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.