The document provides an overview of MRI physics, detailing concepts like precession, relaxation times (T1 and T2), and contrast generation in MRI imaging. It covers the physics principles relevant to MRI, such as Lenz's law and Faraday's law, as well as technical details about the MRI system, including k-space and the spin-echo sequence. It also discusses applications of MRI in medical imaging for various conditions such as brain tumors, ischemic stroke, and cancer prognosis through magnetic resonance spectroscopic imaging (MRSI).

1. MRI Physics 101
Prof. Sairam Geethanath, Ph.D.
Medical Imaging Research Centre
Dayananda Sagar Institutions
1st October 2016
A"enua'on (E/E0)
(µV)
γ2G2δ2(∆- δ/3) (109 sm-2)

2. Declara'on
• I have no conflicts of interest to declare, with respect to the contents
of this presenta'on and data shown here is already in public domain
• Wipro GE Healthcare funds part of my research as part of a DST
Technology systems development grant
MIRC 2

3. Outline
• High school physics – relevant to MRI
• Overview of the MRI system
• Precession and the Pelton Wheel
• Relaxa'on 'mes: T1 and T2
• Basis for contrast genera'on
• Spin Echo sequence: An example of image genera'on
• Spa'al localiza'on through magne'c field gradient
• k-space and its traversal
• Tools to get started
• Pulseq - Acquisi'on
• GPI - Reconstruc'on
• DIY assignments
MIRC 3

4. Important high school Physics
4
Lenz’s law Reciprocity theorem Biot Savart’s Law Faraday’s law
Vineet, MIRC Abitha, MIRC Aparana, MIRC Anusha, MIRC

5. Gradient
coils
Subject
Radio
frequency
coil
Magnet
Overview of a MRI system
Image courtesy: MRI scanner cutaway: Colinmcnulty.com
Representa8on of a typical MRI scanner
MIRC 5

6. Larmor Equation
Effect of the B0 , G.r and B1 fields
z
x
y
MIRC 6

7. Why do we need to 'p the spins? – Understanding precession

8. • The application of an RF pulse, causes alignment of spins away from the longitudinal axis (lower energy state) on a
transverse plane (higher energy state)
• Spins release the absorbed energy and drop back to their lower energy states
• Spin can exchange a quantum of energy with the lattice (also precessing at same frequency)
• Spin transitions from 𝑚=−​1/2 (excited state) to 𝑚=​1/2 (ground state) is accompanied by a transition
upwards in energy from some lower lattice state to a higher lattice state h
• Energy transition must be equal ‘ law of conservation of energy’
• Transfer of energy occurs through collisions, rotations, or electromagnetic interactions with the surrounding lattice
• This energy loss is unrecoverable and represents the transfer of heat.
h"p://mriques'ons.com
Magne'c resonance imaging: Physical principles and sequence design
𝑚=−​1/2
𝑚=​1/2
ℎ
𝑙
𝑝𝑟𝑜𝑡𝑜𝑛 𝑙𝑎𝑡𝑡𝑖𝑐𝑒
Boltzmann equa'on for
popula'on states
T1 relaxation

9. • The electromagnetic field from a particle can be considered to emanate
from an idealized tiny bar magnet with north and south poles ("dipole") .
• A dipole-dipole interaction is a "through space" interaction of the
fields from two spinning particles
• Four major factors determine the strength of the dipolar interaction: (1)
types of spins; (2) the distance between them; (3) the angle between
them; and (4) their relative motion.
h"p://mriques'ons.com/dipole-dipole-interac'ons.html
T1 of water
T1 of water doped with
Copper Sulphate
T1 of oil
Dipole – dipole interactions

10. h"p://mriques'ons.com
Magne'c resonance imaging: Physical principles and sequence design
0 2 4
4.0
4.5
5.0
Mz
t
M0
First RF Second RF
TR
T1
0.63 M0
• Spin lamce interac'on result in re-growth of longitudinal magne'za'on
• Rate of change of longitudinal magne'za'on is captured by an exponen'al recovery,
is a cross product of magne'za'on moment M and the applied external field B
• Synonyms: longitudinal relaa'on, thermal relaxa'on and spin-lamce relaxa'on
T1 recovery

