كتاب
Convolutional Neural Networks for Fiber Orientation Distribution Enhancement to Improve Single-Shell Diffusion MRI Tractography
| العنوان: | Convolutional Neural Networks for Fiber Orientation Distribution Enhancement to Improve Single-Shell Diffusion MRI Tractography |
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| المؤلفون: | Lucena, Oeslle, Vos, Sjoerd B., Vakharia, Vejay, Duncan, John, Ourselin, Sebastien, Sparks, Rachel |
| المصدر: | Lucena , O , Vos , S B , Vakharia , V , Duncan , J , Ourselin , S & Sparks , R 2020 , Convolutional Neural Networks for Fiber Orientation Distribution Enhancement to Improve Single-Shell Diffusion MRI Tractography . in Mathematics and Visualization . Mathematics and Visualization , Springer Science and Business Media Deutschland GmbH , pp. 101-112 . https://doi.org/10.1007/978-3-030-52893-5_9Test |
| بيانات النشر: | Springer Science and Business Media Deutschland GmbH |
| سنة النشر: | 2020 |
| المجموعة: | King's College, London: Research Portal |
| مصطلحات موضوعية: | Deep learning, Diffusion weighted image, Tractography |
| الوصف: | Diffusion MRI (dMRI) tractography may help locate critical white matter (WM) tracts that should be preserved during neurosurgery. A key step in this process is estimating fiber orientation distribution (FOD), often done from a model such as constrained spherical deconvolution (CSD). Multi-shell (MS) multi-tissue CSD (M-CSD) provides a robust WM FOD by estimating the relative contribution to the dMRI signal from each tissue type (WM, grey matter, and cerebrospinal fluid), however, single-shell (SS) single tissue CSD (S-CSD) cannot independently estimate the signal contribution for each tissue type. S-CSD is therefore less accurate estimating FOD in voxels where multiple tissues are present. Due to that inaccuracy, tractography using S-CSD often generates more spurious WM streamlines compared to M-CSD. In this work, we present a framework to regress the M-CSD model coefficients from the S-CSD model coefficients using a convolutional neural network (CNN) in order to improve tractography. We construct a training dataset comprising acquired MS dMRI and paired synthetic SS dMRI, generated by selecting the outer shell from the MS dMRI. We select a High Resolution Network (HighResNet) as our choice of CNN to ensure subtle details of the CSD models are preserved during regression. The HighResNet is trained to perform patch-based regression from the S-CSD model coefficients and a co-registered T1-wieghted MR (T1) to the M-CSD model coefficients. We evaluate the method on patients with epilepsy who appeared structurally normal on T1. Four WM tracts related to language are extracted using a ROI-based probabilistic tractography. For comparison, M-CSD is as a pseudo ground truth. The original S-CSD generated tracts with Dice of 0.53–0.64, and the HighResNet regressed CSD models generated tracts with Dice of 0.73–0.77. We demonstrate HighResNet can regress M-CSD model coefficients from S-CSD model coefficients resulting in tracts more similar to the M-CSD generated tracts and with fewer spurious streamlines than S-CSD ... |
| نوع الوثيقة: | book part |
| اللغة: | English |
| DOI: | 10.1007/978-3-030-52893-5_9 |
| الإتاحة: | https://doi.org/10.1007/978-3-030-52893-5_9Test https://kclpure.kcl.ac.uk/portal/en/publications/1d0828a7-235d-4bf1-afd2-d8071718f907Test http://www.scopus.com/inward/record.url?scp=85095865838&partnerID=8YFLogxKTest |
| حقوق: | info:eu-repo/semantics/restrictedAccess |
| رقم الانضمام: | edsbas.294BE843 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1007/978-3-030-52893-5_9 |
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