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PET-MRI Tools: A program package
for the analysis of dynamic PET studies
registered to anatomical images
University of Debrecen,
Medical and Health Science Centre
Department of Nuclear Medicine
József Varga
July 2007
PET-MRI Tools - July 2007 J. Varga 2
Purposes:
• Originally: PARTIAL VOLUME CORRECTION
in the calculation of quantitative parameters derived from (static
and dynamic) PET studies of the:
- brain (serotonine receptors)
- kidneys (angiotensine receptors)
Other aims:
• General processing with anatomical VOIs
(volumes of interest)
• Kinetic analysis (curves and parametric images)
• Use of some brain atlas for automatic region definitions
• 3D statistics (with SPM)
PET-MRI Tools - July 2007 J. Varga 3
Requirements:
• Easy integration of GPL (public) programs
and earlier packages of our own
– C/C++ source codes
– MatLab modules
– Executables
• Handling multiple medical file formats
• Easy development and testing of new and adapted
algorithms
– Must support both scripting and C/C++
• Convenient interface for non-programmers
PET-MRI Tools - July 2007 J. Varga 4
Principles of PET-MRI Tools
• Platform: primarily PC with Windows
• Bilingual environment
(C++ and MatLab)
• Modular structure, with MINC
files as connection points
• Flexible
• Simple user interface
generally available
fast development + fast execution
exchangable moduls
to support development
for physician users
PET-MRI Tools - July 2007 J. Varga 5
PROGRAMMING ENVIRONMENT
Operating system: MS Windows 9X/NT/2000/XP
Hardware: PC
MatL
ab
MS
Vis
ual
Stu
dio
Standalone executables
C/C++ modules
MatLab modules
Matlab programs
MEX files (dll-s)
PET-MRI Tools - July 2007 J. Varga 6
USER INTERFACE / 1:
Interface generator for functions
function [lh,tacs]=tac( image_file, voi_file, tac_file, makeplot,...
voilevel, weighted )
% Generates time-activity curves, and saves them to file
%#!1:Image file;infile;*.mnc
%#!2:VOI file;infile;*.voi
%#3:New TAC file name;outfile;*.tac
%#4:Show graphs?;check;1
%#5:Threshold if using VOI masks;num;0.005;1;0.5
%#6:Weight with probabilities?;check;1
%#>2
if ( nargin<2 )
[lh,tacs]=runfn('tac');
return;
end
function [lh,tacs]=tac( image_file, voi_file, tac_file, makeplot,...
voilevel, weighted )
% Generates time-activity curves, and saves them to file
PET-MRI Tools - July 2007 J. Varga 7
USER INTERFACE / 2:
Menu
% Menu structure for curve processing
% Jozsef Varga, Cyric, Sendai, 2001
Curve processing
>&Conversion
>>Conversion &planner,convplan
>>&Set to MINC,set2minc
>>&Analyze to MINC,anal2minc
>>&InterFile to MINC,dynif2minc
>>S&um dynamic series,start_volsum
>&Display
>>&Show series,minc_show_caller
...
>&Curves
>>&TAC creation,tac_caller
>>Create from &C/S,cs_tac_caller
>>&Read from any file,loadcurves_caller
>>Read from TAC &file,readcurves_caller
PET-MRI Tools - July 2007 J. Varga 8
USER INTERFACE / 3:
Procedure Control
% lungtu.proc
%#!1:SPECT file name in DIAG;infile;*.kv*
%#!2:New file name;outfile;m:\tudo\*.mnc
%#>0
1:Convert;diag2minc;#0,1#;#0,2#;1;0
2:Create coronal;reslice_minc;#0,2#;coronal;[changeext('#0,2#','C.mnc')];3;;
3:Draw VOI;drawvoi_call;#2,3#;;[changeext('#0,2#','C.voi')]
4:Create isocount VOI;autiso_rel_cs;#0,2#;#3,3#;;[changeext('#0,2#','_tu.voi')]
5:Check VOI;drawvoi_call;#0,2#;;#4,4#;#4,4#
6:Quantitate;tuq;#0,2#;#4,4#
PET-MRI Tools - July 2007 J. Varga 9
USER INTERFACE / 4:
Batch processing
• Repeated calling of the same procedure for a
list of studies
