Sliding Gaussian ball adaptive growth: point cloud-based iterative algorithm for large-scale 3D photoacoustic imaging

Sliding Gaussian ball adaptive growth: point cloud-based iterative algorithm for large-scale 3D photoacoustic imaging | 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 Sliding Gaussian ball adaptive growth: point cloud-based iterative algorithm for large-scale 3D photoacoustic imaging Changhui Li, Yao Yao, Shuang Li, Yibing Wang, Jian Gao, Chulhong Kim, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5373791/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Dec, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Large-scale 3D photoacoustic (PA) imaging has become increasingly important for both clinical and pre-clinical applications. Limited by cost and system complexity, only systems with sparsely-distributed sensors can be widely implemented, which desires advanced reconstruction algorithms to reduce artifacts. However, high computing memory and time consumption of traditional iterative reconstruction (IR) algorithms is practically unacceptable for large-scale 3D PA imaging. Here, we propose a point cloud-based IR algorithm that reduces memory consumption by several orders, wherein the 3D PA scene is modeled as a series of Gaussian-distributed spherical sources stored in form of point cloud. During the IR process, not only are properties of each Gaussian source, including its peak intensity (initial pressure value), standard deviation (size) and mean (position) continuously optimized, but also each Gaussian source itself adaptively undergoes destroying, splitting, and duplication along the gradient direction. This method, named the sliding Gaussian ball adaptive growth (SlingBAG) algorithm, enables high-quality large-scale 3D PA reconstruction with fast iteration and extremely low memory usage. We validated SlingBAG algorithm in both simulation study and in vivo animal experiments. Biological sciences/Biological techniques/Imaging/3-D reconstruction Biological sciences/Biological techniques/Imaging/Optical imaging Iterative 3D PA reconstruction Point cloud-based model Gaussian ball representation Sparse sensor distribution Full Text Additional Declarations There is NO Competing Interest. Supplementary Files Supplementaryinformationsubmit.pdf SupplementaryVideo1.mp4 Supplementary Video 1 SupplementaryVideo2.mp4 Supplementary Video 2 SupplementaryVideo3.mp4 Supplementary Video 3 SupplementaryVideo4.mp4 Supplementary Video 4 SupplementaryVideo5.mp4 Supplementary Video 5 SupplementaryVideo6.mp4 Supplementary Video 6 Cite Share Download PDF Status: Published Journal Publication published 06 Dec, 2025 Read the published version in Nature Communications → 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5373791","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":385066274,"identity":"d1eb35f3-aa30-4d5f-aa86-0da345418319","order_by":0,"name":"Changhui Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYDACCeaGAx8YEkBMA2K1MDYcnEGyFmYekrTwz25sPGy7Iy2xgb15mwRDzR0iLLlzsOFw7pmcxAaeY2USDMeeEdZiIJEI1NJWkdggkWMGdORhIrVYgrTIvyFFC2Mb0GESPERqkbiR2HCw90yacRtPWrFFwjEitPDPSD784eeOZNl+9sMbb3yoIUILGDA2MDCwgRgJRGqAaBkFo2AUjIJRgBMAAOFBPFcot+mRAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-6179-2684","institution":"Peking University","correspondingAuthor":true,"prefix":"","firstName":"Changhui","middleName":"","lastName":"Li","suffix":""},{"id":385066275,"identity":"040b15ab-20b9-4031-9b69-b43bef88bf39","order_by":1,"name":"Yao Yao","email":"","orcid":"","institution":"Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Yao","suffix":""},{"id":385066276,"identity":"94a9f669-5db2-4584-aa85-64bcac8044ae","order_by":2,"name":"Shuang Li","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Shuang","middleName":"","lastName":"Li","suffix":""},{"id":385066277,"identity":"e00152d5-994e-4715-892a-1462561efd43","order_by":3,"name":"Yibing Wang","email":"","orcid":"https://orcid.org/0009-0004-9841-3846","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yibing","middleName":"","lastName":"Wang","suffix":""},{"id":385066278,"identity":"b14e191b-bf11-4850-9ae5-f412f08b0371","order_by":4,"name":"Jian Gao","email":"","orcid":"","institution":"Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Gao","suffix":""},{"id":385066279,"identity":"57b31ef4-253d-4561-be57-c773e97d15dc","order_by":5,"name":"Chulhong Kim","email":"","orcid":"https://orcid.org/0000-0001-7249-1257","institution":"Pohang University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Chulhong","middleName":"","lastName":"Kim","suffix":""},{"id":385066280,"identity":"9ff249a4-7688-4a48-81e6-d3d79baec236","order_by":6,"name":"Seongwook Choi","email":"","orcid":"","institution":"Pohang University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Seongwook","middleName":"","lastName":"Choi","suffix":""},{"id":385066281,"identity":"b9d77717-1f6e-47d8-9754-ce8b5f9a4cc7","order_by":7,"name":"Yu Zhang","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Zhang","suffix":""},{"id":385066282,"identity":"8c8a58b3-3d33-43fc-965d-eb71160c9ee9","order_by":8,"name":"Qian Chen","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-11-01 14:06:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5373791/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5373791/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-025-66855-w","type":"published","date":"2025-12-06T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":99676963,"identity":"c9252917-0f68-4a90-a5b3-b1692f31fc35","added_by":"auto","created_at":"2026-01-07 08:10:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7973187,"visible":true,"origin":"","legend":"","description":"","filename":"SlingBAGNBE.