Sandsynligheder og regioner

Volumes Of Interest Definition
Mario Quarantelli
Biostructure and Bioimaging Institute – CNR
Naples - Italy
HBM2004 - PVEOut Satellite Meeting
Budapest, 12 June 2004
PVEOut project, EU
IBB, 2004
Background
• Manual delineation of VOI’s is:
– Operator-dependent, less reproducible?
– very time demanding (up to 8 hours for 37
VOI’s per subject)
– prone to errors
• Ideally a method for VOI definition should
be
– Fully automated
– Accurate (gold standard?) and reproducible
– Capable of working on multiple modalities
• PET (FDG, receptors)
• SPET (CBF, receptors)
• MRI (T1, EPI, segmented)
PVEOut project, EU
IBB, 2004
REQUIREMENTS FOR PVE-C
• VOI’s must be brought in the single
patient space (where resolution is
defined)
• VOI’s must cover the whole brain
• Possibly homogeneous VOI’s should be
defined (tracer distribution)
– Different VOI sets for different tracers
PVEOut project, EU
IBB, 2004
The complete process of digitalized brain atlas
based identification of anatomical structures
requires three different tools:
 A VOI database of 3D brain structures (atlas or
template) in a standardized coordinate system
 A spatial normalization software for the definition
of a correspondence between each individual 3D MRI
data set and a standard space (Talairach, MNI,
others). If we calculate a normalization matrix to
move from the patient space to the standard space,
this matrix will be used backward to superimpose
the template onto the single subject study
A software for applying the labeled VOI's to the
functional images.
PVEOut project, EU
IBB, 2004
SOFTWARE
PERFORMS…
WEB
OPERATOR-
OS
AVAILABILITY
Unix
free
INTERVENTION
AIR 3.08
Study deformation
bishopw.loni.ucla.edu
Automated
onto the Atlas
Convex Hull SN
9 parameters
Win
ric.uthscsa.edu
Automated
Solaris
free
Deformation
www.bic.mni.mcgill.ca
Automated
Unix
Restricted
Atlas/VOI
www.appmed.se
Interactive
Win
Commercial
www.dhbr.neuro.ki.se/Hba
Interactive
Solaris
free
affine transform
MNI ANIMAL
CBA 4
deformation onto the
study
HBA
Study deformation
onto the Atlas/VOI
SPM
Study deformation
onto the Atlas
PVEOut project, EU
Linux
www.fil.ion.ac.uk/spm
Automated
Unix
free
Win
IBB, 2004
ATLAS - Talairach based
Andreasen, NC, Rajarethinam R, Cizadlo T, et al. Automatic Atlas-Based
volume estimation of human brain regions from MR images. J Comput Assist
Tomogr 1996;20:98-10
Quarantelli M , Larobina M, Volpe U, Amati G, Tedeschi E, Ciarmiello A,
Brunetti A, Galderisi S, Alfano B. Stereotaxy-based regional brain volumetry
applied to segmented MRI: validation and results in deficit and nondeficit
schizophrenia. NeuroImage. 2002 Sep;17:373-384
PVEOut project, EU
IBB, 2004
Talairach stereotactic coordinate system is widely
used for inter-subject normalization and localization
of activation sites in nuclear medicine functional
studies.
_______________________
Talairach J et al., 1952. Presse Med 28:605-609
Talairach, J., and Tournoux, P. 1988. Co-planar
stereotaxic atlas of the human brain. Thieme, New
York
PVEOut project, EU
IBB, 2004
Under the assumption of proportionality of normal
brain structures, the proportional grid approach
proposed by Talairach divides the supratentorial brain
into:
•8 axial planes above the AC-PC line
•4 axial planes below the AC-PC line
•4 coronal planes anterior to the AC
•3 coronal planes between AC and PC
•4 coronal planes posterior to the PC
•4 sagittal planes on each side of the midsagittal plane
PVEOut project, EU
Defining 1056 small boxes
IBB, 2004
ATLAS - Talairach based
• Assignment of Talairach boxes was done preliminarily by visual
inspection of the Talairach atlas [Talairach, J.,1988], based on
the labeling of cortical structures therein reported.
The software then:
• Allows for identification of the AC and PC on original axial images
• GM selection
• Segmentation is either
• performed binarily, i.e. each intracranial pixel is labeled as
belonging univocally to GM, WM and CSF
• or segmented maps are binarized (for probabilistic
segmentation, each voxel is zeroed if (pGM+pWM+pCSF)
<50%, remaining voxels are assigned to the most probable
tissue
• Rebinning of GM volume to take care of anisotropic voxels (e.g.
0.94x0.94x4mm).
PVEOut project, EU
IBB, 2004
ATLAS - Talairach based
• Re-alignment of the segmented GM volume to the AC-PC line
• Automated identification of the falx cerebri (FC) for
correction of possible rotation around the Y axis (due to
malpositioning of the head at the time of the MR scan).
