Diffusion-weighted imaging is a method of MRI imaging capable of showing the diffusion in the tissue.

2. Introduction
 Two types of movement are found in tissues.
 One is coherent bulk flow, which occurs for blood or cerebrospinal fluid, this movement
arises from a difference in pressure between two locations, produced by contractions of
the heart. Direct visualization of blood flow within the vascular network is accomplished
using MR angiographic techniques.
 Another manifestation of this continuous movement of molecules is a relatively small
displacement of the molecule in space, known as translational motion.
 Although many processes in nature can cause translational motion, one of the most
important ones in biological systems is diffusion.

3. Introduction
 Diffusion of molecules occurs due to a concentration difference between two environments,
such as on either side of a cell membrane.
 Diffusion is thermodynamic in origin and is a non equilibrium process responsible for the
random transport of gases and nutrients from the extracellular space into the cell interior.
 The transit occurs from a region of high concentration to low concentration, analogous to
heat flow between hot and cold regions.
 In pure solutions, diffusion is characterized by a constant known as the diffusion coefficient
D, measured in units of mm2s–1, which describes the amount of material transported across
the membrane.

4. Introduction
 Water molecules in the body undergo a random motion called “Brownian motion” which is a
result of body heat.
 These constant random motions are uniform in all directions.
 But diffusion in body tissues is restricted by various organelles, membranes and tissue planes.
 Intracellular diffusion is more hindered than the extracellular component because of the
presence of cell membrane.

5. Introduction
 Because of the interaction of water molecule within biological tissues which can impede
the actual diffusion the measured diffusion is termed as apparent diffusion coefficient
(ADC) which is expressed in mm2/s.
 Sometimes restrictions in diffusion are directional, depending on the structure of the
tissues, and diffusion is also restricted in pathology.
 In areas of restricted diffusion the ADC is low, whereas in areas of free diffusion it is high.

6. Diffusion of water molecules (red arrows) is hindered in
the intracellular compartment due to the presence of
macromolecules, increased viscosity, and multiple
membranes. Only mild restriction of diffusion (due
principally to tortuosity) occurs in the extracellular space.

7. 7
experiment demonstrating pure diffusion is to place
the dye on an agar plate (where no convection
currents exist). Here the dye droplets are seen to
diffuse out symmetrically with time, gradually
enlarging and fading in color.

8.  The diffusion coefficient of water molecules in pure water is
D = 3×10‐3 mm2/s
 Over a time interval of Δt=50 ms (typically the measurement interval for diffusion MRI
experiments) the displacement of water molecules is 30 μm >> than typical cell dimensions,
which implies that in in‐vivo situations, diffusion will be highly restricted by the arrangement of
the cellular matrix (extra‐cellular water) or by the cellular membrane (intra‐cellular water).

9. Types
 Free diffusion:
 No obstacles facing the movement of the water molecules. This type of motion is
possible in cerebrospinal fluid (CSF).
 Isotropic restricted diffusion:
 In all spatial directions, the water molecules have limited displacement may be
pathological in nature (e.g. abscesses, tumors, etc.)
 Anisotropic restricted diffusion:
 Certain tissues constitute obstacles which direct the molecules in particular
directions: diffusion is only restricted in certain spatial directions.
 Such is the case with nerve fibers comprising beams of axons and concentric
layers of myelin (the fatty “sheath”) which prevents any transversal diffusion.

10. Principle of DWI
 The basic principle of DWI is based on the Brownian motion of water molecules which takes place
in the body and it utilizes the motion sensitizing gradients to study this motion .
 DWI can generate images in which the contrast between the structures is based on the diffusion
characteristics.
 The basis of all pulse sequences used in DWI is based on the technique suggested by Stejskal and
Tanner in 1965 and implemented by Le Bihan in 1986.
 Their technique essentially involves adding two diffusion sensitizing gradient, one on either side of
180° refocusing pulse.

