Some Aspects in Medical Imaging

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15 Νοε 2013 (πριν από 3 χρόνια και 6 μήνες)

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Some Aspects in Medical Imaging

Debasis Mitra

Computer Science

Florida Institute of Technology


Acknowledgement:

Grant T. Gullberg

Radiotracer Department

Life Sciences Division

Lawrence Berkeley National Lab

&

Unknown sources from the Web

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Co
-
ordinates

o
Why this talk?


o
Where am I now?


o
What does this lab do?


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Lawrence Berkeley National Lab

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Center for Functional Imaging

Biomedical Imaging is the
Engineering behind Radiology




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Types of Imaging Instruments



Computer Tomography (X
-
ray)



Magnetic Resonance Imaging (MRI)



Single Photon Emission Computed Tomography
(SPECT): gamma ray of 100
-
few hundred kev



Positron Emission Tomography (PET): gamma ray from
in situ positron annihilation, 500 kev



Ultra Sound



Optical or Laser Tomography (Infrared)



Fluoroscopy, Opto
-
acoustic, Electron, Atomic
-
force,
Radio
-
frequency,…

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CT

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GE VG3 Millennium Hawkeye
SPECT/CT


-
ray detectors

collimators

Resolution

Sensitivity

Acquisition

system

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Scintillation Camera and
Collimator

Patient

Collimator localizes events in object and determines sensitivity and spatial resolution
of the camera

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parallel hole

converging

pinhole

Collimator

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Positron Emission
Tomography Does Not
Need a Collimator

Positron annihilates with electron



two gamma photons each at
511 keV leave under 180


Coincidence detection (“electronic
collimation”)

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PET

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MRI

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Brain tumor

Epilepsy: MRI, PET
-
time 1, 2

Fiber Tracking
of DTMRI Data

A

B

C

D

E

F

Rohmer D, Sitek A, Gullberg GT:
Reconstruction and visualization of
fiber and laminar structure in the
normal human heart from ex vivo
DTMRI data.
Investigative Radiology
,
42:777
-
789, 2007.


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Ultrasound

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CardiARC

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Clinical Feasibility

Results

Conventional

Spectrum Dynamics

1.45 Mcounts total (heart 10%, backgnd 90%)

Pixel size 6.91 mm
×

6.91 mm

Iterative reconstruction

Total acquisition time: 17.5 min

0.8 Mcounts total (heart 60%, backgnd 40%)

Pixel size 2.46 mm
×

6.91 mm
×

6.91 mm

Iterative reconstruction

Total acquisition time: 2.2 min

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Radiopharmaceuticals

for

Cardiac Imaging

201
Tl

99m
Tc
-
sestamibi (2
-
Meth0xy
-
2
-
methylpropyl
isonitrile)

99m
Tc
-
tetrafosmin

99m
Tc
-
teboroxime

123
I
-
iodorotenone

123
I
-
BMIPP (fatty acid)

123
I
-
IPPA (fatty acid)

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Targets of Study



Heart,


Lungs, liver, other organs in torso




Brain:
Alzheimer’s Disease Neuroimaging Initiative (ADNI)




Breast




Tumor




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Breast Cancer

Physics behind Models



Emission tomography
: SPECT, PET, MRI




Transmission tomography
: X
-
ray, Optical




Reflection
: Ultra Sound, Total Internal

Reflection Fluoroscopy (TIRF for single cell

visualization)




Scattering
:
Muon

tomography?




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Mathematical Problem
Formulation

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Forward Problem (modeling): How the data would look like

given probe and the model



D = F(M): Forward project



An implementation is a Simulation software




Inverse Problem (tomography): What the model would be

given the probe and data


M = F
~

(D): back
-
project



An implementation is a Reconstruction software



Noise in data makes it a hard statistical problem



Data volumemay be additional computational



challenge



http://en.wikipedia.org/wiki/Inverse_problem


Reconstruction Algorithms

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Analytic
-
inverse: E.g., Radon transformation for

emission/absorption (mostly useless except for theoretical

purpose)




Algebraic Reconstruction: voxel by voxel reconstruct the model




Iterative Reconstruction using Expectation Maximization




Ordered Set


EM




Maximum A Posteriori (MAP
-
EM)




Penalized Least Square (PLS): 1.5 iteration!

