NA-MIC National Alliance for Medical Image Computing UNC/Utah-II Core 1 Guido Gerig, Casey Goodlett, Marcel Prastawa, Sylvain Gouttard

National Alliance for Medical Image Computing UNC/Utah-II Contributions Method’s Development: DTI: –Population-based DTI Analysis –Quantitative analysis of populations of fiber tracts –Guidelines for DWI acquisition/processing (statistical theory and experiment) –Application to Core-3 clinical studies Segmentation of pathology: –Automatic segmentation and characterization of brain lesions Driving biological problems: White matter integrity of selected fiber tracts in SZ (DBP PNL) Lesion analysis in Lupus (DBP Mind Inst.) Training/education Core5: DTI Workshop HBM Chicago MICCAI’07 Tutorial DTI Analysis DTI tractography-validation workshop Santa Fe / SLC

National Alliance for Medical Image Computing Population-based DTI Analysis Atlas based population studies: Remove shape differences to estimate diffusion statistics –Diffeomorphic group-wise registration –Tensor field operations –Population mean tensor field –Fiber tracts from atlas Application: From sets of DTI to group statistics Atlas coordinate frame I1I1 I5I5 I2I2 I3I3 I4I4 [Goodlett et al 2006] [Joshi et al 2004]

National Alliance for Medical Image Computing Quantitative analysis of populations of fiber tracts Extension of tract-oriented statistics to populations Fiber bundle: 1-D function of arc-length Towards functional data analysis Applied to PNL clinical SZ study (12CNTL, 14 SZ) PNL-II SZ study with 37 subjects MIND/mBIRN DTI validation (17 images)

National Alliance for Medical Image Computing Quantification of measurement error in DTI: Theoretical predictions and validation Casey Goodlett & Tom Fletcher, ISMRM’07, MICCAI’07 Evaluation of gradient direction schemes and tensor estimation routines: Maximum accuracy and precision of tensor derived measures Result: Recommendations for optimal imaging protocols Red – aligned Green - unaligned

National Alliance for Medical Image Computing Segmentation of lesions in brain MRI Marcel Prastawa, Utah Segmentation of pathology important challenge Advanced atlas-modulated segmentation methods: Robust statistics, outlier detection & hierarchical segmentation From voxels to objects (voxel regions) Collaboration with DBP J. Bockholt, MIND Inst. NM: Lesions in lupus patients voxel only hierarchical

National Alliance for Medical Image Computing Brain lesion segmentation Multi-channel MRI (T1, T2, Flair) Lesion rules (Python interpreter): Lesion is brighter than gm in T2, brighter than wm in Flair, and lesion is brighter than gm in Flair (cf. van Leemput TMI 2001) Test on MIND lupus demo case

National Alliance for Medical Image Computing Validation/Verification A) Comparison to Ground Truthb) Standard objective test data: Synthetic MRI Extension of tumor/ edema simulation (MICCAI’05, TMI in rev) Synthetic MRI with lesions: BrainWeb data + DTI + reaction- diffusion + elastic deformation Bockholt ManualAutomatic, Prob. Automatic, Labels Automatic, 3D

National Alliance for Medical Image Computing Simulated Lesion MRI Multi-contrast MRI simulation with lesions To be created in close collaboration with clinical experts (Lupus: J. Bockholt, DBP) Used for objective evaluation of manual and automatic procedures

National Alliance for Medical Image Computing NAMIC toolkit Population-based analysis of DTI: Comprehensive framework for population-based DTI analysis Applications and libraries available as open source Ongoing Core-1/Core-2 activity: Integration of code into Slicer-3 and ITK Framework by definition requires workflow environment (batchmake) and linear/nonlinear registration modules Segmentation of brain lesions: Automatic lesion segmentation package in the ITK framework as workflow system (co-registration of MRI, atlas registration, robust estimation, bias correction, segmentation of tissue and lesions)

National Alliance for Medical Image Computing NAMIC-Publications J. H. Gilmore, W. Lin, I. Corouge, Y. S. K. Vetsa, J. K.Smith, Ch. Kang, H. Gu, R.M. Hamer, J. A. Lieberman, G. Gerig, Early Postnatal Development of Corpus Callosum and Corticospinal White Matter Assessed with Quantitative Tractography, AJNR Am J Neuroradiol Oct;28(9): Goodlett, C., Fletcher, P. Th., Lin, W., and Gerig, G., "Quantification of measurement error in DTI: Theoretical predictions and validation", Proc. MICCAI’07, Springer LNCS 4792, Nov. 2007, pp C. B. Goodlett, P. T. Fletcher, W. Lin, and G. Gerig, Noise- induced bias in low-direction diffusion tensor MRI: Replication of Monte-Carlo simulation with in-vivo scans, short paper accepted ISMRM 2007