11. Principle of DWI
 These gradient lobes have the same magnitude but are opposite in direction.
 The first gradient lobe is called dephasing gradient which will dephase the spins of water
molecules and the second is called the rephasing gradient which will rephase the spins to
their original state.
 The water molecules whose diffusion is not restricted will get dephased by the first gradient
lobe and during the process the molecules will move to another location in which the water
molecules will be subjected to a different magnetic environment so that the rephasing pulse
will not exactly rephase them to their original state.

12. Principle of DWI
 This will cause attenuation of signal from these water molecules as they are not exactly
rephased to produce a strong echo.
 Whereas, water molecules whose diffusion is restricted will not move and they will be
subjected to the same magnetic environment and the dephasing and rephasing gradients will
exactly neutralize each other and their spins will be in phase to produce a strong echo.
 These diffusion gradients can be applied in any of the axis (x, y, z) or in any combination and
it is called diffusion sensitizing direction.

13. Principle of DWI
 DWI sequences are acquired with ultrafast sequence ,EPI with two large gradient pulses applied after
excitation.
 Gradient pulse cancel each other if spins do not move, while moving spins experienced phase shift.
 In DWI, normal tissue that exhibits a high ADC has lower signal intensity than abnormal tissue that has
a low ADC as the molecules within it are free to move, while diffusion becomes restricted when
pathology is present.
 So in DWI sequences signal attenuation occur in normal tissue with random diffusion and bright signal
appear in tissue with restricted diffusion (e.g ischemia) .

14. 14
The Stejskal-Tanner pulsed gradient spin echo technique
forms the basis of current diffusion-weighted pulse
sequences. Stationary spins are not affected by the paired
gradients, but diffusing spins are dephased.

17. DW Images
 There are two types of DW images.
 Diffusion or trace images are those where damaged tissue that has restricted diffusion (low
ADC) is brighter than normal tissues where diffusion is free (high ADC).
 This is because spins in restricted tissue are refocused as they stay in the same place during
the application of both gradients.
 However, in normal tissue where diffusion is random, refocusing is not complete and signals
cancel.
 If motion varies rapidly, diffusion attenuation occurs and signal is lost in that area. Hence
abnormal tissue is brighter than normal tissue.

18.  ADC maps , are acquired via post - processing by calculating the ADC for each voxel of tissue
and allocating a signal intensity according to its value.
 Therefore restricted tissue, which has a low ADC, is darker than free diff using areas that have
a high ADC.
 The contrast is therefore the mirror of the trace images.
 By producing ADC maps it is possible to differentiate between areas with a low ADC and those
with a long T2 decay time.
 The ADC map enables differentiation of this area from the other high signal intensities seen on
the ADC map.
 These areas represent tissues with a long T2 decay time, not those with a low ADC.
DW Images

20. Apparent Diffusion Coefficient
 ADC image is a parametric map which is derived from the DW images.
 Magnitude of diffusion
 It needs images acquired at minimum of two b values.
 It reflects the true diffusion and also it depends on the spatial orientation, microscopic
perfusion, bulk tissue motion and pulse timing.
 ADC map is the Gray Scale representation of pixel by pixel ADC values calculated from
diffusion images.

21. Apparent Diffusion Coefficient
 For proper ADC value the b value should be optimal.
 The ADC is actually a tensor quantity or a matrix:
 The diagonal elements of this matrix can be combined to give information about the magnitude of
the apparent diffusion:
 (ADCxx + ADCyy + ADCzz)/3.

22. The b value
 Represents the strength of ‘diffusion sensitizing gradients’
 The b-value is a factor that reflects the strength and timing of the gradients used to
generate diffusion-weighted images.
 The higher the b-value, the stronger the diffusion.
 b factor is expressed in s/mm2(the opposite unit of D)
 The b value depends on the strength, duration and the spacing of the pulse gradient. The
large b value is achieved with increasing the gradient amplitude and direction and by
widening the interval between the gradient pulse
 To sense slow moving water molecules and smaller diffusion distances, b values should be
higher (e.g. b = 1000 s/mm2).
 A useful rule of thumb is to choose the b value such that (b X ADC) is nearly equal to 1