Dynamic Imaging

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Problem: Objects move during data gathering



Question: How to reconstruct (1) Object, (2) Motion




A successful approach: Level Set




For blood concentration change in tissues:


Temporal B
-
spline




Tensor imaging with MRI

Fit the
123
I
-
BMIPP Data to a
Compartment Model

Need to estimate an input
function.

Time activity curves have to be
estimated directly from the
projections.

A methyl group on the


position
of the carbon chain limits the
oxidation of
123
I
-
BMIPP.


Differs from
123
IPPA which is


completely metabolized to


benzoic acid.

Model of IPPA


Metabolism

TG

Benzoic acid

IPPA

k
21

k
3
2

k
23

k
12

bloo
d

C
2
(t)

C
3
(t)

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Spatiotemporal Modeling Using A Small
Number of
Splines

to Represent Realistic
Physiological Curves


Zero Order (voxels) B
-
Spline Spatial Bases


Quadratic B
-
Spline Temporal Basis Functions

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0
o

8
o

16
o

24
o

32
o

40
o

48
o

56
o

64
o

72
o

80
o

88
o

96
o

104
o

112
o

120
o

128
o

136
o

352
o

1 sec frames, 180˚ rotation of one head

Recirculation time is 6
-
8 seconds

Slow
-
Rotation Dynamic Pinhole
SPECT

0
10
20
30
40
50
60
70
80
90
0
500
1000
1500
Time(Seconds)
Relative activity
Blood Time Activity Curve
Estimated from Projections Using
Factor Analysis

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Results


Dynamic Early Data

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Image Spatial Representations

Pixels / voxels


Blobs

Linear B
-
splines

Cubic B
-
splines

“Custom
-
made” shapes

Irregular meshes





. . . . . . . . . . . . . .

regular

sparse

Metabolic Rate of BMIPP

Normal

SHR

Ki=0.15 min
-
1

Ki=0.40 min
-
1

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Metabolic Rate:
FDG
vs

BMIPP

FDG

BMIPP

Ki=0.15 min
-
1

Ki=0.40 min
-
1





32
12
32
21
k
k
k
k
Ki


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SHR: hypertensive rat model
(genetically modified)

WKY: normal rat

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Flow rate changes

SHR: Hypertensive, WKY: normal

SHR

SHR

WKY

WKY

Age
(months)

A

(min
-
1
)

B

(min
-
1
)

A

(min
-
1
)

B

(min
-
1
)

7

0.94

1.44

14

21

0.22

0.60

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For
war
d

War
ping

0.9
0

0.0
0

0.6
0

0.3
0

A

B

C

SHR

WKY

1
st

PS

FS

6/18/20
03

8/06/20
03

10/01/2
003

7/14/20
04

12/2/20
03

9/21/20
04

0.6
0

0.0
0

1.2
5

0.8
5

6/18/20
03

8/06/20
03

10/01/2
003

7/14/20
04

12/2/20
03

9/21/20
04

6/18/20
03

8/06/20
03

10/01/2
003

4/27/20
04

0.8
5

1.2
5

0.0
0

0.6
0

6/18/20
03

8/06/20
03

10/01/2
003

4/27/20
04

SHR red

A

Normal

B

C

D

SHR red

Normal

septum

anterior wall

Temporal Comparison of 1
st

Principal
Strain for SHR and WKY

Veress A et al.: Regional changes in the diastolic
deformation of the left ventricle for SHR and WKY rats
using
18
FDG based microPET technology and
hyperelastic warping. Annals of Biomedical
Engineering 36:1104

1117, 2008.


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Summed Images (between 2 and 12 min)

Parametric Images of k
21

Parametric Imaging


Sitek A, Di Bella EVR, Gullberg GT, Huesman
RH: Removal of liver activity contamination in
teboroxime dynamic cardiac SPECT imaging
using factor analysis.
J Nucl Cardiology

9:197
-
205, 2002.

