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Ten biopsy-proven prostate cancer patients were reconstructed from full-angle data decimated to 30, 15, and 10 views using OSEM, then postfiltered with Gaussian or edge-preserving NLM. Halving views to 30 preserved diagnostic accuracy, while 15 introduced angular aliasing and 10 generated unacceptable artifacts. For 30 views, NLM improved NRMSE, PSNR, and bias over Gaussian, with strong interobserver agreement, supporting feasibility for routine and emergency imaging.",{"@graph":69,"@context":126},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/sparse-view-reconstruction-and-noise-reduction-in-99mtc-psma-prostate-imaging-preliminary-study/455957/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/sparse-view-reconstruction-and-noise-reduction-in-99mtc-psma-prostate-imaging-preliminary-study/455957.png","ImageObject",300,407,{"name":92,"@type":93},"Sarah ","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"What is the purpose of this study on sparse-view PSMA SPECT?","Question",{"text":112,"@type":113},"The study evaluates feasibility and clinical value of sparse-view acquisition for 99mTc-PSMA prostate imaging and compares Gaussian filtering with an edge-preserving NLM filter in the sparse-view setting.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were the sparse-view datasets generated and reconstructed?",{"text":117,"@type":113},"Ten biopsy-proven prostate cancer patients underwent full-angle acquisitions (60 views), which were decimated to 30, 15, and 10 views. Images were reconstructed with OSEM (9 iterations, 5 subsets) and postfiltered using a Gaussian kernel or NLM.",{"name":119,"@type":110,"acceptedAnswer":120},"What were the main findings for 30, 15, and 10 views?",{"text":121,"@type":113},"Reducing to 30 views preserved diagnostic accuracy. Reducing to 15 views introduced sampling artifacts consistent with angular Nyquist violation, and 10 views produced unacceptable artifacts.",{"name":123,"@type":110,"acceptedAnswer":124},"Why is NLM postfiltering important in the 30-view reconstructions?",{"text":125,"@type":113},"For 30 views, NLM outperformed Gaussian filtering, improving NRMSE (up to 39.3%), PSNR (by 4.5%), and normalized bias (18.55%), with high interobserver agreement.","https://schema.org",{"og:url":83,"og:type":128,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":130,"canonical":83},"index,follow",{"doc_id":132,"site_id":62},455957,1791101295,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":143,"language":144,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":145,"faqs":146,"seo_title":147,"seo_description":67,"update_tm":148,"read_time":41,"preview_page_count":4},962085320529,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Original study  \nSparse-view reconstruction and noise reduction in 99mTcprostate specific membrane antigen prostate imaging  \nSayna Jamaatia, Masoumeh Dorri Givb, Xiaotong Hongc, Ramin Sadeghib, Amin Saber Tanhab, Masoud Noroozid and Hossein Arabie  \nPurpose This preliminary study evaluates the feasibility and clinical value of sparse-view acquisition in 99mTcprostate specific membrane antigen (PSMA) single photon emission computed tomography (SPECT) prostate imaging. This study also compares the performance of traditional Gaussian filtering vs. an edge-preserving nonlocal means (NLM) filter in the sparse-view SPECT approach.  \nMethods Ten patients with biopsy-proven prostate cancer (Gleason 4–10) underwent full-angle acquisitions (60 views), which were decimated to 30, 15, and 10 views. Images were reconstructed with ordered-subsets expectation maximization (9 iterations, 5 subsets) and postfiltered with a Gaussian kernel or NLM. Quantitative performance metrics included mean absolute percentage error (MAPE), normalized root mean square error (NRMSE), peak signal-to-noise ratio (PSNR), and normalized bias (NB). Two nuclear medicine physicians (10–12 years’ experience) provided blinded visual assessments.  \nResults Halving the views to 30 preserved diagnostic accuracy (MAPE_ROI1 ≤ 7%). Reducing to 15 views introduced sampling artifacts consistent with violation of the angular Nyquist limit, and 10 views produced unacceptable artifacts (MAPE_ROI1 > 50%). For 30-view reconstructions, NLM outperformed Gaussian, improving  \nNRMSE by up to 39.3%, PSNR by 4.5%, and NB by 18.55%. Expert readers confirmed these trends, with high interobserver agreement (intraclass correlation coefficient = 0.83) for 30 views.  \nConclusion Sparse-view 99mTc-PSMA SPECT with 30 views appears feasible for routine and emergency prostate imaging in this setting. Reconstructions from 15 views showed aliasing from angular undersampling and  \nare not recommended without anti-aliasing strategies and further validation. This work characterizes the added value of NLM postreconstruction filtering in PSMA-SPECT and motivates larger studies. Nucl Med Commun 47: 185–196 Copyright © 2025 The Author(s). Published by Wolters Kluwer Health, Inc.  \nNuclear Medicine Communications 2026, 47:185–196  \nKeywords: prostate cancer, prostate specific membrane antigen, single photon emission computed tomography, sparse-view reconstruction  \naDepartment of Energy Engineering, Sharif University of Technology,  \nTehran, bNuclear Medicine Research Center, Department of Nuclear Medicine, Ghaem Hospital, Medical University of Mashhad, Mashhad, Iran, cDepartment of Biomedical Engineering, Southern Medical University, Guangzhou,  \nChina, dDepartment of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran and eDepartment of Nuclear Medicine & Molecular Imaging, Geneva University Hospital, Geneva, Switzerland  \nCorrespondence to Hossein Arabi, PhD, Department of Nuclear Medicine & Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland  \nTel: +41 767261714; e-mail: [hossein.arabi@unige.ch](hossein.arabi@unige.ch)[ ](hossein.arabi@unige.ch)[Received 20 May 2025 Accepted 28 October 2025](Received 20 May 2025 Accepted 28 October 2025) .  \nIntroduction  \nSingle photon emission computed tomography (SPECT) generates a three-dimensional map of gamma-ray emissions from radionuclides. This imaging technique provides valuable functional data on organs and tissues, allowing for the detection of abnormalities at a functional level before anatomical changes become apparent. SPECT is widely used to assess normal physiological processes and diagnose a range of conditions, including cardiovascular diseases, neurological disorders, thyroid dysfunction, and cancer [1–7]. However, scan durations could take 20–30 min or longer, depending on the area being imaged and the clinical protocol. For example, whole-body scans or more detailed protocols can push  \nThis","cbCaigqMLqR897Yo","https://ap.wps.com/l/cbCaigqMLqR897Yo","pdf",9226128,12,"English","# Purpose\n# Methods\n# Results\n# Conclusion\n# Introduction","[{\"question\":\"What is the purpose of this study on sparse-view PSMA SPECT?\",\"answer\":\"The study evaluates feasibility and clinical value of sparse-view acquisition for 99mTc-PSMA prostate imaging and compares Gaussian filtering with an edge-preserving NLM filter in the sparse-view setting.\"},{\"question\":\"How were the sparse-view datasets generated and reconstructed?\",\"answer\":\"Ten biopsy-proven prostate cancer patients underwent full-angle acquisitions (60 views), which were decimated to 30, 15, and 10 views. Images were reconstructed with OSEM (9 iterations, 5 subsets) and postfiltered using a Gaussian kernel or NLM.\"},{\"question\":\"What were the main findings for 30, 15, and 10 views?\",\"answer\":\"Reducing to 30 views preserved diagnostic accuracy. Reducing to 15 views introduced sampling artifacts consistent with angular Nyquist violation, and 10 views produced unacceptable artifacts.\"},{\"question\":\"Why is NLM postfiltering important in the 30-view reconstructions?\",\"answer\":\"For 30 views, NLM outperformed Gaussian filtering, improving NRMSE (up to 39.3%), PSNR (by 4.5%), and normalized bias (18.55%), with high interobserver agreement.\"}]","Sparse-view reconstruction and noise reduction in 99mTc-PSMA prostate imaging - Preliminary study | PDF",1790744791]