximum SNR (49.53 ± 11.5) was observed at the lowest b-value (400 s mm-2
) and
decreased with increasing b-values. SNR decreased with b-value 15.01% for 500 s mm-2
, 27.01%
for 600 s mm-2
, 38.83% for 700 s mm-2
, and 52.20% for 800 s mm-2
. Highest eigenvalues (λ1 =
1.94 ± 0.25, λ2 = 1.50 ± 0.21, λ3 = 1.13 ± 0.21 mm2
s-1
) were observed at b-value = 400 s mm-2
,

38
gradually decreasing with increasing b-values and the lowest values (λ1 = 1.65 ± 0.25, λ2 = 1.30
± 0.17, λ3 = 1.08 ± 0.17 mm2
s-1
) were found at 800 s mm-2
. Student’s t-test applied to data
obtained at b-values of 400 s mm-2
and 800 s mm-2
was significant for λ1 (p < 0.04), λ2 (p <
0.02), and showed no significant difference for λ3.
Figure 8 shows a comparison of scatter plots of the estimated eigenvalues, ADC and FA in VLM
with the model input values, simulation, and in vivo muscle data obtained from previous studies
as a function of b-values. Estimated ADC values generally decreased with increasing b-values
(Figure 8D). Mean observed FA was between 0.27 and 0.21, and showed no differences when b-
values were varied (Figure 8E).
Fiber tract images were assessed using fixed constraints of minimum FA = 0.2, maximum angle
threshold = 55° and seed ROI = 500 mm3
. Mean fiber length (MFL), mean number of
reconstructed fibers (MNF) and FDi showed an increasing trend with increasing b-values from
400 s mm-2
up to 600 s mm-2
and gradually decreased with further increasing b-values (700 and
800 s mm-2
). No significant differences were observed between MNF passing through the seed
ROI for the b = 600 s mm-2
and 500 s mm-2
, and 700 s mm-2
, however significant differences
were found with b = 400 s mm-2
(p < 0.001) and 800 s mm-2
(p < 0.05).
Qualitative analysis using one-way ANOVA on summed rank data from 3 independent raters
produced significant difference (chi-squared H = 10.664, p = 0.03) between different b-values.
Subjectively superior quality in the visualization of fiber tracks with well-organized long
compact fiber bundles (n=13, mean value measured from the 3 independent raters summed rank
data = 20.69 ± 6.64) was observed at 600 s mm-2
, followed by 500 (19.23 ± 6.64) and 700 s mm-2
(14.85 ± 6.83) and shortened and unorganized fiber bundles occurred more at 400 (16.08 ± 6.54)
and at 800 s mm-2
(13.17 ± 5.97).

39
Figure 8. Scatter plots of diffusion indices in VLM
These scatter plots for A. λ1, B. λ2, C. λ3, D. ADC, and E. FA show the trends of the measured diffusion
parameters in VLM as a function of b-value. VLM diffusion tensor measures acquired in the present
study are compared with the diffusion model input values, numerical simulations and in vivo muscle
(*Froeling et al.[40] ).

40
Inter-rater reliability assessed using kappa analysis produced fair-to-moderate agreement
between raters for 400 s mm-2
(κ = 0.30, SE = 0.11, 95% CI = 0.07 to 0.53), 500 s mm-2
(κ =
0.52, SE = 0.11, 95% CI = 0.29 to 0.74), 600 s mm-2
(κ = 0.38, SE = 0.12, 95% CI = 0.13 to
0.63), 700 s mm-2
(κ = 0.25, SE = 0.11, 95% CI = 0.01 to 0.46), and 800 s mm-2
(κ = 0.30, SE =
0.13, 95% CI = 0.04 to 0.55).
Figure 9 shows the muscle fiber tracks obtained at different b-values co-registered with the
anatomical VLM images. MFL and FDi were observed to be constant within b-value range 500-
700 s mm-2
and were significantly different from 400 and 800 s mm-2
(p < 0.05). Table 1 lists the
variances in the mean SNR, FA, MFL and MNF. Figure 10 depicts the relationship between SNR
and FDi as a function of b-value. These results suggest that with an increase of b-value from 400
to 800 s mm-2
, SNR decreases should be expected but FDi, MFL and MNF will be lower at 400 s
mm-2
and reach maximum values between 500-700s/mm2
. Furthermore, a decreasing trend was
noted as the b-value was increased beyond 800 s mm-2
.
D. Discussion
In this study, a series of DTI datasets was acquired using an SS-EPI sequence, obtained by
varying b-values in a cohort of thirteen healthy volunteers to assess the optimal b-value for
tensor analysis and fiber tractography in VLM at 3T. SNR decreased with increasing b-value in
the VLM, as expected from previous results reported in human calf muscles [64, 66]. SNR was
reduced by 25% in VLM at 600 s mm-2
and by 50% at 800 s mm-2
compared to SNR obtained at
400 s mm-2
. Increasing b-values also resulted in a decreasing eigenvalues, ADC and FA. These
results suggest that as the b-value increases above 400 s mm-2
, a steady reduction in SNR (with
baseline offsets due to Rician noise distribution [71]) causes diffusion indices to be
underestimated in a manner similar to the observations reported in the current study.

