Correcting fast irregular motion in PET: Maximum-Likelihood Motion and Activity (MLMA) reconstruction

Correcting fast irregular motion in PET: Maximum-Likelihood Motion and Activity (MLMA) reconstruction | 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 Research Article Correcting fast irregular motion in PET: Maximum-Likelihood Motion and Activity (MLMA) reconstruction Rodrigo José Santo, Ethan Waterink, Cornelis A.T. van den Berg, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8467687/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 Purpose PET imaging naturally suffers from motion blur due to long acquisitions. As such, motion-compensation provides a promising solution to improve image quality. Traditional methods for motion-correction often involve a combination of gating and data-binning, assuming that motion is periodic, to accumulate sufficient counts per motion-state frame. Irregular motion can be estimated but it requires complex motion-capture systems or elaborate data-driven algorithms, which are difficult to configure (physical or model setup) and hampered by the high noise of short timeframes. We propose a new method that alternatingly estimates and corrects for motion at high temporal frequency in PET imaging: Maximum-Likelihood Motion and Activity (MLMA) reconstruction. MLMA estimates both the time-series of deformation vector fields and the motion-corrected activity image for the whole acquisition. Together, this allows to visualize anatomical structures moving in time. Methods The method exploits the high compressibility and spatial smoothness of motion through a cubic B-spline motion-model and through spatial regularization. MLMA was configured to 2Hz resolution and applied on A) the digital XCAT phantom, B) acquisitions of a moving anthropomorphic torso phantom and C) clinical patient data. Results The results show that MLMA can accurately correct motion at high frequency (2Hz), with subvoxel accuracy (up to 2.5mm RMSE on 4mm isotropic voxels) and realistic breathing (amplitude range 14.7mm and average period 4.5s). This enables visually-noticeable improvements on image quality. Conclusion The proposed MLMA reconstruction method resolves the motion encoded in very-short PET timeframes, irrespective of the very low counts and noise inherent to PET projection data. Nuclear Medicine & Medical Imaging Motion correction gateless motion-corrected PET high-frequency motion-corrected PET irregular non-rigid motion correction Full Text Additional Declarations The authors declare no competing interests. This study does not fall under the scope of the Dutch Medical Research Involving Human Subjects Act (WMO). It therefore does not require approval from an accredited medical ethics committee in the Netherlands. However, in the UMC Utrecht, an independent quality check has been carried out to ensure compliance with legislation and regulations (regarding Informed Consent procedure, data management, privacy aspects and legal aspects). 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-8467687","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":566475363,"identity":"0e75f972-ab37-42cc-83f2-46ab002c38eb","order_by":0,"name":"Rodrigo José 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It therefore does not require approval from an accredited medical ethics committee in the Netherlands. However, in the UMC Utrecht, an independent quality check has been carried out to ensure compliance with legislation and regulations (regarding Informed Consent procedure, data management, privacy aspects and legal aspects).\u003c/p\u003e","formattedTitle":"\u003cp\u003eCorrecting fast irregular motion in PET: Maximum-Likelihood Motion and Activity (MLMA) reconstruction\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University Medical Center Utrecht","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"Motion correction, gateless motion-corrected PET, high-frequency motion-corrected PET, irregular non-rigid motion correction","lastPublishedDoi":"10.21203/rs.3.rs-8467687/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8467687/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePET imaging naturally suffers from motion blur due to long acquisitions. As such, motion-compensation provides a promising solution to improve image quality. Traditional methods for motion-correction often involve a combination of gating and data-binning, assuming that motion is periodic, to accumulate sufficient counts per motion-state frame. Irregular motion can be estimated but it requires complex motion-capture systems or elaborate data-driven algorithms, which are difficult to configure (physical or model setup) and hampered by the high noise of short timeframes.\u003c/p\u003e\n\u003cp\u003eWe propose a new method that alternatingly estimates and corrects for motion at high temporal frequency in PET imaging: Maximum-Likelihood Motion and Activity (MLMA) reconstruction. MLMA estimates both the time-series of deformation vector fields and the motion-corrected activity image for the whole acquisition. Together, this allows to visualize anatomical structures moving in time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe method exploits the high compressibility and spatial smoothness of motion through a cubic B-spline motion-model and through spatial regularization. MLMA was configured to 2Hz resolution and applied on A) the digital XCAT phantom, B) acquisitions of a moving anthropomorphic torso phantom and C) clinical patient data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results show that MLMA can accurately correct motion at high frequency (2Hz), with subvoxel accuracy (up to 2.5mm RMSE on 4mm isotropic voxels) and realistic breathing (amplitude range 14.7mm and average period 4.5s). This enables visually-noticeable improvements on image quality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe proposed MLMA reconstruction method resolves the motion encoded in very-short PET timeframes, irrespective of the very low counts and noise inherent to PET projection data.\u003c/p\u003e","manuscriptTitle":"Correcting fast irregular motion in PET: Maximum-Likelihood Motion and Activity (MLMA) reconstruction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-07 12:34:29","doi":"10.21203/rs.3.rs-8467687/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":"b1c909d5-8238-495b-ba8d-5fc325825aa2","owner":[],"postedDate":"January 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60297421,"name":"Nuclear Medicine \u0026 Medical Imaging"}],"tags":[],"updatedAt":"2026-01-07T12:34:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-07 12:34:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8467687","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8467687","identity":"rs-8467687","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}