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SUMMARY


The SHR shows increased glucose metabolism
and reduced fatty acid metabolism
.


The reverse is true for the
nomotensive

WKY rat.


The SHR model is used to develop techniques for
analysis of imaging data of heart failure related to
metabolism.


Molecular Insight Pharmaceuticals is now
evaluating
123
I
-
BMIPP in clinical trials.


These results of fatty acid metabolism correlate
with those in humans with hypertensive left
ventricular hypertrophy.

(de
las

Fuentes et al. J
Nucl

Cardiol

13:369,
2006)

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COMMENTS


The SHR has a defective gene (CD36) on chromosome 4.


The defect is associated with compromised long
-
chain FA
transport across the cell membrane.


The defect causes insulin resistance, alteration in basal
glucose metabolism.


Short
-
chain FA diet decreases glucose uptake, alleviates
hypertrophy, but hypertension is not improved.


Proposed research will compare
123
I
-
BMIPP with
18
FTHA.

Hajri T et al.: Defective fatty acid uptake in the spontaneously hypertensive rat is a
primary determinant of altered glucose metabolism, hyperinsulinemia, and
myocardial hypertrophy. J Biological Chem 276:23661
-
23666, 2001.

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MRI is way advanced in

Dynamic Imaging

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Diffusion Tensor Imaging

A high
-
resolution diffusion tensor imaging scan

reveals differences between healthy tracts of axons,

at left and in the lower enlargement, and tracts of

injured axons, at right and in the top enlargement,

in a person who sustained a moderate to severe

traumatic brain injury. Such damage has been shown

to correlate with cognitive impairment.

(Image courtesy of Dr. Deborah Little)

Diffusion Tensor

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Diffusion within a single
voxel
.
(a)

Diagram shows the 3D diffusion probability density function
in a
voxel

that contains spherical cells (top left) or randomly oriented tubular structures that
intersect, such as axons (bottom left). This 3D displacement distribution, which is roughly bell
shaped, results in a symmetric image (center), as there is no preferential direction of
diffusion. The distribution is similar to that in unrestricted diffusion but narrower because there
are barriers that hinder molecular displacement. The center of the image (origin of the
r

vector) codes for the proportion of molecules that were not displaced during the diffusion time
interval.

Sheet Tracking
of DTMRI Data

A

B

C

D

E

F

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Fiber Tracking of Right and Left
Ventricle

A

B

Cardiac Band Hypothesis: The four chamber heart is built
from a single continuous band of muscle.


Torrent
-
Guasp F, Kocicab MJ, Cornoc AF, et al.
Towards new
understanding of the heart structure and function. Eur J Cardiothorac
Surg. 2005;27:191
-
201.

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Advancement of Data Acquisition
Technology

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List mode
: acquire data for recording time for each

track and reconstruct with it: a computational challenge




Time
-
of
-
flight
: Acquire event versus data collecting

time: new type of detectors needed




Compton gamma camera
: provides some measure of

angle of a track




Newer Technology:
Opto
-
acoustic, Fluorescence, …




Target
-
specific detectors
: e.g., Cardiac
-
Spect
, faster

and cleaner data with higher resolution

Molecular Imaging

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Medical imaging is primarily at organ
-
level




With more genetic information available today it is

usual to think in terms of metabolism behind images,

and target
cellular
-
level processes




Current focus is to develop
ligands

that are

(1)
tagged with imaging agents, (2) binds to some

protein or metabolite
that we want to visualize with
imaging



Understanding
dynamic

organ
-
level images from

metabolic point of view is another new area

Total Internal Reflection Imaging

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TIRF imaging of actin networks and their

reorganization in the cortex of
Dictyostelium

cells.

Auto
-
diagnosis/prognosis:

Machine learning

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Images are still used by radiologists for

diagnosis/prognosis, or by biologist for doing science:

technology targets exclusively to improve


image quality, and nothing more






It is quite possible to use
machine learning
algorithms

to help the process:

image is input,
zones of interest with annotations
are output

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Thanks!



Debasis Mitra

dmitra@cs.fit.edu