41
Figure 9. Tractography of fiber tracks as a function of b-value in VLM
Tractography of muscle fiber tracks, obtained within the VL, is co-registered with a T1-weighted axial
image obtained from the same 38 year-old male volunteer. A. The red ROI drawn over VL muscle on the
center slice of the mid-section of the thigh identifies the region of analysis. Tractograms, obtained were
acquired with b-values of, B. 400 s mm-2
, C. 500 s mm-2
, D. 600 s mm-2
, E. 700 s mm-2
, and F. 800 s mm-
2
. Fiber track quality with well-organized long compact fiber bundles was observed between 500-700 s
mm-2
. Shortened and unorganized fiber bundles occurred at b-value = 800 s mm-2
. The color orientation
of muscle fibers is, XYZ: RGB.

42
Table 1. Descriptive statistics for fiber tractography measurements at different b-values
b value (s mm-2
) SNR FA MFL (mm) MNF
400
500
600
700
800
p-value
49.53 ± 11.51
42.09 ± 8.85
36.15 ± 8.23
30.29 ± 8.08
23.67 ± 4.83
< 0.001
0.27 ± 0.10
0.26 ± 0.09
0.26 ± 0.09
0.23 ± 0.10
0.21 ± 0.10
NS
56.6 ± 14.3
58.9 ± 12.7
59.3 ± 12.9
58.6 ± 11.1
57.7 ± 11.8
NS
328.5 ± 49.4
363.3 ± 86.7
369.3 ± 97.0
358.6 ± 85.5
280.8 ± 63.7
< 0.001
Vastus lateralis muscle (VLM); Values are mean ± SD; SNR: signal-noise ratio; FA: fractional anisotropy; MFL:
mean fiber length; MNF: mean number of fibers.

43
Figure 10. Relationship between SNR and fiber density index
The relationship between SNR (solid line) and fiber density index (dotted line) for VLM is presented as a
function of increasing b-value. Although SNR steadily decreases, improved fiber density index resulted in
a higher number of fiber tracks within the b-value range of 500-700 s mm-2
.

44
These observations can be attributed to the increased bias due to actual non-Gaussian multi-
compartmental diffusion in VLM and increasing noise in DW images with increasing b-values
causing significant deviation from the model input values that are based on the assumption of
Gaussian diffusion and mono-exponential T2 decay.
These results also are consistent with simulation studies on diffusion in skeletal muscle [40, 67].
In simulation studies using linear least-squares method for diffusion tensor estimation [15], at
increasing b-values, λ2 and ADC remained constant, whereas λ1 and FA increased and λ3 faintly
decreased relative to model input values. Diffusion parameters measured in in vivo VLM in this
study revealed a discrepancy with these simulation data, and exhibited a steady decrease in
eigenvalues and ADC with increasing b-values. These attributes were moderately lower, but
closely resembled the decreasing trend previously observed in in vivo calf muscle derived
diffusion measures [15] as seen in Figure 8. Changes in eigenvalues, ADC and FA in this study
compare favorably with literature values [65, 72, 73] in healthy thigh muscles acquired at 3T. No
significant change between measured eigenvalues, ADC and FA was observed between b = 500
and 600 s mm-2
compared to other b-values.
At present, there is no accepted standard for analyzing muscle DTI based fiber tractography. The
FACT algorithm, used in this study, is commonly used for clinical research. For data obtained
with the lowest b-value (400 s mm-2
), the fiber tracking algorithm was not stable and
reconstructed fiber tracts were relatively lower in number and shorter in length. These
observations likely reflect the limited sensitivity to molecular diffusion within the muscle at
lower b-values, which can result in insufficient fiber tracking. Significant reduction of MFL,
MNF and FDi also resulted in disoriented fiber tracking at b-values beyond b = 700 s mm-2
due
to a greater proportion of voxels having increased noise bias.