23. Cont..
 In principle, though, all the random motions are observable with a diffusion sequence,
 What, therefore, are the different scales on which these random motions take place?
 We can distinguish three such scales:
 – water, within the cells (diffusion coefficient Dint)
 – water, outside the cells (diffusion coefficient Dext)
 – blood, within the tissues: its tortuous trajectory and its numerous changes in direction are similar
to a random motion called “perfusion” (diffusion coefficient Dp).
 The velocity of the blood in this microcirculation is far greater than that of water in its intra- or
extra-cellular movements: the mechanism of perfusion thus involves far greater diffusion
coefficients: Dp >> Dint and Dp >> Dext.

24. Cont..
 However, only a fraction of each voxel is occupied by the capillary network which decreases the
apparent diffusion coefficient of the perfusion mechanism.
 Ultimately, we can say that Dp is around 10 times greater than Dint or Dext: this difference
means that it is possible to separate these three scales.
 Thus, if we work with a field Bo = 1.5 T, we estimate that for:
 b < 100 s/mm2, we measure large values for the diffusion coefficients
 300 < b < 3000 s/mm2, the diffusion distances measured tend to correspond to extra-cellular
water diffusion;
 b > 6000 s/mm2, we are dealing with intra-cellular diffusion.

25. What value should we choose for b, then?
 It is the extra-cellular water movements that we wish to study in diffusion MRI.
 We have also seen that by increasing the value of b, we increase the sensitivity of the sequence
to the phenomenon of diffusion.
 Increasing the value of b leads to a decrease in the SNR.
 For all of these reasons, we typically choose a value for b of around 1000 s/mm2.

26. Stejskal-Tanner pulsed gradient diffusion method.
b = γ² G² δ² (Δ−δ/3)

27. 27
Three brain images using b-values of 0, 1000, and 3000 s/mm² below show progressively more diffusion
weighting (as evidenced by the brighter corticospinal tracts) but also more noise

28. DWI Acquisition Steps
 The DW pulse sequence is first run with the DG's turned off or set to a very low value. This generates a
set of b0 ("b-zero") images that are T2-weighted and will serve as a baseline for later calculated maps.
(For abdominal imaging b50 images are often obtained, the small but nonzero gradient amplitude
helping to suppress signal in vessels).
 The DW sequence is then run with the DG's turned on individually or in combination and at various
strengths. This produces DW source images sensitized to diffusion in multiple different directions.
 The DW source images are combined to produce a set of Trace DW images, the first-line images used for
clinical diagnosis.
 An Apparent Diffusion Coefficient (ADC) map is then calculated using the data from the b0 and source
images. The ADC map is used to clarify abnormalities seen on the trace images.
 Further advanced processing can be optionally performed, creating additional calculated image sets for
analysis. These may include exponential ADC maps, fractional anisotropy images, principal diffusion
direction maps, and fiber tracking maps.
28

29. ARTIFACTS AND PITFALLS
 There are many artifacts and pitfalls which are inherent to the technique
itself:
 T2 Shine Through
 T2 Washout
 T2 Washout
 Eddy Current Artifacts
 Susceptibility Artifacts
 Chemical Shift Artifacts
 Motion Artifacts
29

30. T2 shine through
 This is one of the well-known phenomenon which can
potentially lead to a wrong diagnosis when diffusion
weighted images are interpreted in isolation.
 It is because DW sequence is based on a T2 weighted
sequence and its effects are shown in the final image. It
is very prominent in low b value images where the T2
effects predominate.
 For example, simple cysts in which diffusion is not
restricted are seen as hyperintense structure on DW
images.
 The ADC map or exponential images can help to
overcome this pitfall since both of them eliminate the T2
effect in the image and represent diffusion
characteristics
30

31. Trace DW image ADC Map T2-weighted SE image
31
Rounded left parietal lesion (a glioma) shows moderate brightness on Trace DW image. This is not due to
restricted diffusion, however, as the lesion is also bright on the ADC map (implying increased diffusivity).
The T2-weighted SE image confirms the brightness on the Trace image is a T2 "shine-through" effect.

