Fitting parametric models of diffusion MRI in regions of partial volume

TY - CHAP

T1 - Fitting parametric models of diffusion MRI in regions of partial volume

AU - Eaton-Rosen, Zach

AU - Cardoso, M. J.

AU - Melbourne, Andrew

AU - Orasanu, Eliza

AU - Bainbridge, Alan

AU - Kendall, Giles S.

AU - Robertson, Nicola J.

AU - Marlow, Neil

AU - Ourselin, Sebastien

PY - 2016/1/1

Y1 - 2016/1/1

N2 - Regional analysis is normally done by fitting models per voxel and then averaging over a region, accounting for partial volume (PV) only to some degree. In thin, folded regions such as the cerebral cortex, such methods do not work well, as the partial volume confounds parameter estimation. Instead, we propose to fit the models per region directly with explicit PV modeling. In this work we robustly estimate region-wise parameters whilst explicitly accounting for partial volume effects. We use a high-resolution segmentation from a T1 scan to assign each voxel in the diffusion image a probabilistic membership to each of k tissue classes. We rotate the DW signal at each voxel so that it aligns with the z-axis, then model the signal at each voxel as a linear superposition of a representative signal from each of the k tissue types. Fitting involves optimising these representative signals to best match the data, given the known probabilities of belonging to each tissue type that we obtained from the segmentation. We demonstrate this method improves parameter estimation in digital phantoms for the diffusion tensor (DT) and Neurite Orientation Dispersion and Density Imaging' (NODDI) models. The method provides accurate parameter estimates even in regions where the normal approach fails completely, for example where partial volume is present in every voxel. Finally, we apply this model to brain data from preterm infants, where the thin, convoluted, maturing cortex necessitates such an approach.

AB - Regional analysis is normally done by fitting models per voxel and then averaging over a region, accounting for partial volume (PV) only to some degree. In thin, folded regions such as the cerebral cortex, such methods do not work well, as the partial volume confounds parameter estimation. Instead, we propose to fit the models per region directly with explicit PV modeling. In this work we robustly estimate region-wise parameters whilst explicitly accounting for partial volume effects. We use a high-resolution segmentation from a T1 scan to assign each voxel in the diffusion image a probabilistic membership to each of k tissue classes. We rotate the DW signal at each voxel so that it aligns with the z-axis, then model the signal at each voxel as a linear superposition of a representative signal from each of the k tissue types. Fitting involves optimising these representative signals to best match the data, given the known probabilities of belonging to each tissue type that we obtained from the segmentation. We demonstrate this method improves parameter estimation in digital phantoms for the diffusion tensor (DT) and Neurite Orientation Dispersion and Density Imaging' (NODDI) models. The method provides accurate parameter estimates even in regions where the normal approach fails completely, for example where partial volume is present in every voxel. Finally, we apply this model to brain data from preterm infants, where the thin, convoluted, maturing cortex necessitates such an approach.

KW - Cerebral Cortex

KW - Diffiusion MRI

KW - DTI

KW - Neonates

KW - NODDI

KW - Partial Volume

KW - Preterm

UR - http://www.scopus.com/inward/record.url?scp=84981709961&partnerID=8YFLogxK

U2 - 10.1117/12.2216352

DO - 10.1117/12.2216352

M3 - Conference paper

AN - SCOPUS:84981709961

VL - 9784

BT - Medical Imaging 2016

PB - SPIE

T2 - Medical Imaging 2016: Image Processing

Y2 - 1 March 2016 through 3 March 2016

ER -