Transcript Some Aspects in Medical Imaging Debasis Mitra Computer Science Florida Institute of Technology Acknowledgement: Grant T.

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
oWhy this talk?
oWhere am I now?
oWhat does this lab do?
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Lawrence Berkeley National Lab
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Center for Functional Imaging
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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
collimators
-ray detectors
Resolution
Sensitivity
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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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Collimator
converging
parallel hole
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pinhole
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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
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Coincidence detection (“electronic
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collimation”)
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PET
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MRI
Epilepsy: MRI, PET-time 1, 2
Brain tumor
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A
B
Fiber Tracking
of DTMRI Data
C
D
E
Rohmer D, Sitek A, Gullberg GT:
Reconstruction and visualization of
fiber and laminar structure in the
normal human heart from ex vivo
DTMRI
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42:777-789, 2007.
F
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Ultrasound
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CardiARC
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Clinical Feasibility
Results
Spectrum Dynamics
Conventional
1.45 Mcounts total (heart 10%, backgnd 90%)
Pixel size 6.91 mm × 6.91 mm
Iterative reconstruction
Total acquisition time: 17.5 min
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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
201Tl
99mTc-sestamibi
(2-Meth0xy-2-methylpropyl
isonitrile)
99mTc-tetrafosmin
99mTc-teboroxime
123I-iodorotenone
123I-BMIPP
123I-IPPA
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(fatty acid)
(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
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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
• 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
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Reconstruction Algorithms
• 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!
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Dynamic Imaging
• 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
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Fit the 123I-BMIPP Data to a
Compartment Model
Need to estimate an input
function.
bloo
Time activity curves have to be d
estimated directly from the
projections.
A methyl group on the  position
of the carbon chain limits the
oxidation of 123I-BMIPP.
IPPA
123
Differs from IPPA which is
completely metabolized to
benzoic acid.
k21
k3
C2(t)
k12
2
C3(t)
k23
TG
Model of IPPA
Metabolism
Benzoic acid
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Spatiotemporal Modeling Using A Small
Number of Splines to Represent Realistic
Physiological Curves
–Quadratic B-Spline Temporal Basis Functions
–Zero Order (voxels) B-Spline Spatial Bases
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Slow-Rotation Dynamic Pinhole
SPECT
0o
72o
8o
16o
24o
32o
80o
88o
96o
104o
Blood
Curve
40o Time Activity
48o
56o
Estimated from Projections Using
Factor Analysis
112o
120o
128o
64o
136o
1500
Relative activity
1000
500
352o
0
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0
1 sec frames, 180˚ rotation of one head
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Recirculation time is 6-8 seconds
10
20
30
40
50
Time(Seconds)
60
70
80
90
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Results — Dynamic Early Data
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Image Spatial Representations
Pixels / voxels
regular
Blobs
Linear B-splines
Cubic B-splines
sparse
“Custom-made” shapes
Irregular meshes
..............
Metabolic Rate of BMIPP
Normal
Ki=0.40 min-1
SHR
Ki=0.15 min-1
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Metabolic Rate:
FDG vs BMIPP
BMIPP
FDG
Ki=0.40 min-1
Ki=0.15 min-1
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Ki  k21k32  k12  k32 
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SHR: hypertensive rat model
(genetically modified)
WKY: normal rat
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Flow rate changes
SHR: Hypertensive, WKY: normal
Age
(months)
7
SHR
SHR
WKY
WKY
A
(min-1)
0.94
B
(min-1)
A
(min-1)
1.44
B
(min-1)
14
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0.22
0.60
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Temporal Comparison of 1st Principal
Strain for SHR and WKY
anterior wall
A
septum
A
SHR red
0.6
0
SHR
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B
03
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C
03
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Normal
03
003
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SHR
red
03
003
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03
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04
0.6
0
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Normal
03
003
1.2
5
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03
1.2 04
0.8
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04
5
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03
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03
10/01/2
003
0.6
0
0.3
0
0.0
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04
0.8
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04
0.9
0
0.0
1st PS 0
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04
WKY
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D
03
C
B
FS
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0.0
0
For
war
d
War
ping
Veress A et al.: Regional changes in the diastolic
deformation of the left ventricle for SHR and WKY rats
using 18FDG based microPET technology and
hyperelastic warping. Annals of Biomedical
Engineering 36:1104–1117, 2008.
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Parametric Imaging
Summed Images (between 2 and 12 min)
Parametric Images of k21
Sitek A, Di Bella EVR, Gullberg GT, Huesman
RH: Removal of liver activity contamination in
teboroxime dynamic cardiac SPECT imaging
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using
factor analysis. J Nucl Cardiology 9:197205, 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 123I-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 123I-BMIPP with 18FTHA.
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
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)
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Diffusion Tensor
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.
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A
B
Sheet Tracking
of DTMRI Data
C
E
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D
F
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Fiber Tracking of Right and Left
Ventricle
B
A
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
• 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
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Molecular Imaging
• 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
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Total Internal Reflection Imaging
TIRF imaging of actin networks and their
reorganization in the cortex of Dictyostelium cells.
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Auto-diagnosis/prognosis:
Machine learning
• 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
[email protected]
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