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Volumes Of Interest Definition

Volumes Of Interest Definition. Mario Quarantelli Biostructure and Bioimaging Institute – CNR Naples - Italy HBM2004 - PVEOut Satellite Meeting Budapest, 12 June 2004. Background. Manual delineation of VOI’s is: Operator-dependent, less reproducible?

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Volumes Of Interest Definition

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  1. Volumes Of Interest Definition Mario Quarantelli Biostructure and Bioimaging Institute – CNR Naples - Italy HBM2004 - PVEOut Satellite Meeting Budapest, 12 June 2004 IBB, 2004

  2. 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) IBB, 2004

  3. 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 IBB, 2004

  4. 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. IBB, 2004

  5. IBB, 2004

  6. 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 IBB, 2004

  7. 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 IBB, 2004

  8. 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 • Defining 1056 small boxes IBB, 2004

  9. 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). IBB, 2004

  10. 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 IBB, 2004

  11. 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 IBB, 2004

  12. #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. IBB, 2004

  13. Representative slices from the segmented MRI study of the validation set with the smallest error (mean error per structure 3 ml).… IBB, 2004

  14. ... and with the largest error (mean error per structure 11.2 ml). IBB, 2004

  15. 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/projects/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 IBB, 2004

  16. AtlasMNI 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 IBB, 2004

  17. ## ## ## ## # # # # # # # # # # # # # # # # ** ** ** ** # # # # ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** * * * * * * * * ## # # # # # ** * * * * ** * IBB, 2004

  18. 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 NRU, 2004

  19. Example of 4 template VOI sets VOI set 1 VOI set 2 VOI set 3 VOI set 4 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) NRU, 2004

  20. Transformations used between “template” and “new subject” spaces Translation Rotation Scaling Shearing Transformed image Image transformation Original image Warping algorithm Deformation field Goal image Affine (12 param.) transformation Woods, JCAT, 1992 Warping (soft) transformation Kjems, IEEE TMI, 1999 NRU, 2004

  21. Transformation of three template MR’s to “new subject space” New Temp.1 Temp.2 Temp.3 New Temp.1 Temp.2 Temp.3 Affine and warp transformation NRU, 2004

  22. 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 NRU, 2004

  23. 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 NRU, 2004

  24. Conclusion NRU, 2004

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