32. T2 Washout
 This phenomenon is seen when the T2 hyperintensity is
balanced by the increase in ADC in the DW image.
 This is usually seen in vasogenic edema in which there
is increase in ADC because of increased diffusivity and
that effect is balanced by the T2 hyperintensity.
 Thus in the DW image the lesion appears isointense.
Again this effect can be overcome by interpreting DW
images along with ADC map in which the lesion appears
hyperintense
32

33. T2 Blackout
 This effect is noted when the T2 hypointensity is
reflected on the DW image as hypointensity It is usually
seen in hematoma which is seen as T2 hypointensity
because of susceptibility effect.
33

34. Cont..
 Eddy current artifacts
 Eddy Currents are produced in the patient body and in the scanner hardware by rapidly
changing magnetic gradients which are used in EPI sequence for sensing diffusion and
readout.
 This can cause image distortion, spatial blurring and misregistration artifacts.
 Susceptibility artifacts
 This is seen in EPI sequences at the bone-soft tissue or air soft tissue interface. This leads
to accumulation of phase shifts which are more pronounced in the phase encoding
direction. This can be reduced by using DW-HASTE sequences.

35. cont..
 Effects of contrast medium
 The gadolinium based contrast media used for MR imaging can have an effect on the diffusion
parameters especially in kidneys where they are concentrated by causing paramagnetic effects
locally.
 Recent evidence suggests that the ADC values of kidneys can be significantly lower in the post
contrast image than the pre-contrast image. Similar effects were not found in the liver, pancreas
and spleen.

36. Clinical applications for diffusion

37. Neuro imaging
 DWI is the gold standard for imaging of stroke.
 Highly sensitive in detecting ischemia.
 Has ability to differentiate acute infarct from old lesions
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38. Clinical applications for diffusion
 Study of
an
ischemic
accident

39. Head and neck
 Can differentiate pleomorphic adenoma from benign and malignant lesions of parotid gland.
 Can differentiate Sino-nasal malignant lesion (ADC is low) from benign lesions
 ADC map can be used to characterize the tumor and also in grading of tumor
 Can differentiate squamous cell carcinoma from lymphoma.
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40. Thorax
 In lung carcinoma ,malignant lesions have usually lower ADC than benign lesions
 Can differentiate collapsed lung from malignant lesions
 BREAST:
 Tumor response after therapy.
 Increase in ADC value in comparison to pretreatment value can be used as a non invasive biomarker
of disease response.
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41. Liver
 Hemangioma and simple cyst show high ADC value
 malignant lesions such as hepatic adenoma and focal nodular hyperplasia can be differentiated .
 Gall bladder and biliary duct
 Malignant gallbladder lesions from the benign.
 Malignant lesion tend to show high signal intensity on DWI and low ADC values compared to benign
lesions.
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42.  Pancreas
 Carcinoma of pancreas show low ADC values compared to rest of the normal pancreas.
 Kidneys and adrenals:
 Malignant renal lesions can be differentiated from the benign lesions.
 Renal cysts show high ADC values than normal parenchyma and other solid lesions.
 In characterization of malignant masses.
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43. Female pelvis
 Endometrial carcinoma has low ADC compared to normal endometrium
 The ADC value differ according to different menstrual phases.
 Cervical carcinoma has low ADC compared to normal cervical stroma.
 Can differentiate the squamous cell carcinoma from the adenocarcinoma of cervix.
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44. Diffusion Tensor Imaging
 Is rapidly evolving non-invasive MRI technique for delineating the anatomy and pathology of
WM tracts.
 Useful for pre surgical planning in patients with intra-axial, focal mass lesions.
 Delineation of the various tracts around a focal lesion and knowledge of the status of
infiltration Vs displacement , that help neurosurgeons decide on the surgical approach.

