MFDRNet: A Self-Supervised Framework for Sparse-View XACT Image Reconstruction via Multi-View Fusion and Artifact Disentanglement

MFDRNet: A Self-Supervised Framework for Sparse-View XACT Image Reconstruction via Multi-View Fusion and Artifact Disentanglement | 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 MFDRNet: A Self-Supervised Framework for Sparse-View XACT Image Reconstruction via Multi-View Fusion and Artifact Disentanglement Xin Jiang, Zheng Sun, Hang Ren, Sitong Zhai, Mingzhe Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7475702/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 Background and Purpose: X-ray-induced Acoustic Computed Tomography (XACT) holds significant promise for real-time radiotherapy dose verification by leveraging the linear relationship between acoustic pressure and deposited radiation dose. However, clinical implementation faces significant challenges from highly sparse data acquisition, constrained by equipment cost, patient safety, and acquisition time, resulting in severe reconstruction artifacts that compromise diagnostic utility. Methods: We introduce MFDRNet, a novel Multi-view Fusion and Disentangling Reconstruction Network that addresses sparse-view XACT reconstruction through three core components: (1) Multi-View Masking Modeling (MVM) that exploits structural redundancy across multiple sparse sampling perspectives, (2) Multi-Scale Attention Multi-View Fusion (MSA-MVF) that employs a tri-branch attention mechanism to integrate features, and (3) Artifact-Disentangling Block (ADB) that explicitly separates content from artifacts. The framework operates under a self-supervised learning paradigm, eliminating the dependency on ground truth annotations while achieving effective sparse signal reconstruction. Results: Extensive experiments on phantom, animal, and human datasets demonstrates substantial performance improvements over established methodolo-gies. Under extreme sparsity conditions (16-channel sampling representing 94% data reduction), MFDRNet achieves 29.37 dB PSNR and 0.9580 SSIM on phantom data, yields up to 400% PSNR improvement over traditional Time-Reversal 1 methods in clinical scenarios. Ablation studies confirm the additive contributions of all architectural components, with progressive performance enhancement across module additions culminating in a 10.92 dB PSNR gain over baseline approaches. Conclusion : MFDRNet successfully enables high-quality XACT image reconstruction under severe sparse sampling constraints while preserving critical anatomical structures and diagnostic contrast essential for radiotherapy dose verification. Its strong generalization across domains and demonstrated clinical viability position this framework as a viable solution for practical XACT implementation in precision radiotherapy guidance. Biological sciences/Cancer Physical sciences/Engineering Health sciences/Medical research Health sciences/Oncology X-ray induced acoustic computed tomography sparse sampling radiotherapy artifact suppression Full Text Additional Declarations No competing interests reported. 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7475702","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":526154749,"identity":"c981ffe0-af9c-4c12-8a07-4b651e30d6b9","order_by":0,"name":"Xin Jiang","email":"","orcid":"","institution":"Chongqing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Jiang","suffix":""},{"id":526154750,"identity":"0d4f3fbe-0d86-4b98-89a0-5b0a97db8c0e","order_by":1,"name":"Zheng Sun","email":"","orcid":"","institution":"Chongqing University of 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suppression","lastPublishedDoi":"10.21203/rs.3.rs-7475702/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7475702/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and Purpose:\u003c/strong\u003e X-ray-induced Acoustic Computed Tomography (XACT) holds significant promise for real-time radiotherapy dose verification by leveraging the linear relationship between acoustic pressure and deposited radiation dose. However, clinical implementation faces significant challenges from highly sparse data acquisition, constrained by equipment cost, patient safety, and acquisition time, resulting in severe reconstruction artifacts that compromise diagnostic utility. \u003cstrong\u003eMethods:\u003c/strong\u003e We introduce MFDRNet, a novel Multi-view Fusion and Disentangling Reconstruction Network that addresses sparse-view XACT reconstruction through three core components: (1) Multi-View Masking Modeling (MVM) that exploits structural redundancy across multiple sparse sampling perspectives, (2) Multi-Scale Attention Multi-View Fusion (MSA-MVF) that employs a tri-branch attention mechanism to integrate features, and (3) Artifact-Disentangling Block (ADB) that explicitly separates content from artifacts. The framework operates under a self-supervised learning paradigm, eliminating the dependency on ground truth annotations while achieving effective sparse signal reconstruction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eExtensive experiments on phantom, animal, and human datasets demonstrates substantial performance improvements over established methodolo-gies. Under extreme sparsity conditions (16-channel sampling representing 94% data reduction), MFDRNet achieves 29.37 dB PSNR and 0.9580 SSIM on phantom data, yields up to 400% PSNR improvement over traditional Time-Reversal 1 methods in clinical scenarios. Ablation studies confirm the additive contributions of all architectural components, with progressive performance enhancement across module additions culminating in a 10.92 dB PSNR gain over baseline approaches.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: MFDRNet successfully enables high-quality XACT image reconstruction under severe sparse sampling constraints while preserving critical anatomical structures and diagnostic contrast essential for radiotherapy dose verification. Its strong generalization across domains and demonstrated clinical viability position this framework as a viable solution for practical XACT implementation in precision radiotherapy guidance.\u003c/p\u003e","manuscriptTitle":"MFDRNet: A Self-Supervised Framework for Sparse-View XACT Image Reconstruction via Multi-View Fusion and Artifact Disentanglement","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 02:24:01","doi":"10.21203/rs.3.rs-7475702/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e613d520-badd-4b8a-8510-914dfbba2ed6","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55924100,"name":"Biological sciences/Cancer"},{"id":55924101,"name":"Physical sciences/Engineering"},{"id":55924102,"name":"Health sciences/Medical research"},{"id":55924103,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-10-08T02:53:36+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-08 02:24:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7475702","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7475702","identity":"rs-7475702","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}