11. Spin-Spin and Effec-ve Spin-Spin relaxa-on:
• NMR signal – phase coherence of nuclear spins & Exponen'al signal decay – loss of phase coherence
• Spin-spin relaxa'on – dipole coupling between neighbouring spins
• Sampled signal, as a func'on of 'me is given by,
S(t)=S0 exp(-t/T2) Where, S0 – ini'al signal magnitude at t=0
• Generally, the field is not en'rely homogeneous
• Phase coherence loss – combina'on of spin-spin relaxa'on and magne'c field inhomogeneity, which introduce
a range of Larmor frequencies
• Dispersion of frequencies – loss of coherence- signal decay
In completely homogeneous field, the phase coherence loss is due to spin-spin relaxa8on
T2 relaxation

12. • Effec've spin-spin relaxa'on 'me constant, T2
* is defined as,
​1/T2∗ = ​1/T2 +γ∆B0
Where,
γ – gyromagne'c ra'o of the observed nucleus
∆B0 – magne'c field inhomogeneity
• Signal as a func'on of 'me in situa'on with field inhomogeneity is given by,
S(t)=S0 exp(-t/T2∗)
Where,
S0 – ini'al signal magnitude at t=0
Phase coherence loss due to spin-spin relaxa8on is irreversible, whereas loss due to field inhomogeneity can be
reversed by Spin Echo
(called as Hahn Echo)Erwin Hahn
T2
* relaxation

13. Figure1: Spin-Echo pulse sequence diagram
• The 'me between the 90°pulse and the 180°pulse is called TΕ, the echo-8me
• Reversing the de-phasing due to magne'c field inhomogeneity is the goal of this basic spin-echo experiment
• Only the de-phasing that occurred as a result of magne'c field inhomogeneity will be re-focused
spin echo
Terranova student Guide, Magritek Limited

14. • Sampling commences at the centre of the echo
• Delay between the 180°pulse and the first sampled data point is TΕ
• TΕ must be chosen to be long enough
- to view the en're echo
- to allow for the complete relaxa'on of the signal excited
T2 Measurement:
• Measured using a succession of spin-echo experiments with incrementally longer echo 'mes
• The plot of echo amplitude as a func'on of echo 'me will be an exponen'al decay with a characteris'c decay
'me constant, T2
• The echo amplitude is given by,
where E - amplitude of an echo acquired with TΕ
E0 - echo amplitude in the absence of a T2 decay

15. Relaxa'on processes – Bloch equa'ons & contrast
MIRC 15
h"ps://www.chemie.uni-hamburg.de/nmr/insensi've/tutorial/en.lproj/vector_model.html
h"p://www.dayanandasagar.edu/index.php/sharing
Bloch, 1946
T1 T2 DIFFUSION

16. Spa'al localiza'on – Lauterbur gradient experiment
MIRC 16
Lauterbur, Nature, 1973

17. Theory
The signal acquired is the Free Induc'on Decay (FID) from all the spins of the par'cular slice
The Larmor frequency is given by

18. f
t
A
A
Discrete Fourier Transform

19. A visual representa'on of k-space

20. Top view of k-space
• Ideal k-space is Hermi'an in
nature, discoun'ng errors from
measurement
• Used in acquisi'ons like HASTE

21. Jargon for engineers J

22. Front view Top view
An analogy for k-space trajectory
Consider a hill

23. 20 40 60 80 100 120
50
100
150
200
250
Few k-space trajectories trajectories
y
x

24. Example problem 1:
Design Cartesian k-space trajectory
Given parameters:
∆x = 1 mm
∆y = 1 mm
Lx = 25.6 cm
L y = 25.6 cm
Evaluate the unknowns :
Variable Value
Nx
Ny
256
256
3.9 m-1
3.9 m-1
[-500, 500] m-1
[-500, 500] m-1

25. kx
ky
RF Pulse
Gz
Gy
Gx
Timing Diagram Depic'ng Cartesian Sampling
Gx
Gy
Gz
RF Pulse

26. Reconstruc'on & ar'facts

27. Open source tool for PSD - Pulseq
MIRC 27
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09
0
500
1000
1500
RFmag(Hz)
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09
t (s)
0
2
4
RFph(rad)
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09
t (s)
-500
0
500
Gz(kHz/m)
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09
-600
-400
-200
0
Gy(kHz/m)
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09
-1000
0
1000
Gx(kHz/m)
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09
-1
0
1
ADC
Source: h"p://pulseq.github.io/ (Needs Matlab installed)
Layton et. al., MRM 2016