45
Acquisitions obtained at b-values between 500 and 700 s mm-2
resulted in stable fiber tracking
results with long and well organized fibers in VLM and were similar to previous reported values
from other muscles [66, 73]. MFL, MNF and FDi, measured within the seed ROI’s, were
maximum at a b-value of 600 s mm-2
.
This study has some limitations. Firstly, due to hardware constraints, higher b-values could only
be achieved at the expense of increased TE and an additional decrease in SNR. Secondly, this
study included a small number of normal healthy subjects and variation of diffusion indices
according to age, sex and the effect of different levels of fat infiltration and fitness levels within
the muscle were not addressed. Finally, the FA and angular tolerance and the constraints chosen
in this study for VLM fiber tractography, on which the MFL and MNF fiber tracking algorithm
directly depended, were based on previous studies [47, 64, 65, 69, 72]. However several DTI
studies [48, 63, 74, 75] have reported variable seed ROI-based fiber tractography measures on
normal healthy human/animal subjects using different fiber tracking software, and fiber
reconstruction algorithms. Robust seed ROI/voxel-wise post-processing methodologies and
standardized criteria for qualitative and quantitative DTI fiber tracking have not yet been
established.
It is not yet clearly understood which criteria are optimal for DTI and deterministic tractography.
ROI-based VLM diffusion tensor and fiber estimation can also be influenced by physiological
and geometrical dependence of muscle fibers, and barriers to molecular movement. Future in
vivo studies are needed to address the effects of diffusion-encoding gradient directions and
spatial resolution to further reduce any systematic bias on DTI and tractography measurements.
Additionally, quantitative diffusion phantom studies would be useful to further validate and
address the reliability and repeatability of diffusion parameters.

46
E. Conclusion
This study reported diffusion indices and fiber tractography assessment in the vastus lateralis
muscle within the b-value range 400 - 800 s mm-2
. Superior quality fiber tracts were observed
with optimal b-value of 600 s mm-2
at 3T. These results should help guide future studies carried
out in VLM, in which fiber tractography data are correlated with biochemical, electron
microscopy and optical fluorescence data from VLM biopsies.

47
III. Effect of voxel size, echo time and fiber orientation on vastus lateralis muscle lipid
measurements assessed by 1
H-MRS and two-point Dixon MRI

48
A. Introduction
Localized hydrogen-1 magnetic resonance spectroscopy (1
H-MRS) is used routinely to measure
the relative concentrations of intramyocellualar (IMCL) and extramyocellular (EMCL) lipids
non-invasively in muscle[37]. The ability to distinguish IMCL from EMCL using 1
H-MRS is due
to change in bulk magnetic susceptibility (BMS) effects caused by their geometric arrangements
within muscle, which leads to a frequency separation between the two pools [7, 15].
IMCL are located within myocytes as spherical droplets and are independent of muscle
orientation relative to main magnetic field (B0). EMCL are nestled in long fatty septa, discretely
distributed along the muscle fiber bundles, and are majorly dependent on the orientation of the
muscle fibers relative to B0. The major downside of 1
H-MRS is that although two separated
peaks are detectable, the IMCL and EMCL peaks do partially overlap [15, 18, 25, 27].
Inconsistencies in quantitating EMCL limit the accuracy and reproducibility of these IMCL
concentration measurements and sophisticated peak-fitting with prior knowledge constraints are
essential for efficient quantification.
In vitro studies [25, 26] on bi-compartmental oil phantoms have demonstrated the influence of
lipid orientation relative to the direction of B0. These studies concluded that EMCL lipid strands
can be modeled as cylinder and the influence of its orientation on B0 [26, 27, 76, 77]
approximated as (3cos2
θ – 1). Changes in induced BMS in EMCL relative to B0 produce
disproportionate alterations in the magnitude, shift in the center of the resonance and line shapes
[15]. These effects are observed to be minimal if the orientations of the muscle fibers are parallel
to B0.
The orientation of EMCL found in skeletal muscle between fiber bundles and as interstitial lipid
deposits, can be indirectly assessed by estimating the muscle fiber pennation angle [64, 72, 78],

