TY - CHAP
T1 - Beyond the resolution limit
T2 - Diffusion parameter estimation in partial volume
AU - Eaton-Rosen, Zach
AU - Melbourne, Andrew
AU - Jorge Cardoso, M.
AU - Marlow, Neil
AU - Ourselin, Sebastien
PY - 2016/1/1
Y1 - 2016/1/1
N2 - Diffusion MRI is a frequently-used imaging modality that can infer microstructural properties of tissue,down to the scale of microns. For single-compartment models,such as the diffusion tensor (DT),the model interpretation depends on voxels having homogeneous composition. This limitation makes it difficult to measure diffusion parameters for small structures such as the fornix in the brain,because of partial volume. In this work,we use a segmentation from a structural scan to calculate the tissue composition for each diffusion voxel. We model the measured diffusion signal as a linear combination of signals from each of the tissues present in the voxel,and fit parameters on a per-region basis by optimising over all diffusion data simultaneously. We test the proposed method by using diffusion data from the Human Connectome Project (HCP). We down sample the HCP data,and show that our method returns parameter estimates that are closer to the higher solution ground truths than for classical methods. We show that our method allows accurate estimation of diffusion parameters for regions with partial volume. Finally,we apply the method to compare diffusion in the fornix for adults born extremely preterm and matched controls.
AB - Diffusion MRI is a frequently-used imaging modality that can infer microstructural properties of tissue,down to the scale of microns. For single-compartment models,such as the diffusion tensor (DT),the model interpretation depends on voxels having homogeneous composition. This limitation makes it difficult to measure diffusion parameters for small structures such as the fornix in the brain,because of partial volume. In this work,we use a segmentation from a structural scan to calculate the tissue composition for each diffusion voxel. We model the measured diffusion signal as a linear combination of signals from each of the tissues present in the voxel,and fit parameters on a per-region basis by optimising over all diffusion data simultaneously. We test the proposed method by using diffusion data from the Human Connectome Project (HCP). We down sample the HCP data,and show that our method returns parameter estimates that are closer to the higher solution ground truths than for classical methods. We show that our method allows accurate estimation of diffusion parameters for regions with partial volume. Finally,we apply the method to compare diffusion in the fornix for adults born extremely preterm and matched controls.
UR - http://www.scopus.com/inward/record.url?scp=84996508203&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-46726-9_70
DO - 10.1007/978-3-319-46726-9_70
M3 - Conference paper
AN - SCOPUS:84996508203
SN - 9783319467252
VL - 9902 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 605
EP - 612
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
PB - Springer Verlag
ER -