{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/using-convolution-neural-networks-to-learn","title":"Enhancing Fiber Orientation Distributions using convolutional Neural Networks","arxiv_id":"2008.05409","date":"2020-08-12","proceeding":null,"authors":["Oeslle Lucena","Sjoerd B. Vos","Vejay Vakharia","John Duncan","Keyoumars Ashkan","Rachel Sparks","Sebastien Ourselin"],"abstract":"Accurate local fiber orientation distribution (FOD) modeling based on diffusion magnetic resonance imaging (dMRI) capable of resolving complex fiber configurations benefits from specific acquisition protocols that sample a high number of gradient directions (b-vecs), a high maximum b-value(b-vals), and multiple b-values (multi-shell). However, acquisition time is limited in a clinical setting and commercial scanners may not provide such dMRI sequences. Therefore, dMRI is often acquired as single-shell (single b-value). In this work, we learn improved FODs for commercially acquired MRI. We evaluate patch-based 3D convolutional neural networks (CNNs)on their ability to regress multi-shell FOD representations from single-shell representations, where the representation is a spherical harmonics obtained from constrained spherical deconvolution (CSD) to model FODs. We evaluate U-Net and HighResNet 3D CNN architectures on data from the Human Connectome Project and an in-house dataset. We evaluate how well each CNN model can resolve local fiber orientation 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 models; and 3) when testing on a dataset with a fewer number of gradient directions than used to train the CNN models. Our approach may enable robust CSD model estimation on single-shell dMRI acquisition protocols with few gradient directions, reducing acquisition times, facilitating translation of improved FOD estimation to time-limited clinical environments.","url_abs":"https://arxiv.org/abs/2008.05409v2","url_pdf":"https://arxiv.org/pdf/2008.05409v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"using-convolution-neural-networks-to-learn","repo_url":"https://github.com/OeslleLucena/RegressionFOD","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[{"method_slug":"3d-cnn","method_name":"3D CNN"},{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"diffusion","method_name":"Diffusion"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}