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Abstract #0283

Automated Tract-Specific Quantification Using Probabilistic Atlas Based on Large Deformation Diffeomorphic Metric Mapping and Its Application to Alzheimer's Disease

Kegang Hua1, Kenichi Oishi1, Hangyi Jiang1, Xin Li1, Jiangyang Zhang1, Kazi Dilruba Akhter1,2, Michael I. Miller3,4, Van Zijl C.M. Peter1,5, Marilyn Albert6, Constantine G. Lyketsos7, Michelle M. Mielke7, Susumu Mori1,2

1Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States; 2F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States; 3Center for Imaging Science, Johns Hopkins University, Baltimore, MD, United States; 4Department of Biomedical Engineering, Johns Hopkins University , Baltimore, MD, United States; 5F.M. Kirby Research Center for Functional Brain Imaging , Kennedy Krieger Institute, Baltimore, MD, United States; 6Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States; 7Department of Psychiatry, Johns Hopkins Bayview Medical Center, Baltimore, MD, United States


Tractography is widely used to define locations of specific tracts in the white matter and perform tract-specific quantification of various MR parameters such as FA and MD. However, tractography requires placements of ROIs to extract tracts of interest, which involves subjective and expert judgment. In this presentation, an automated tract-specific quantification approach is demonstrated based on pre-defined population-averaged tract information and a highly non-linear image transformation technique. This tool was applied to an Alzheimers disease population and age-matched control. The results show accurate tract identification and consistent diffusivity abnormality of the forceps major.

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