49
deduced from the diffusion of water molecules within the fibers using diffusion tensor imaging
(DTI).
The direction of maximum diffusion, i.e the principal direction of diffusion can be directly
obtained by computing the eigenvectors (ε1, ε2, ε3) and eigenvalues (λ1, λ2, λ3), of the tensor. The
pennation angle (PA) of muscle fibers then can be determined by assuming that the fiber
orientation coincides with the direction in which diffusion is least restricted, which is specified
by the primary eigenvector ε1, corresponding to the largest eigenvalue, λ1, with the SI axis of
subject (z-axis in the magnet reference frame).
1
H-MRS studies using short TE (20 ~ 50 ms) provide higher signal-to-noise ratio (SNR) and
minimize T2 relaxation changes, however, broader line widths and enhanced peak magnitude
with asymmetric appearance of EMCL were observed [28]. Massive EMCL resonance overlap
with the IMCL resulted in smaller chemical shifts (δ).
Recent studies at various field strengths [20, 28, 29] have shown improved resolution and
reliable separation of EMCL and IMCL lipid resonances at longer TE (135 ~ 280 msec) in
tibialis anterior (TA) and soleus (SO) muscles.
Although the majority of clinical physiology studies investigating the role of muscle lipid
metabolism utilize vastus lateralis (VL) biopsies, substantial amount of 1
H-MRS studies
measuring IMCL have been performed in SO, TA, or gastrocnemius (GA) muscles, due to better
separation between EMCL and IMCL. IMCL measurements by 1
H-MRS in VL remain limited,
but critically needed due to its clinical relevance.
While 1
H-MR spectroscopy presents the metabolite concentration in a large volume of muscle,
MR imaging methods, such as two-point Dixon MRI, provide high-resolution three-dimensional
depictions of muscle fat composition. Studies on calibrated phantoms [50, 54, 57] and direct

50
correlation with fat percentage levels with gas chromatography analysis suggest that fat fraction
FF (%) measurements from MRI studies are accurate. [53, 79]
The purpose of this current study is to evaluate the influence of voxel size, echo time on fiber
orientation, IMCL and EMCL lipid concentrations and fat fractions in human VL muscle using
1
H-MRS and diffusion tensor imaging (DTI) at 3T. The secondary aim is to validate that MRI-
based fat fraction measurement in the VL muscle correspond directly to standard 1
H-MRS fat
fraction measurements.
B. Methods
Twelve healthy subjects (8 males, 4 females; age: 28.17 ± 3.59 yo, BMI: 23.89 ± 3.14 kg/m2
)
were studied. Informed consent was obtained from all study subjects following institutional
review board (IRB) approval. The exclusion criteria included typical contraindications for MRI
(e.g. pacemakers and other potentially dangerous implanted devices), musculoskeletal disorders,
muscle anomalies, a history of prior surgeries and muscle trauma that necessitated medical
attention within the last 6 months. To minimize differences in the hydration of the muscles,
subjects were asked to refrain from participating in intense physical activities during the 48 hours
before the study.
1. MRI Protocol
All MRI and 1
H-MRS studies were performed on a 3.0 T MRI scanner (TIM Trio, Siemens
Healthcare, Malvern, PA) using a 4-channel wrap-around receive-only array coil. Subjects were
oriented in a supine position with the right leg placed as close to the center of the table. Sandbags
and foam blocks were used to stabilize the leg to avoid motion artifacts and compression of the
leg muscles. MR images and spectral data were acquired from the VL muscle. The coil was fixed
to the magnet table in a reproducible position to ensure similar positioning for all of the subjects.