• Automatic mode of procedure control:
Steps labeled as non-essential are skipped
(e.g. visual inspection and possibility for manual
corrections)
PET-MRI Tools - July 2007 J. Varga 10
ELEMENTS:
Conversion from scanner (PET, MRI) formats
to MINC
GE4096 GEAdv InterFile DICOM Signa4 Signa5
(SPECT) (CT)
Functional MINC
Structural MINC
A single MINC file contains all the information about a study
Shimadzu3
(DIAG)
Micro-CT Micro-PET
PET-MRI Tools - July 2007 J. Varga 11
ELEMENTS: Image display modes
• Multi-image
(„montage”)
• Fused
• 2 image sets
+ VOIs
x
PET-MRI Tools - July 2007 J. Varga 12
ELEMENTS: 3D browser modes
• From single file
• From fused files
PET-MRI Tools - July 2007 J. Varga 13
ELEMENTS for processing MRI
Automatic brain extraction,
Segmentation (3 methods),
Tissue labeling
Gray segment in PET geometry
Tissue labels
PET-MRI Tools - July 2007 J. Varga 14
Membership functions (0 P 1)
CSF gray white
AFCM
CUA
PET-MRI Tools - July 2007 J. Varga 15
Segments: slice #33
AFCM CUA 3D
More gray in the cerebellum
Original MRI
Both label sets fused
PET-MRI Tools - July 2007 J. Varga 16
Gray segment in
the PET geometry,
before and after
convolution with
PET PSF
PET-MRI Tools - July 2007 J. Varga 17
Comparison of MRI brain segmentation
methods for the PVC of coregistered PET
• Simulated MRI images
• Four combinations of added noise and inhomogeneities
• Three theoretically different segmentation methods:
– CUA: Gaussian components, ML
(Wang, Y., 1995)
– AFCM: „Adaptive Fuzzy C-means”
(Pham D. L., Prince J. L., 1999)
– SPM segmentation: template+affine transformation
(Ashburner J, Friston KJ, 1997)
• Comparison in PET geometry
Varga J., Pham D.L., Wang Y. & al.: Comparison of MRI brain segmentation methods for the
partial volume correction of coregistered PET. Eur. J. Nucl. Med. 29: S157, 2002.
PET-MRI Tools - July 2007 J. Varga 18
Conclusions:
• Fuzzy segmentation should be preferred
• The local accuracy of the applied template-
based method was questionable, making it less
appropriate for PVC of the brain cortex.
• AFCM performs better at low noise, but is more
sensitive to noise than the CUA method
• Our method of comparing convolved rather than
high-resolution segments is more realistic when
the application of routine (suboptimal) MRI for
the PVC of emission tomograms is considered.
PET-MRI Tools - July 2007 J. Varga 19
Elements for processing PET Brain extraction,
3D volumes of interest
Time-activity curves
Graphical analysis
PET-MRI Tools - July 2007 J. Varga 20
„Graphical” analysis
Input data?
Arterial curve Reference area
Irreversible:
Patlak
Reversible:
Logan
Linear: Logan
Bilinear: Ichise
Non-linear: SRTM,
Lammertsma-Hume
PET-MRI Tools - July 2007 J. Varga 21
Elements: Coregistration and fusion
(baboon study shown)
PET-MRI fusion (AIR)
SERT distribution volume (Logan) parametric image fused with MRI
Control
Suppressed
MDMA
PET-MRI Tools - July 2007 J. Varga 23
Elements: Volume operation tools,
including partial volume correction
Raw image
Corrected image (3 comp.)
([C-11] McN study of Parkinson-dis. patient)
PET-MRI Tools - July 2007 J. Varga 24
4D PET MINC
MRI MINC
Coregistration (minc_air) Volume drawing
(polygon <-> mask)
Curves
Quant.
Kinetic anal. Graphical anal. (Patlak, Logan, Ichise)
3D
4D
Param. image (Logan)
PVC (pvc3; pvc_r)
Summation (vol_sum)
PET-MRI Tools - July 2007 J. Varga 25
What is partial volume effect?
original
degraded
Hot objects of size
smaller than or close
to system resolution
seem to be less
active.
