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Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes via the Bhattacharyya Distance

1School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
2Laboratory of Neuro Imaging, Department of Neurology, University of California, Los Angeles, CA, USA.
3Brain Injury Research Center, Department of Neurology and Neurosurgery, University of California, Los Angeles, CA, USA.
IEEE Engineering in Medicine and Biology Society
Publication Date:
IEEE Trans Biomed Eng
Volume Number:
Issue Number:
IEEE Trans Biomed Eng. 2013 Sep;60(9):2511-20.
PubMed ID:
Bhattacharyya Distance (BD), deformable image registration, multimodal registration, Mutual Information (MI), traumatic brain injury (TBI)
Appears in Collections:
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
R01 MH082918/MH/NIMH NIH HHS/United States
R41 NS081792/NS/NINDS NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Lou Y., Irimia A., Vela P.A., Chambers M.C., Van Horn J.D., Vespa P.M., Tannenbaum A.R. Multimodal Deformable Registration of Traumatic Brain Injury MR Volumes via the Bhattacharyya Distance. IEEE Trans Biomed Eng. 2013 Sep;60(9):2511-20. PMID: 23962986. PMCID: PMC4000558.
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An important problem of neuroimaging data analysis for traumatic brain injury (TBI) is the task of coregistering MR volumes acquired using distinct sequences in the presence of widely variable pixel movements which are due to the presence and evolution of pathology. We are motivated by this problem to design a numerically stable registration algorithm which handles large deformations. To this end, we propose a new measure of probability distributions based on the Bhattacharyya distance, which is more stable than the widely used mutual information due to better behavior of the square root function than the logarithm at zero. Robustness is illustrated on two TBI patient datasets, each containing 12 MR modalities. We implement our method on graphics processing units (GPU) so as to meet the clinical requirement of time-efficient processing of TBI data. We find that 6 sare required to register a pair of volumes with matrix sizes of 256 × 256 × 60 on the GPU. In addition to exceptional time efficiency via its GPU implementation, this methodology provides a clinically informative method for the mapping and evaluation of anatomical changes in TBI.

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