28. Open source tool for students to use – recon - GPI
MIRC 28
• h"p://gpilab.com/
2016/09/gpi-on-windows/
• Simula'on
• Reconstruc'on
• Image Processing
• Visualiza'on
• Mac OS X/ Ubuntu /
Windows
Zwart et. al., MRM, 2016

29. DIY assignments
MIRC 29
Course assignments (and solu'ons):
1. Introduc'on and preliminaries
2. Physics
3. WORK IN PROGRESS (Introductory PSD and recon)
4. Fast Imaging
5. MR applica'ons

30. ?
MIRC 30
sairam.geethanath@dayanandasagar.edu
http://dayanandasagar.edu/index.php/mirc
http://dayanandasagar.edu/index.php/sharing

31. Prof. Sairam Geethanath, Ph.D.
Medical Imaging Research Centre
Dayananda Sagar Institutions
1st October 2016

32. ¡ Head
¡ Tumors, aneurysms, bleeding in the brain,
¡ Nerve injury, damage caused by stroke.
¡ Spine
¡ Discs and nerves of the spine for conditions such
as spinal stenosis, disc bulges, and spinal tumors.
¡ Chest : Heart, the valves, and coronary blood vessels
¡ Blood vessels and ?low – Dr. Ramesh Venkatesan’s talk
¡ Abdomen and pelvis
¡ Belly, liver, gallbladder, pancreas, kidneys, and bladder
¡ Bones and joints
¡ Arthritis, problems with joints ,bone marrow problems,
¡ Bone tumors, cartilage problems,
¡ Torn ligaments or tendons, infection.

34. ¡ DWI is a commonly used MRI sequence for evaluation of
acute ischaemic stroke
¡ Sensitive in detection of small and early infarcts
¡ Non invasive way of understanding brain structural connectivity and
macroscopic axonal organization
¡ DW image is generated based on the directional rate of diffusion of
water molecules inside the brain due to Brownian motion
¡ Image intensities inversely related to the relative mobility of water
molecules in tissue and the direction of the motion

35. Result Interpretation
Quantitative analysis
Segmentation
Feature Extraction
Visualization
(may include generating FA,ADC, Tractograpghy)
Processing
(may include DWI enhancement using Super resolution techniques )
Preprocessing
(may include registration,skull stripping,normalization motion , denoisng of low field MR
image/DWI)

36. ¡ Dark regions – water diffusing slower,
more obstacles to movement OR
increased viscosity
¡ Bright regions – water diffusing faster
DWI
¡ Bright regions – decreased water diffusion
¡ Dark regions – increased water diffusion
Figure Source: www.radiopaedia.org
ADC
Matlab Code available with Arush, MIRC

37. COLOUR FA MAP TRACTOGRAPHY
• According to the principal direction
of diffusion, colour coding of the
diffusion data is done
• Red - transverse axis (x-axis)
• Blue – superior-inferior (z –axis)
• Green – anterior-posterior axis (y-
axis)
• Intensity of the colour is
proportional to the fractional
anisotropy
• It is 3D modeling technique used to
visually represent neural tracts
using data collected by diffusion
tensor imaging (DTI)
• Voxels are connected based upon
similarities in the maximum
diffusion direction.
Figure Source: www.radiopaedia.org
Matlab Code available with Arush, MIRC

38. ADC map computa'on
b=0 b=100 b=200 b=500 b=1000
Signal intensity decreasing with increase
in b-value ADC map scanner ADC map matlab

39. FA map Mean Diffusivity map
Scanner
MATLAB
Color coded FA map
Frac-onal Anisotropy map

40. FA Col FA
No
filter
PM filter
using
fixed
Kappa
PM filter
with
Automated
Kappa
Scanner results
Diffusion Weighted images denoising for FA map computa-on

41. Figure source: Nucifora et al. Radiology 245:2 (2007)
Corticospinal Tracts -ProbabilisticCorticospinal Tracts - Streamline
1.Streamline tractography
• Connects neighbouring voxels
from user defined voxels (seed
regions)
• Tracts are traced until
termination criteria are met.
2.Probabilistic tractography
• Value of each voxel in the map is the
probability the voxel which is in the
diffusion path between the ROIs.
• It provides quantitative probability of
connection at each voxel
• Allows tracking into regions where there is
low anisotropy.