45. INTRODUCTION
 Diffusion tensor imaging (DTI) is a noninvasive tool for studying white matter composition.
 The contrast in DTI is provided by water movement within tissues.
 Diffusion tensor metrics are surrogate markers of normal age-related maturational changes
and disease states that affect the composition of white matter, thereby affecting the
diffusion of water.
 It is common for DTI to be processed using a single-tensor model; that is, to assume there is
only one dominant fiber direction within each voxel

46. DIFFUSION TENSOR
 The acquisition of the DTI requires the acquisition of six directionally weighted samples of the
effect of the diffusion process relative to the axes of the imaging system.
 Specifically, the magnitude of the diffusion attenuation in MR signal along the x, y, and z axes
themselves, as well as in the xy, xz, and yz directions, must be measured.
 The attenuation of MR signal in the presence of gradients in each of these directions is
calculated relative to an image acquired with no diffusion encoding (baseline).
 Hence, seven (six directions, one baseline) acquisitions for each slice level are required.
 Once the tensor is sampled, the magnitude calculated from the trace expresses the total (no
directionality) diffusivity at each voxel location

47. Contd
 The directionality of diffusion is assessed by an Eigen decomposition of the diffusion tensor.
 The largest Eigen value corresponds to the major axis of the diffusion ellipsoid and so represents
the major directionality of diffusion at that location.
 The information is provided by 3 eigen values which represent the direction of 3 major axes of the
ellipsoid and 3 eigen vectors that represent the magnitude in these directions
 In the white matter, diffusion is anisotropic and is related to cell density and integrity, axonal
integrity, and myelination status
The diffusion ellipsoid has three unit vectors, (ε1, ε2,
and ε3) called eigenvectors, with corresponding lengths
(λ1, λ2, and λ3), the eigenvalues. For perfect isotropic
diffusion, the ellipsoid becomes a sphere with λ1 = λ2 =
λ3.

48. Extracting information from DTI
 Using DTI, diffusion data can be analyzed in three ways to provide information on tissue
microstructure and architecture for each voxel.
 The mean diffusivity,
 The degree of anisotropy
 The main direction of diffusivity/mapping of fiber orientation
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49. Fractional anisotropy
 Is a measure of the portion of the magnitude of the diffusion due to anisotropy
 Ranges from 0 to 1
 0 isotropic diffusion
 1 purely anisotropic diffusion
 Can characterize demyelinating lesion , e.g breakdown of myelin and axonal loss can reduce FA and
remyelination can increase FA
 FA of CSF is 0

50. Relative anisotropy
 Relative anisotropy is derived from a ratio of the anisotropic portion of the diffusion tensor to the
isotropic portion
 For highly anisotropic medium , RA tends toward 1.41

51. Mean diffusivity MD
 Measure of directionally averaged magnitude of diffusion, (λ1+λ2+λ3)/3
 Higher MD values mean that the tissue
 is more isotropic
 MD is an inverse measure of membrane density and tumor cellularity

52. Axial diffusivity Da
 Da is the apparent diffusion parallel to white matter tracts
 Da = Principal Eigen value = λ1
 Da is variable in white matter pathologies
 Da decreases in axonal degeneration

53. Radial diffusivity Rd
 Apparent diffusion perpendicular to the white matter tracts
 Dr = (λ2+λ3)/2
 Dr generally increases in white matter demyelination
 Change in axonal diameter and density also affect Dr

54. Color coding
 A color is assigned for each voxel location by using the primary eigenvector (corresponding to the
largest eigenvalue) of the diffusion tensor.
 At each voxel, the absolute values of the x, y, and z components are used as the red, green, and
blue color values, respectively, such that a red voxel in the image means the vector points left–
right (or right–left), green means anteroposteriorly (or posteroanteriorly), and blue represents
superoinferiorly (or inferosuperiorly).
 For instance, if a vector points mostly in the red direction, then the x value of the vector will be
large and the color will be pure red; otherwise, the color will be a mixture of red, green, and
blue, depending on the magnitudes of the vector components

55. Clinical application
Edematous or tumor-
infiltrated tracts lose
some anisotropy
but remain
identifiable
Destroyed WM tracts lose
directional organization
and diffusion anisotropy is lost
completely
Intact WM tracts
displaced by tumor
retain anisotropy
and remain
identifiable