PET-MRI Tools - July 2007 J. Varga 26
PVE and spillover
• Partial volume effect: a hot object is smaller than the
resolution volume
it seems less active
• Spillover: warm environment increases the counts of
a small colder object
PET-MRI Tools - July 2007 J. Varga 27
Mathematical formulation
FOV
o rdrrPSFrIrI ),()()(
where: Io observed image
I (true) activity distribution
PSF point spread function
PSFIIo
Short notation (convolution):
PET-MRI Tools - July 2007 J. Varga 28
Voxel-by-voxel PVC method implemented
(3 compartments):
PET:
Summation (vol_sum)
MRI:
Brain extraction (bet)
Segmentation Coregistration (minc_air)
PVC3 (corr_spillover)
Spreading (PETconvolve)
Reslice segments (tissue_masks)
PET-MRI Tools - July 2007 J. Varga 29
Example: Partial volume correction of
serotonine transporter PET
Uncorrected
Corrected
(2 tissue compartments)
PET-MRI Tools - July 2007 J. Varga 30
General model (Rousset):
N
kD
k
N
kD
k
k
k
k
k
r)drPSF(r,TI(r)
r)dr)PSF(r,r(TI(r)
T
D
1
kk
1
:Dover constant is Teach if
: ionsconcentratactivity with true
, domains :components tissueN Supposing
(Rousset O.G. et al., J Nucl Med, 1998)
PET-MRI Tools - July 2007 J. Varga 31
Calculation for VOI-s
i k
i k
VOI Dpix
ik
N
k
kik
N
kVOI D
k
ipix
ii
drr)drPSF(r,n
wTw
drrdrrPSFTn
tVOI
1 with t
:separation
),(1
t
:in conc. observedmean with
regions, tonsobservatio gRestrictin
1
i
1;
i
geometric transfer matrix
PET-MRI Tools - July 2007 J. Varga 32
Conditions of the general model:
• Each domain is homogeneous
(true activity concentration is constant inside)
• The domains cover the volumes of interest (VOIs)
AND their neighborhood
PET-MRI Tools - July 2007 J. Varga 33
MIXED MODEL, Stage 1: Automatic segmentation combined with
manual VOIs
• Automatic segmentation
G (gray), W (white) and CSF
(together they cover the whole brain)
• Manual VOIs on transversal, coronal and/or sagittal
slice sets
• Using the rest of the tissue compartments (outside all
manual VOIs) as additional domains for the
calculations
PET-MRI Tools - July 2007 J. Varga 34
Stage 1: Steps of the calculations
• Calculating the representations of the coronal and sagittal VOIs
in the transversal slices
• Subtracting the union of the manual VOIs from the tissue
segments
G0, W0, CSF0 (rest of the segments)
• Application of the general model to the manual VOIs
AND G0, W0, CSF0
(together they cover the whole brain)
PET-MRI Tools - July 2007 J. Varga 35
Stage 2: Template-based VOIs
• Two associated templates are necessary:
– An „atlas”: set of VOI templates
– An MRI slice set that the „atlas” refers to
• E.g.: ICBM template
PET-MRI Tools - July 2007 J. Varga 36
Stage 2: Steps of the procedure
• Registration (calculation of the spatial transformation) of the MRI
template to the patient’s MRI
E.g.: AIR5, warping with 5th order polynomials
• Application of the transformation to the VOI templates (so that they
fit to the patient’s MRI)
• Coregistration of the patient’s MRI to PET
AIR, linear
• Transformation of the VOIs to the PET geometry
• Application of the template-based OR mixed PVC model.
PET-MRI Tools - July 2007 J. Varga 37
Patient’s
MRI MRI template
MRIICBM registration: 1. step: affine (12 pm.)
2. step: 5th order nonlin. (168 pm.)
Patient’s
4D PET Labels
Labels / VOIs in
PET geometry
Automatic processing of brain PET studies:
Summed
(3D) PET
Rigid
coregistration
ICBM
Combined PETICBM
transform
Extracted
brain
Parametric
images
Regional
parameters
Time-act.
curves
PV-corrected
4D PET
PV-corrected
param. images
PV-corrected
curves
PET-MRI Tools - July 2007 J. Varga 38
Patient’s
MRI MRI template
Registration:
1. step: affine (12 pm.)
2. step: 5th order nonlin. (168 pm.)
Patient’s
4D PET Labels
PET / param. images
in ICBM geometry SPM
3D comparison of brain PET studies:
Summed
(3D) PET
Rigid
coregistration
ICBM
Combined PETICBM
transform
Extracted
brain
Parametric
images
PET-MRI Tools - July 2007 J. Varga 39
Patient’s
SPECT
SPECT
template
Coregistration: 1. step: scaled rigid (7 pm.)
2. step: affine (12 pm.)
Patient’s
dynamic planar
Hemispherical CBF (from Patlak plot)
Normalisation
Labels
Brain in standard
geometry Averaging (whole brain)
SPM
3D comparison of brain SPECT studies: