期刊
COMPUTERS IN BIOLOGY AND MEDICINE
卷 135, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2021.104643
关键词
Diffusion weighted image; Deep learning; Constrained spherical deconvolution; Tractography
类别
资金
- National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London
- NIHR Clinical Research Facility
- EPSRC Research Council [EPSRC DTP EP/R513064/1]
- National Institute for Health Research University College London Hospitals Biomedical Research Centre (NIHR BRC UCLH/UCL High Impact Initiative) [BW.mn.BRC10269]
This study aims to improve FOD estimation from clinically acquired dMRI. Patch-based 3D convolutional neural networks (CNNs) are evaluated on their ability to regress multi-shell FODs from single-shell FODs, using constrained spherical deconvolution (CSD). U-Net and HighResolution Network (HighResNet) 3D CNN architectures are evaluated on different datasets to assess their ability to resolve FODs in various scenarios, including datasets with different acquisition protocols and number of gradient directions. This work represents progress towards more accurate FOD estimation in time- and resource-limited clinical environments.
Local fiber orientation distributions (FODs) can be computed from diffusion magnetic resonance imaging (dMRI). The accuracy and ability of FODs to resolve complex fiber configurations benefits from acquisition protocols that sample a high number of gradient directions, a high maximum b-value, and multiple b-values. However, acquisition time and scanners that follow these standards are limited in clinical settings, often resulting in dMRI acquired at a single shell (single b-value). In this work, we learn improved FODs from clinically acquired dMRI. We evaluate patch-based 3D convolutional neural networks (CNNs) on their ability to regress multi-shell FODs from single-shell FODs, using constrained spherical deconvolution (CSD). We evaluate U-Net and HighResolution Network (HighResNet) 3D CNN architectures on data from the Human Connectome Project and an in-house dataset. We evaluate how well each CNN can resolve FODs 1) when training and testing on datasets with the same dMRI acquisition protocol; 2) when testing on a dataset with a different dMRI acquisition protocol than used to train the CNN; and 3) when testing on a dataset with a fewer number of gradient directions than used to train the CNN. This work is a step towards more accurate FOD estimation in time- and resource-limited clinical environments.
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