TY - GEN
T1 - Fiber tracking in traumatic brain injury
T2 - 1st International Workshop on Brainlesion, Brainles 2015 Held in Conjunction with International Conference on Medical Image Computing for Computer-Assisted Intervention, MICCAI 2015
AU - Dennis, Emily L.
AU - Prasad, Gautam
AU - Daianu, Madelaine
AU - Zhan, Liang
AU - Babikian, Talin
AU - Kernan, Claudia
AU - Mink, Richard
AU - Babbitt, Christopher
AU - Johnson, Jeffrey
AU - Giza, Christopher C.
AU - Asarnow, Robert F.
AU - Thompson, Paul M.
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - Traumatic brain injury (TBI) can cause widespread and long-lasting damage to white matter. Diffusion weighted imaging methods are uniquely sensitive to this disruption. Even so, traumatic injury often disrupts brain morphology as well, complicating the analysis of brain integrity and connectivity, which are typically evaluated with tractography methods optimized for analyzing normal healthy brains. To understand which fiber tracking methods show promise for analysis of TBI, we tested 9 different tractography algorithms for their classification accuracy and their ability to identify vulnerable areas as candidates for longitudinal follow-up in pediatric TBI participants and matched controls. Deterministic tractography models yielded the highest classification accuracies, but their limitations in areas of extensive fiber crossing suggested that they generated poor candidates for longitudinal follow-up. Probabilistic methods, including a method based on the Hough transform, yielded slightly lower accuracy, but generated follow-up candidate connections more coherent with the known neuropathology of TBI.
AB - Traumatic brain injury (TBI) can cause widespread and long-lasting damage to white matter. Diffusion weighted imaging methods are uniquely sensitive to this disruption. Even so, traumatic injury often disrupts brain morphology as well, complicating the analysis of brain integrity and connectivity, which are typically evaluated with tractography methods optimized for analyzing normal healthy brains. To understand which fiber tracking methods show promise for analysis of TBI, we tested 9 different tractography algorithms for their classification accuracy and their ability to identify vulnerable areas as candidates for longitudinal follow-up in pediatric TBI participants and matched controls. Deterministic tractography models yielded the highest classification accuracies, but their limitations in areas of extensive fiber crossing suggested that they generated poor candidates for longitudinal follow-up. Probabilistic methods, including a method based on the Hough transform, yielded slightly lower accuracy, but generated follow-up candidate connections more coherent with the known neuropathology of TBI.
UR - http://www.scopus.com/inward/record.url?scp=84961620554&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-30858-6_4
DO - 10.1007/978-3-319-30858-6_4
M3 - Conference contribution
AN - SCOPUS:84961620554
SN - 9783319308579
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 33
EP - 44
BT - Brainlesion
A2 - Reyes, Mauricio
A2 - Crimi, Alessandro
A2 - Maier, Oskar
A2 - Maier, Oskar
A2 - Handels, Heinz
A2 - Menze, Bjoern
PB - Springer Verlag
Y2 - 5 October 2015 through 5 October 2015
ER -