56. Cerebral ischemia

57. Multiple Sclerosis

58. Epilepsy

59. Metabolic disorders

60. Brain tumor

61. Limitation of DTI
 Lacks sensitivity in gray matter
 Less effective when complexity of tissue increases
 Appears normal in sub‐acute injuries

62. Effect of low SNR ON DTI
 As a rule of thumb ,the SNR of b=0 s/mm2 images of a DTI should be at least 20 to derive relative
unbiased measures of parameter such as FA.
 Insufficient SNR result in bias of the estimated diffusion tensor parameter.
 Anisotropy indices such as FA or RA can be significantly overestimated (biased)at low SNR.
 Very small signal tend to be over estimated by noise.
 overestimation of diffusion signal result in underestimation of diffusivity.
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63. DTI
 DTI is a powerful tool to investigate microstructural white matter changes and brain connectivity
 DTI is currently being clinically used in conjunction with functional MRI for presurgical brain
mapping and is gradually becoming the standard of care
 For indications such as demyelination, trauma, epilepsy and congenital anomalies, DTI provides
useful information that is clinically helpful and often helps in diagnostic interpretation and clinical
decision making
 As the technique becomes more robust, it will be increasingly applied in clinical practice for other
indications

64. Fiber tractography
 By combining the anisotropy data with the directionality it is possible to estimate fiber
orientation.
 In the algorithm, called ‘fiber assignment by continuous tracking’ (FACT), reconstruction
of the tract is performed by sequentially piecing together discrete and shortly spaced
estimates of fiber orientation to form continuous trajectories.
64

65. The problem of crossed fibers
 Fiber-crossing is a significant problems in tractography where this occurs, there is not one but
many prevailing diffusion direction.
 The tensor model is unable to distinguish between these different directions
 Where fibers cross, two different conclusions may arise:
 - the reconstruction of the fiber continues in an incorrect direction;
 – the reconstruction of the fiber is stopped because the anisotropy factor is too low.
 In any case, neither of these conclusions is satisfactory.
 Certain techniques, which are more elaborate than FACT, can help to limit errors due to fiber
crossing.

67. Whole brain tractography of the human in vivo data using the corpus callosum as a
ROI, with directions: left – right (red), superior – inferior (blue), anterior – posterior
(green).

68. Optimizing DTI for TRACTOGRAPHY
 The acquisition for fiber tractography must be contiguous in 3D with no gaps between sections.
 The voxel must be isotropic(section thickness is the same as in-plane pixel length)
 This requires much thinner sections .
 Large voxel size may result in partial volume averaging meaning it might contain more than 1
fiber.
 The presence of multiple voxel fiber population with different orientation will cause errors in
the estimation of fiber direction.
 This can be overcomed by using HARDI
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69. Diffusion kurtosis imaging
 Diffusional kurtosis imaging (DKI) extends conventional diffusion tensor imaging (DTI) by
estimating the kurtosis of the water diffusion probability distribution function.
 The kurtosis is a general, dimensionless statistic for quantifying the non-Gaussianity of any
distribution. A positive kurtosis means the distribution is more strongly peaked and has heavier
tails than a Gaussian distribution with the same variance.
 Water diffusion in biological tissues is non-Gaussian due to the effects of cellular
microstructure (e.g., cell membranes and organelles). This is particularly evident in brain,
where water diffusion is strongly restricted by myelinated axons.
 Qualitatively, a large diffusional kurtosis suggests a high degree of diffusional heterogeneity
and microstructural complexity

70. CONTD..
 Because diffusion in brain is anisotropic, DKI requires the introduction of a diffusional kurtosis tensor
in addition to the diffusion tensor used in DTI.
 From the diffusion and diffusional kurtosis tensors (which are calculated together from a single
diffusion-weighted imaging dataset), several rotationally invariant metrics can be computed.
 These include standard DTI metrics, such as the mean diffusivity and fractional anisotropy, as well as
metrics reflecting the diffusional kurtosis, such as the mean, axial, and radial kurtoses.
 The diffusional kurtosis metrics are strongly linked to cellular microstructure, as this is the main
source of diffusional non-Gaussianity in tissues.
 The extra information provided by DKI can also resolve intra-voxel fiber crossings and thus be used to
improve fiber tractography of white matter

