{"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/generating-diffusion-mri-scalar-maps-from-t1","title":"Generating Diffusion MRI scalar maps from T1 weighted images using generative adversarial networks","arxiv_id":"1810.02683","date":"2018-10-05","proceeding":null,"authors":["Xuan Gu","Hans Knutsson","Markus Nilsson","Anders Eklund"],"abstract":"Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive\nmicrostructure assessment technique. Scalar measures, such as FA (fractional\nanisotropy) and MD (mean diffusivity), quantifying micro-structural tissue\nproperties can be obtained using diffusion models and data processing\npipelines. However, it is costly and time consuming to collect high quality\ndiffusion data. Here, we therefore demonstrate how Generative Adversarial\nNetworks (GANs) can be used to generate synthetic diffusion scalar measures\nfrom structural T1-weighted images in a single optimized step. Specifically, we\ntrain the popular CycleGAN model to learn to map a T1 image to FA or MD, and\nvice versa. As an application, we show that synthetic FA images can be used as\na target for non-linear registration, to correct for geometric distortions\ncommon in diffusion MRI.","url_abs":"http://arxiv.org/abs/1810.02683v3","url_pdf":"http://arxiv.org/pdf/1810.02683v3.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":"generating-diffusion-mri-scalar-maps-from-t1","repo_url":"https://github.com/xuagu37/CycleGAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"diffusion-mri","task_name":"Diffusion  MRI"}],"methods":[{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"cycle-consistency-loss","method_name":"Cycle Consistency Loss"},{"method_slug":"gan-least-squares-loss","method_name":"GAN Least Squares Loss"},{"method_slug":"instance-normalization","method_name":"Instance Normalization"},{"method_slug":"patchgan","method_name":"PatchGAN"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}