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5373791/v1_covered_9a589721-7438-49c4-ba9e-0d2920cbf720.pdf"},{"id":75055444,"identity":"1f0eacc2-758b-4426-a7ac-8749fbf14984","added_by":"auto","created_at":"2025-01-30 02:21:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2242620,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformationsubmit.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5373791/v1/fda9885dd19301505a08bff9.pdf"},{"id":75055443,"identity":"ccebbc9f-0b60-48df-8e4a-d974b5bcc2f3","added_by":"auto","created_at":"2025-01-30 02:21:27","extension":"mp4","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1515921,"visible":true,"origin":"","legend":"Supplementary Video 1","description":"","filename":"SupplementaryVideo1.mp4","url":"https://assets-eu.researchsquare.com/files/rs-5373791/v1/62480715f6ab88a7fd24805f.mp4"},{"id":75055448,"identity":"9c33e3d4-c381-476f-91d6-57f2b248376d","added_by":"auto","created_at":"2025-01-30 02:21:28","extension":"mp4","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":4915016,"visible":true,"origin":"","legend":"Supplementary Video 2","description":"","filename":"SupplementaryVideo2.mp4","url":"https://assets-eu.researchsquare.com/files/rs-5373791/v1/bdf10ca14fc777de6347005e.mp4"},{"id":75056035,"identity":"f3d6dbc3-6009-4cc1-83c1-7f6de21925c4","added_by":"auto","created_at":"2025-01-30 02:29:28","extension":"mp4","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":2568216,"visible":true,"origin":"","legend":"Supplementary Video 3","description":"","filename":"SupplementaryVideo3.mp4","url":"https://assets-eu.researchsquare.com/files/rs-5373791/v1/0d9d92d54ce5b95aa93aa3e3.mp4"},{"id":75056036,"identity":"1e7d1bff-2675-4e8a-9a81-0edc1a2c0f1d","added_by":"auto","created_at":"2025-01-30 02:29:28","extension":"mp4","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":6216721,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Video 4\u003c/p\u003e","description":"","filename":"SupplementaryVideo4.mp4","url":"https://assets-eu.researchsquare.com/files/rs-5373791/v1/736499d04b1113379b9b0899.mp4"},{"id":75055449,"identity":"32c84d63-84ce-432f-be98-7f8052bbbb4d","added_by":"auto","created_at":"2025-01-30 02:21:28","extension":"mp4","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":7724977,"visible":true,"origin":"","legend":"Supplementary Video 5","description":"","filename":"SupplementaryVideo5.mp4","url":"https://assets-eu.researchsquare.com/files/rs-5373791/v1/4eaa96d8ba8ba9fe179eb274.mp4"},{"id":75055446,"identity":"d3d8aeff-074d-458f-91d2-e26be66028c8","added_by":"auto","created_at":"2025-01-30 02:21:28","extension":"mp4","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":7224113,"visible":true,"origin":"","legend":"Supplementary Video 6","description":"","filename":"SupplementaryVideo6.mp4","url":"https://assets-eu.researchsquare.com/files/rs-5373791/v1/1f60694d1c77bed97158ba66.mp4"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Sliding Gaussian ball adaptive growth: point cloud-based iterative algorithm for large-scale 3D photoacoustic imaging","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Iterative 3D PA reconstruction, Point cloud-based model, Gaussian ball representation, Sparse sensor distribution","lastPublishedDoi":"10.21203/rs.3.rs-5373791/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5373791/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Large-scale 3D photoacoustic (PA) imaging has become increasingly important for both clinical and pre-clinical applications. Limited by cost and system complexity, only systems with sparsely-distributed sensors can be widely implemented, which desires advanced reconstruction algorithms to reduce artifacts. However, high computing memory and time consumption of traditional iterative reconstruction (IR) algorithms is practically unacceptable for large-scale 3D PA imaging. Here, we propose a point cloud-based IR algorithm that reduces memory consumption by several orders, wherein the 3D PA scene is modeled as a series of Gaussian-distributed spherical sources stored in form of point cloud. During the IR process, not only are properties of each Gaussian source, including its peak intensity (initial pressure value), standard deviation (size) and mean (position) continuously optimized, but also each Gaussian source itself adaptively undergoes destroying, splitting, and duplication along the gradient direction. This method, named the sliding Gaussian ball adaptive growth (SlingBAG) algorithm, enables high-quality large-scale 3D PA reconstruction with fast iteration and extremely low memory usage. We validated SlingBAG algorithm in both simulation study and in vivo animal experiments.","manuscriptTitle":"Sliding Gaussian ball adaptive growth: point cloud-based iterative algorithm for large-scale 3D photoacoustic imaging","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-30 02:21:23","doi":"10.21203/rs.3.rs-5373791/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"28d2667b-b96c-4356-a59d-059ecf5f4911","owner":[],"postedDate":"January 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":41016459,"name":"Biological sciences/Biological techniques/Imaging/3-D reconstruction"},{"id":41016460,"name":"Biological sciences/Biological techniques/Imaging/Optical imaging"}],"tags":[],"updatedAt":"2026-01-07T08:10:10+00:00","versionOfRecord":{"articleIdentity":"rs-5373791","link":"https://doi.org/10.1038/s41467-025-66855-w","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2025-12-06 05:00:00","publishedOnDateReadable":"December 6th, 2025"},"versionCreatedAt":"2025-01-30 02:21:23","video":"","vorDoi":"10.1038/s41467-025-66855-w","vorDoiUrl":"https://doi.org/10.1038/s41467-025-66855-w","workflowStages":[]},"version":"v1","identity":"rs-5373791","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5373791","identity":"rs-5373791","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}