71. CONTD..
 An advantage of DKI is that it is relatively simple to implement for human imaging on
conventional MRI clinical scanners.
 DKI protocols differ from DTI protocols in requiring at least 3 b-values (as compared to 2 b-
values for DTI) and at least 15 independent diffusion gradient directions (as compared to 6
for DTI).
 Typical protocols for brain have b-values of 0, 1000, 2000 s/mm2 with 30 diffusion directions.
Image post-processing requires the use of specialized algorithms

72. Q space imaging
 Q space imaging is an advanced diffusion imaging that provide a model free diffusion description of
the diffusion process in complex biological tissue
 As compared to model based approach, this model free description of the diffusion process has the
potential to yield more accurate structural and orientational information of the image object
regardless of its complexity
 Unlike DTI, this imaging requires the application of much stronger diffusion encoding gradient and
much larger no of encoding step
 The signal generated by the 3D diffusion measurement of each voxel is called q space signal
 The q-space is the 3D space with coordinates (qx, qy, qz) defined by the diffusion-encoding gradients
on the three channels Gx, Gy, and Gz.

73. CONTD..
 There are two types of q space imaging technique:
 Diffusion spectrum imaging
 Q ball imaging

74. Diffusion spectrum imaging
 In contrast to conventional DTI, which involves 6 diffusion-encoding gradients, DSI
characteristically uses 515 diffusion-encoding gradient vectors.
 Each point in q-space represents the MR signal intensity in the voxel during the application of a
particular q-vector.
 The MR signal is brightest when the q-vector is orthogonal to fiber direction and most
attenuated (weakest) when it is applied along the direction of fiber orientation.
 An inverse Fourier transform of q-space yields a probability density function of fiber orientation
per voxel and may include multiple local maxima, each denoting a distinct fiber population.

75. Q ball imaging
 To overcome the time consuming disadvantage of DSI, while maintaining the capability of
probing the tissue structure without any modeling, the q-ball imaging technique was introduced .
 Instead of sampling q-space on a complete 3D q-space trajectory, such as a 3D Cartesian grid,
the q-ball technique traverses q-space only on a sphere with large enough diameter , so that the
average displacement of molecules can be captured with high enough precision.
 Although the q-ball technique still requires quite a large number of q values (diffusion encoding
directions), this number is considerable smaller than the full-fledged DSI approach, resulting in
much shorter scan time while still maintaining the capability to resolve complex tissue structure,
such as multiple crossing fibers.

76. HARDI
 High angular resolution diffusion imaging (HARDI) is a magnetic resonance imaging (MRI)
technique, determining the diffusion of water molecules in tissue in vivo and architecture of
nerve root tracts
 HARDI is advantageous over the well-known diffusion tensor imaging (DTI), since it is able to
extract more than one fiber orientation within a voxel and can therefore resolve crossing, kissing
or fanning fiber tracts.
 Basically, we look into the voxel from a large number of different
directions (typically 40 or more).

77. HARDI
 It employs dense sampling of the signal within a sphere with a constant high b value (typically >4000
sec/mm2) in q-space.
 The orientation distribution function is estimated from the resultant data by using various
algorithms.
 Like DTI, q-ball imaging is based on a hypothesis about the shape of the diffusion probability density
function.
 With q-ball imaging, the images obtained resemble those acquired with diffusion spectrum imaging.