51
Gradient-echo T1-weighted localizer images (TR = 7.8 ms, TE = 3.69 ms, FOV = 250 × 250 mm,
matrix size = 128 × 128, α = 20°, slice thickness = 5 mm, 15 slices) were acquired in the axial
(Figure 11) and sagittal views from the mid-thigh region to facilitate proper positioning of the
voxel for 1
H-MRS and image slices for DTI and two-point Dixon MRI.
Single voxel 1
H-MRS was performed on the subjects after an 8 hour overnight fast. Stimulated
echo acquisition mode (STEAM) MRS pulse sequence was acquired using two different voxel
sizes (VOI 1 = 15  15  15 mm3
and VOI 2 = 15  15 25 mm3
), with voxel positioning as
shown in Figure 11. Each subject underwent acquistions using a short TE = 30 ms, NSA = 64
and subsequently long TE = 270 ms, NSA = 128 with TR = 3000 ms, TM = 10 ms, 1024 data
points, and receiver BW = 2000 Hz. The short TE acquisition was 3’ 24‖ and long echo time
acquisition lasted 6’ 48‖. Water suppression (BW = 35 Hz) was used for metabolite acquisition.
Unsuppressed water spectra (TR = 3000 ms, TE = 30 ms, TM = 10 ms, NSA = 16) for VOI 1 and
VOI 2 were obtained for each subject. For each voxel placement, automated shimming, water
suppression, and transmit-receive gain were optimized, followed by manual adjustment of
gradient shimming targeting water line-widths of 20 Hz deemed acceptable. Figure 12 shows the
representative spectra acquired from another healthy subject at VOI 1 and VOI 2 using 30 ms
and 270 ms. Spectral peak fitting using AMARES algorithm can also be seen.
DTI images were acquired using a single-shot spin echo echo-planar imaging (SE-EPI) sequence
with 30 directions of diffusion sensitization (TR = 4500 ms, TE = 83 ms, b-value = 600 s/mm2
,
FOV = 250 mm × 250 mm, matrix size = 128 × 128, α = 90°, 12 axial 2D slices, slice thickness
= 5 mm, NSA = 1, BW = 1502 Hz/pixel, and inter-echo spacing = 0.73 ms for a total scan time
of 2’ 24‖. Center slice position for DTI images was fixed relative to the center of the voxel used
for spectroscopy.

52
Figure 11. 1
H-MRS of vastus lateralis at different voxel sizes and echo times
A. T1W MRI of the thigh mid-section with trace region showing the boundary of the vastus lateralis
muscle (VOI - white box). 1
H-MRS of vastus lateralis muscle obtained at 3T from a healthy subject using
VOI 1 (B, C) and VOI 2 (D, E) at short and long echo times. Good separation of IMCL and EMCL
methylene (-CH2) peaks at the expense of decreased SNR was observed using long echo times at two
different voxel sizes. Variation in the signal amplitudes (AU-arbitrary units) clearly depicting reduction of
EMCL-CH2 at smaller voxel size and longer echo time 1) IMCL- CH3 (0.9ppm), 2) EMCL-CH3 (1.1ppm)
3) IMCL-CH2 (1.3ppm) 4) EMCL-CH2 (1.5ppm), 5) broad components of –CH2-CH= and -OOC-CH2 6)
creatine CH3 resonance (3.02ppm), 7) TMA resonance (3.2ppm), 8) creatine CH2 resonance (3.91ppm), 9)
olefenic -CH=CH- resonance (5.3ppm).

53
Figure 12. 1
H-MRS lipid peak fitting using AMARES algorithm
1
H–MR lipid spectra collected from different voxel sizes and echo times in vastus lateralis muscle from another
healthy 25-yr-old male subject (BMI ~ 27 kg/m2
). C. Spectral fitting of lipid metabolite peaks with AMARES
algorithm and prior knowledge. The original lipid spectrum (0.7-1.9ppm region), estimated lipid measurements
(chemical shift (δ) between the lipid peaks was measured indirectly from the muscle pennation angle) and the
resultant individual components are shown.

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