• Identification of the boundaries of a box encompassing the
supratentorial brain
• Application of the Talairach proportional grid to the
segmented image set
PVEOut project, EU
IBB, 2004
VALIDATION
10 MR studies have been analyzed twice
using the manual technique and twice using
the automated technique (one month apart)
•Volumetric accuracy
•Specificity
•Reproducibility
PVEOut project, EU
IBB, 2004
N=20
CBL#
FRO
OCC
PAR
TEM#
VENTR
TOTAL (n=120)
Manual
Mean
Max
[ml]
[ml]
0.4
1.3
3.2
13.9
1.5
4.6
4.3
16.2
4.8
12.9
0.4
1.4
2.4
16.2
Automated
Mean
Max
[ml]
[ml]
6.9
14.8
4.9
12.2
1.2
2.6
2.4
8.3
1.9
4.4
0.4
1.6
2.9
14.8
#Difference
in reproducibilities significant at paired t-test after
correction for multiple comparisons.
When pooling all structures together, no differences in the
reproducibilities of the two methods emerged.
PVEOut project, EU
IBB, 2004
Representative slices from the segmented MRI study of the validation
set with the smallest error (mean error per structure 3 ml).…
PVEOut project, EU
IBB, 2004
... and with the largest error (mean error per structure 11.2 ml).
PVEOut project, EU
IBB, 2004
ATLAS - MNI based
• Voxels of the MNI space
belonging to cerebral
lobes, cerebellum, PFC,
Hyppocampus and
Posterior cingulate have
been labeled according to
their MNI coordinates
paralleling the Talairach
Labels database served by
the Talairach Daemon.
http://ric.uthscsa.edu/proj
ects/talairachdaemon.html
– Lancaster JL, Rainey LH,
Summerlin JL, Freitas CS, Fox
PT, Evans AC, Toga AW,
Mazziotta JC. Automated
labeling of the human brainFA
preliminary report on the
development and evaluation of
a forward-transformed method.
Human Brain Mapping
1997;5:238–242
PVEOut project, EU
IBB, 2004
•
•
•
•
•
•
Atlas
MNI based
The MNI atlas module only works if SPM is
installed on the same PC.
PVELab will automatically invoke the SPM
normalization tools, needed to measure the
normalization matrix, which will be used to
assign each GM voxel of the subject to the
corresponding structure
Currently it only uses affine transformation
parameters
Normalization is done using segmented GM and
GM prior
Template is made of binary volumes in analyze
format, with a simple ascii file coupling each
structure to a #
Validation is ongoing
PVEOut project, EU
IBB, 2004
PVEOut project, EU
IBB, 2004
Idea of proposed method
• Multiple sets of “Regions of Interest” (VOI's) is available
in different template spaces.
– These have manually been delineated at high resolution MR
scans (preregistered to the AC-PC line) for a number of template
subjects and afterwards carefully been checked for errors
• Multiple template VOI sets is automatically transferred
from “template spaces” to “new subject space”
• By combining multiple transferred VOI sets it is possible
to limit some of the variation coming from delineation
and identification of transformation parameters
NCI-MCI project, EU
NRU, 2004
Example of 4 template VOI sets
VOI set 4
VOI set 3
VOI set 2
VOI set 1
20 VOI sets (37 VOI’s) have manually been delineated at high resolution structural MR
images (2x2x2 mm voxels) for 10 healthy controls and 10 MCI patients (Karine
Madsen and Steen Hasselbalch, NRU)
NCI-MCI project, EU
NRU, 2004
Warping (soft)
transformation
Affine (12 param.)
transformation
Transformations used between
“template” and “new subject” spaces
Translation
Rotation
Scaling
Shearing
Woods, JCAT, 1992
Transformed
image
Original
image
Goal
image
Image
transformation
Warping
algorithm
Deformation
field
Kjems, IEEE TMI, 1999
NCI-MCI project, EU
NRU, 2004
New
Transformation of three template MR’s
to “new subject space”
New
Temp.3
Temp.2
Temp.1
Temp.3
Temp.2
Temp.1
Affine and warp
transformation
NCI-MCI project, EU
NRU, 2004
Transformation of VOI’s and generation
of probability maps for the VOI’s
• Applying the identified transformation to the VOI’s
defined in “template space” multiple sets of VOI’s are
available in “new subject space”
• A probability map for voxel’s being included in the
final VOI set is individually created for each VOI.
• Proposed method:
– for each template VOI set transformed the probability being
in the VOI is 1 for voxels inside the VOI and 0 outside
– create a common probability map by averaging the
probability maps generated in “new subject space”
– threshold the probability map so the volume of the created
VOI’s are equal to the mean of the transformed template
VOI’s
NCI-MCI project, EU
NRU, 2004
Example of probability MAP for some VOI’s
• Upper panel: Probability map for cerebellum
• Lower panel: Probability map for sensory motor cortex and
parietal cortex
• As expected voxels in the middle of the VOI’s have the highest
probability while more exterior voxels have lower probabilities
NCI-MCI project, EU
NRU, 2004
Conclusion
NCI-MCI project, EU
NRU, 2004