78. 78

79. Whole Body Diffusion Weighted Imaging
 Whole body DWI along with anatomical images such as T1 and T2 can be very useful for evaluation of
malignant tumors.
 Diffusion Weighted Imaging with background Body signal Suppression(DWIBS) is a whole body diffusion
technique done using DW-EPI sequences and STIR for fat suppression.
 It is a free breathing technique in which the scan are taken from base of skull to mid thigh.
 The b value in the ranges of 1000 is used which provide good background suppression.
 Because of high b value, the body fat, muscle and most of the organs get suppressed which highlights
the abnormal areas.
 The disadvantages of this technique is long acquisition times.
80

80. Whole Body Diffusion Weighted Imaging
 Diffusion-weighted whole-body imaging with background body signal suppression” (DWIBS) now
allows acquisition of volumetric diffusion-weighted images of the entire body.
 Thin DWI images can be combined to form reformatted images.
 It has been shown to perform as well as PET/CT in the evaluation of tumors such as lymphoma and
non-small cell lung cancer.18 The DWIBS is a non-invasive technique without the use of radiation
and has a huge potential in oncologic imaging
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Whole-body DWI in a patient with metastatic melanoma

82. 3 vs 1.5T Whole body DWI
 With current technology, whole-body DWI is easier to implement with 1.5-T than with 3-T systems
,at 3 T is often more difficult because artifacts can be more challenging to control and minimize.
 In addition to the general issues at 3 T, such as dielectric effects and tissue-specific absorption
rates there are challenges related to the DWI measurements.
 First, eddy currents induced by rapid switching of the magnetic gradient field with EPI cause
RM that results in geometric distortion and image shearing .
 Second, because of the greater B1 field inhomogeneity at 3 T, it can be difficult to achieve
uniform fat suppression across large fields of view, and the result is chemical-shift and ghosting
artifacts .
 Third, because the MR frequency offsets applied can differ markedly between anatomic
stations, voxel shift can occur in the phase-encoding direction and cause misalignment of
structures (e.g., the spinal cord) between imaging stations.

83. 3 vs 1.5T Whole body DWI
 The result is difficulty in accurate alignment of individual imaging stations to produce smooth
composite images (e.g., whole-body sagittal images), even though images from individual anatomic
stations are of high quality.
 This limitation may not affect the diagnostic quality of the acquired images but falls short of
producing pleasing reformatted images for clinicians and patients to review.
 Coronal reformats are usually less affected because the phase-encoding direction is often antero
posterior.
 Despite the limitations, it is clear that imaging at 3 T has the intrinsic advantage of a higher signal-to-
noise ratio (SNR), which can enhance lesion detection.

84. 85

85. 86

86. 87

87. 88

88. CONCLUSION
 DWI is an evolving technique that provides a new paradigm for tissue characterization.
 Its ability to provide both qualitative and quantitative insight in to complex diffusion mechanisms
and changes at a cellular level with only a small time penalty have made this technique a valuable
addition to clinical MR protocols.
 However, DW sequences must be interpreted in conjunction with other MR sequences to avoid the
pitfalls of this technique.

89. Reference
 Denis Le Bihan, MD, PhD et.al ; Diffusion Tensor Imaging: Concepts and Applications; JOURNAL OF
MAGNETIC RESONANCE IMAGING 13:534–546 (2001).
 Understanding Diffusion MR Imaging Techniques: From Scalar Diffusion-weighted Imaging to
Diffusion Tensor Imaging and Beyond:Radiographics
 Malayeri AA, El Khouli RH, Zaheer A, Jacobs MA, CoronaVillalobos CP, Kamel IR, Macura KJ.
Principles and applications of diffusion-weighted imaging in cancer detection, staging, and
treatment follow-up. Radiographics. 2011;31(6):1773-91.
 Patric Hagmann, MD, PhD et.al ,Understanding Diffusion MR Imaging Techniques: From Scalar
Diffusion-weighted Imaging to Diffusion Tensor Imaging and Beyond. Radiographics 2006; 26:S205–
S223 ● Published online 10.1148/rg.26si065510.
 Dietrich O et.al, Technical aspects of MR diffusion imaging of body. European Journal of Radiology.
2010;76(3): 314-322.
 Jacques-Donald Tournier et.al , Diffusion Tensor Imaging and Beyond; Magn Reson Med. 2011 June ;
65(6): 152–1556. doi:10.1002/mrm.22924.

90. Reference
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