FORCE: FORward modeling for Complex microstructure Estimation

FORCE: FORward modeling for Complex microstructure Estimation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article FORCE: FORward modeling for Complex microstructure Estimation Atharva Jaydeep Shah, Rafael Neto Henriques, Alonso Ramirez-Manzanares, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8151109/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Diffusion Magnetic Resonance Imaging (dMRI) is a noninvasive modality that enables the study of brain tissue microstructure and the reconstruction of neural pathways. To achieve this, most reconstruction methods rely on inverse modeling techniques, which are often ill-posed and struggle to resolve shallow fiber crossings. Moreover, existing methods typically focus either on estimating fiber orientations or on deriving microstructural maps. As a result, obtaining a comprehensive characterization of tissue microstructure and architecture often requires combining multiple models, which is computationally demanding, potentially inconsistent due to model-specific assumptions and acquisition settings. This work introduces FORCE, a forward modeling paradigm that reframes how diffusion data is analyzed. Instead of inverting the measured signal, FORCE simulates a large set of biologically plausible intra-voxel fiber configurations and tissue compositions. It then identifies the best-matching simulation for each voxel by operating directly in the signal space. This unified framework simultaneously resolves low-angle fiber crossings, producing a large suite of microstructural maps and complete tissue segmentation in a single process. The proposed approach demonstrates robust performance across synthetic and real datasets from both human and mouse brains, encompassing multiple resolutions and acquisition types. Biological sciences/Neuroscience/Computational neuroscience Biological sciences/Neuroscience Diffusion-weighted MRI (dMRI) Fiber reconstruction Simulation-based Microstructure modeling Forward modeling Biophysics Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8151109","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":547718083,"identity":"e395a6ce-1a51-4e85-bae5-a9a9ba54963c","order_by":0,"name":"Atharva Jaydeep Shah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYFACHgZmBoYDcmwQNjGADaLFmHQtiQ1Ea+Gf33v4c0HNnfQ+iQTGB2/biNAicYwvTXrGsWe5bRIJzIZzidHCcIzHjJmH7TBIC5s0LzFa5I/xGH/m+Xc4nU0igf03UVoMjvEYAA0/nADUwsZMlBbDYzlm0rx9hw3beB42S845R4QWucNngA77dlhevj354Ic3ZURoQQKMDaSpHwWjYBSMglGAGwAAxfcxU1JskkwAAAAASUVORK5CYII=","orcid":"","institution":"Indiana University","correspondingAuthor":true,"prefix":"","firstName":"Atharva","middleName":"Jaydeep","lastName":"Shah","suffix":""},{"id":547718084,"identity":"8d7f8b12-9ba9-4535-90f7-a5ae75a8f381","order_by":1,"name":"Rafael Neto Henriques","email":"","orcid":"https://orcid.org/0000-0002-3891-8189","institution":"Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências da Universidade de Lisboa","correspondingAuthor":false,"prefix":"","firstName":"Rafael","middleName":"Neto","lastName":"Henriques","suffix":""},{"id":547718085,"identity":"1519269b-f578-4ce1-abe4-f5029c4b4a0b","order_by":2,"name":"Alonso Ramirez-Manzanares","email":"","orcid":"","institution":"Centro de Investigación en Matemáticas A.C. 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To achieve this, most reconstruction methods rely on inverse modeling techniques, which are often ill-posed and struggle to resolve shallow fiber crossings. Moreover, existing methods typically focus either on estimating fiber orientations or on deriving microstructural maps. As a result, obtaining a comprehensive characterization of tissue microstructure and architecture often requires combining multiple models, which is computationally demanding, potentially inconsistent due to model-specific assumptions and acquisition settings. This work introduces FORCE, a forward modeling paradigm that reframes how diffusion data is analyzed. Instead of inverting the measured signal, FORCE simulates a large set of biologically plausible intra-voxel fiber configurations and tissue compositions. It then identifies the best-matching simulation for each voxel by operating directly in the signal space. 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