pet-mri tools (v.0707)

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)


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


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


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)


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


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


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#


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)


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


ELEMENTS: Image display modes

• Multi-image

(„montage”)

• Fused

• 2 image sets

+ VOIs

x


ELEMENTS: 3D browser modes

• From single file

• From fused files


ELEMENTS for processing MRI

Automatic brain extraction,

Segmentation (3 methods),

Tissue labeling

Gray segment in PET geometry

Tissue labels


Membership functions (0 P 1)

CSF gray white

AFCM

CUA


Segments: slice #33

AFCM CUA 3D

More gray in the cerebellum

Original MRI

Both label sets fused


Gray segment in

the PET geometry,

before and after

convolution with

PET PSF


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.


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.


Elements for processing PET Brain extraction,

3D volumes of interest

Time-activity curves

Graphical analysis


„Graphical” analysis

Input data?

Arterial curve Reference area

Irreversible:

Patlak

Reversible:

Logan

Linear: Logan

Bilinear: Ichise

Non-linear: SRTM,

Lammertsma-Hume


Elements: Coregistration and fusion

(baboon study shown)

PET-MRI fusion (AIR)

SERT distribution volume (Logan) parametric image fused with MRI

Control

Suppressed

MDMA


Elements: Volume operation tools,

including partial volume correction

Raw image

Corrected image (3 comp.)

([C-11] McN study of Parkinson-dis. patient)


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)


What is partial volume effect?

original

degraded

Hot objects of size

smaller than or close

to system resolution

seem to be less

active.


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


Mathematical formulation

FOV

o rdrrPSFrIrI ),()()(

where: Io observed image

I (true) activity distribution

PSF point spread function

PSFIIo

Short notation (convolution):


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)


Example: Partial volume correction of

serotonine transporter PET

Uncorrected

Corrected

(2 tissue compartments)


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)


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


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


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


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)


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


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.


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


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


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:

pet-mri tools (v.0707)

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