42. Degree of anisotropy Streamline tractography
Probabilistic tractography
Figure source :Nucifora et al. Radiology 245:2 (2007)

43. • It has been well established that magne'c resonance
imaging (MRI) provides cri'cal informa'on about
cancer [3]
• Magne'c resonance spectroscopic imaging (MRSI)
furthers this capability by providing informa'on
about the presence of certain ‘metabolites’ which
are known to be important prognos'c markers of
cancer [4] (stroke, AD, energy metabolism, TCA
cycle)
• MRSI provides informa'on about the spa'al
distribu'on of these metabolites, hence enabling
metabolic imaging
[3] Huk WJ et al., Neurosurgical Review 7(4) 1984;
[4] Preul MC et al., Nat. Med. 2(3) 1996;
Metabolic imaging: applica'ons
CANCER
NORMAL
[5] H Kugel et al., Radiology 183 June 1992 MIRC 43
[5]

44. • 3D- PRESS makes it possible to localize the signal in the voxel formed by the intersection of the three
slices
Figure 7: Display of the volume of interest (voxel) located at the intersec8on of the slices
[3]*

45. Spectroscopic Imaging Methods
• Spectroscopic Imaging methods map spa'al distribu'on of components with different chemical shi}s
• This imaging is a combina'on of spa'al ad spectral imaging
• Goal : To obtain NMR spectrum at each spa'al posi'on/ to display an image of each chemical shi}
1. 3D Fourier Transform Spectroscopic Imaging:
RF
GZ
Gy
Gx
DAQ
Figure 4: 3D Fourier Transform Spectroscopic Imaging Sequence, Phase
encoding in x and y is followed by data acquisi8on (DAQ) with gradients
turned off
1*
RF
GZ
Gy
Gx
DAQ
TE
90 180
Figure 5: 3DFT Spin Echo Spectroscopic Imaging Sequence

46. 2. Spectroscopic Imaging with Time – Varying Gradients
• Signal is readout in presence of gradient rota'ng at angular frequency Ω as in figure 6
• Using these gradients, data can be acquired over a range of frequencies thus avoiding aliasing
Figure 6: Spectroscopic Imaging Sequence with Rota8ng Gradients
t RF
GZ
Gy
Gx
DAQ
sin Ωt
cos Ωt
1*

47. • Long acquisi'on 'mes for MRSI
• A typical MRSI protocol (32 X 32 X 512) takes ~ 10-12 minutes
• Difficult to maintain anatomical posture for long 'me
• Increases pa'ent discomfort, likelihood of early termina'on of
study
• Discourages rou'ne clinical use of this powerful MRI technique
• To increase throughput (decreased scanner 'me, technician
'me)
• Reduc'on of acquisi'on 'me is usually accomplished by
under-sampling measured data (k-space)
• Limita'ons of Shannon-Nyquist criterion
• Compressed sensing provides a framework to achieve sub-
Nyquist sampling rates with good data fidelity
CS-MRSI: Need for accelera'on
MIRC 47
kx
ky
x
y
3D FT

48. Brain - normal
(N=6)
Brain - cancer
(N=2)
Prostate -cancer
(N=2)
MRSI data Scanner TR(ms) TE(ms) # Averages Grid Size FOV (mm3)
Brain - normal
(N=6)
Siemens 3.0T
Trio Tim
1700 270 4 16 x 16 x 1024 100 x 100 x 15
Brain cancer
(N=2)
Philips 3.0T Achieva 1000
112
112
2
2
18 x 21 x 1024
19 x 22 x 1024
180 x 210 x15
190 x 220 x 15
Prostate cancer
(N=2)
Philips 3.0T Achieva
1200
1000
140
140
1
1
14 x 10 x 1024
16 x 12 x 1024
25 x 50 x 33
20 x 51 x 26
In silico and in vitro phantom studies reported in [6] Geethanath et. al., SPIE Medical Imaging 2010
[7] Geethanath et al., Radiology. 2012
MRSI: acquisi'on parameters
MIRC 48
[7]

49. Applica'on of CS to MRSI
MIRC 49
• Signal model of a free-induc'on decay with N (3 in this case) metabolites
• The sparsely measured Fourier data is represented by y, Object to be es'mated is in
(x, y, f) space is m
• Undersampling in x-y dimensions vs x-f dimension
• Problem defini'on:
• Find the sparsest transform coefficients of m that provides for data consistency between Fourier
coefficients measured and es'mated, at sampled loca'ons
(2)
argminm∥Fu(m)-y∥2
2+λ∥ψ(m)∥1
(3)
(1)

50. Processing So=wares:
[1] jMRUI:
It is a software that can be used to process MRSI data.
The spectra are typically subjected to the following processing steps in jMRUI [5]:
(a) Apodization to remove existing truncation artifacts,
(b) baseline correction,
(c) time-domain Hankel-Lanczos singular value
decomposition filtering of residual water and fat peaks,
(d) Phase Correction,
(e) Frequency Shift.
5*

51. [2] VeSPA:
It is a open source software for MRS applications. It supports four applications:
1. RFPulse (for RF pulse design),
2. Simulation (for spectral simulation),
3. Priorset (for creating simulated MR spectroscopic data)
4. Analysis (for spectral data processing and analysis)
6*
Cr2
3.916
Cho
3.186
Cr
3.03
NAA
2.008
Lipids
0.9-1.4
Gln, Glu, GABA
2.12-2.42
Figure 8: Spectra simulated using VeSPA soVware with major metabolites of brain

52. [7] Geethanath et al., Radiology. 2012
MRSI: in vivo normal brain
1X
NAACr
Cho
5X
MIRC 52

53. [7] Geethanath et al., Radiology. 2012
Brain cancer
1X
2X
5X
10X
Prostate cancer
Normal CancerNormal Cancer
NAACr
ChoNAA
Cr
Cho
Cr2 Cr2 Cr
Cho
Cit
Cit
Cho
+ Cr
CS-MRSI: Cancer results
MIRC 53

54. Brain - cancer
Prostate - cancer
Brain - Normal
Brain - Normal
Brain - cancer
Prostate - cancer
CS-MRSI: Metabolite maps
MIRC 54
[7] Geethanath et al., Radiology. 2012

55. Limita'ons of PRESS
• Conventional slice-selective 180 refocusing pulses do not have particularly good slice profiles, leading to
non-uniform metabolite excitation and signal generation from outside PRESS box
• By definition, it restricts excitation to a rectangular volume, but brain has a curved, elliptical shape –
difficult to obtain signal from cortical regions close to the skull
• 3DPRESS-MRSI sequence - scan-time becomes very long if high spatial resolution in all three directions
is required (number of PE gradients to be recorded becomes very high since encoding is performed in all
three directions, hence giving long scan times)
• Difficulty of obtaining sufficient magnetic field homogeneity for large spatial coverages
[4]*

56. Schizophrenia:
- In a study [7], it is shown that using proton MRSI, in case of patients with schizophrenia, there will be a
relative loss of signal from N-acetyl- containing compounds (NAA)
- Patients with schizophrenia, when compared as a group to normal controls, show a consistent 1H-MRSI
pattern of group differences, i.e., bilateral reductions of NAA/CRE and NAA/CHO in HIPPO and DLPFC;
- 1H-MRSI data in both patients and controls do not show significant changes over a period of 90 days;
however, absolute metabolite ratios in individuals show low predictability over this time interval;
- 1H-MRSI data show relatively low variability (as measured by the coefficients of variation (CVs)) both in
patients and normal controls, especially for NAA/ CRE and CHO/CRE.
7*

57. Mild Cogni've Impairment:
- Mild cognitive impairment (MCI) is a clinical state between normal aging and Alzheimer's disease (AD)
- In a study [8], 1H-MRS findings were compared in the superior temporal lobe, posterior cingulate gyri
and medial occipital lobe among 21 patients with MCI, 21 patients with probable AD, and 63 elderly
controls
- Results showed that, NAA /Cr ratios were significantly lower in AD patients compared to both MCI and
normal control subjects in the left superior temporal and the posterior cingulate VOI
- Myoinositol (MI) /Cr ratios measured from the posterior cingulate VOI were significantly higher in both
MCI and AD patients than controls
- Cho /Cr ratios measured from the posterior cingulate VOI were higher in AD patients compared to both
MCI and control subjects
8*

58. • Increased informa'on content but at cost of increased acquisi'on 'me
• Provides richer insight into the pathophysiology and direct impact on therapeu'c
design
• Mul'ple open-source tools available – jMRUI and Vespa
• Increased clinical research in neuro-,breast, prostate, cardiac (murine) and liver
• Ac've area of research – development of PSD and recon
Summary
MIRC 58

59. References
[1] Nishimura, Dwight George. Principles of magnetic resonance imaging. Stanford University, 1996.
[2] SG Dissertation
[3] Theoretical background: MRI and MRS
[4] Peter B. Barker et al., “In vivo proton MR spectroscopy of the human brain”, Progress in Nuclear Magnetic
Resonance Spectroscopy 49 (2006) 99–128
[5] A. Naressi, et al. Java-based graphical user interface for MRUI, a software package for quantitation of in vivo/medical
magnetic resonance spectroscopy signals. Computers in Biology and Medicine, 31(4), 269-286 (2001)
[6] https://scion.duhs.duke.edu/vespa/
[7] Alessandro Bertolino et al., “Reproducibility of Proton Magnetic Resonance Spectroscopic Imaging in Patients with
Schizophrenia”, Neuropsychopharmacology 1998
[8] K. Kantarci et al., “Regional Metabolic Patterns In Mild Cognitive Impairment And Alzheimer's Disease A 1h Mrs
Study”, Neurology. 2000

60. Acknowledgement
• People
§ Prof. Vikram D. Kodibagkar
§ Prof. Joseph V. Hajnal
§ Colleagues at UT Southwestern
§ Colleagues at ICL
§ Students at MIRC & radiologists at
Sagar Hospital
§ Collaborators from
§ UMN
§ Oxford
§ GE Healthcare (Dr. Ramesh Venkatesan)
§ ASU
§ IISc
§ NIMHANS
§ Philips Healthcare
• Funding
§ Pilot grant (PI: Kodibagkar) from
UL1RR024982, (PI: Milton Packer)
§ ARP#010019-0056-2007 (PI: Kodibagkar)
§ R21CA132096-01A1 (PI: Kodibagkar)
§ W81XWH-05-1-0223 (PI: Kodibagkar)
§ R21 CA139688 (PI: Corum)
§ S10 RR023730 (PI: Garwood)
§ P41 RR008079 (PI: Garwood)
§ MRI India Na'onal Mission grant – SCANERA
(co-PI: Geethanath) from DEITY
§ DST-TSD grant and Wipro GE Healthcare
(PI: Geethanath)
§ KFIST grant (PI: Geethanath)
MIRC 60

61. ?
MIRC 61
sairam.geethanath@dayanandasagar.edu
http://dayanandasagar.edu/index.php/mirc
http://dayanandasagar.edu/index.php/sharing

62. References
• [1] M. Kass et al. Interna'onal Journal of Computer Vision, 1988.
• [2] Hand book of MRI Pulse Sequences, M. A. Bernstein
• [3] M. Grant and S. Boyd disciplined convex programming.
• [4] M. Lus'ng et. al. MRM, 2007
• [5]A. S. Konar et al., Journal of Indian Ins'tute of Science, 2014.
MIRC 62

63. Acknowledgement
• People
§ Dr. Vikram D. Kodibagkar
§ Dr. Joseph V. Hajnal
§ Students at MIRC
§ MRSI project
§ Hyeonman Baek, Ph.D.
§ Ma"hew Lewis, Ph.D.
§ Sandeep K. Ganji, B.
Tech.
§ Yao Ding, M.S.
§ Robert D. Sims, M.D.
§ Changho Choi, Ph.D.
§ Elizabeth Maher, M.D.,
Ph.D.
• Funding
§ Pilot grant (PI: Kodibagkar) from
UL1RR024982, (PI: Milton Packer)
§ ARP#010019-0056-2007 (PI: Kodibagkar)
§ R21CA132096-01A1 (PI: Kodibagkar)
§ W81XWH-05-1-0223 (PI: Kodibagkar)
§ R21 CA139688 (PI: Corum)
§ S10 RR023730 (PI: Garwood)
§ P41 RR008079 (PI: Garwood)
§ MRI India Na'onal Mission grant – SCANERA
(co-PI: Geethanath)
§ DST-TSD grant and Wipro GE Healthcare
(PI: Geethanath)
§ KFIST grant (PI: Geethanath)
§ ROICS project
§ Amaresh Konar,
M.Tech
§ Shashikala, M.Tech
§ Shivaraj, M.Tech
§ Rashmi Rao, B.E.
§ Barjor Gimi, Ph.D.
§ Steen Moeller, Ph.D.
§ Julianna Czum, MD
§ RUSL/Go-Ac've project
§ Amaresh Konar,
M.Tech
§ Pavan Poojar, M.Tech
§ Nutan Dev, B.E.
§ Smera Lingesh, M.Tech
§ Ramesh Venkatesan,
D.Sc
§ Smt. Prema, B.A.
§ Shilpa D. , M.S.
MIRC 63

64. MIRC 64