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 Akhter^1,2, Michael I. Miller^3,4, Van Zijl C.M. Peter^1,5, Marilyn Albert⁶, Constantine G. Lyketsos⁷, Michelle M. Mielke⁷, Susumu Mori^1,2

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.