{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T05:14:49Z","timestamp":1788239689840,"version":"build-2803163510"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001711","name":"Schweizerischer Nationalfonds zur F\u00f6rderung der Wissenschaftlichen Forschung","doi-asserted-by":"publisher","award":["SNSF 320030_231742"],"award-info":[{"award-number":["SNSF 320030_231742"]}],"id":[{"id":"10.13039\/501100001711","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006389","name":"University of Geneva","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100006389","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Brain single-photon emission computed tomography (SPECT) imaging using I-123 DaTSCAN is an effective tool for the diagnosis and follow-up of Parkinson disease. Reducing acquisition time decreases the likelihood of patient motion, improves patient comfort, and increases scanner throughput. However, shorter acquisition times often result in degraded image quality with reduced diagnostic value. This study aimed to use deep learning (DL) approaches to enhance the image quality of accelerated SPECT DaTSCAN acquisitions. A total of 85 patients were scanned on a CZT-based GE StarGuide scanner with a standard acquisition time of 15\u00a0min. To simulate fast acquisition protocols, raw list-mode data were retrospectively undersampled to represent 20%, 25%, and 50% of the original acquisition time and then reconstructed using the clinical routine protocol. A modified U-Net DL architecture incorporating attention and transformer mechanisms was trained to convert the low-count images into full-time equivalents. The output images were compared to full-time reference images using subjective image quality assessment by nuclear medicine physicians as well as region-wise and voxel-wise quantitative metrics on the test set. Both voxel-wise and region-wise metrics showed significant improvement when using the DL models for all fast acquisition times. Subjective image quality evaluations confirmed that the DL-predicted images were noticeably superior to the original low-count reconstructions in terms of perceived image quality and diagnostic confidence. The mean absolute percentage error (MAPE) was reduced from\u2009~\u200930 to\u2009~\u200918% for fast 1\/5 acquisition time images and from\u2009~\u200926 to\u2009~\u200916% for 1\/4 acquisition time images. Our acquisition time-specific deep learning models effectively restore information loss in fast SPECT acquisitions and reduce image noise, without compromising diagnostic value. The models offer a promising approach for faster, patient-friendly brain SPECT imaging without sacrificing image quality or quantitative accuracy.<\/jats:p>","DOI":"10.1007\/s10278-026-02227-0","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T15:02:00Z","timestamp":1788188520000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Acquisition Time\u2013Specific Deep Learning\u2013Guided Image Quality Restoration in Accelerated I-123 DaTSCAN Brain SPECT on a Ring-Shaped CZT-Based Camera"],"prefix":"10.1007","author":[{"given":"Yazdan","family":"Salimi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zahra","family":"Mansouri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gregory","family":"Mathoux","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ines","family":"Castarede","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valentina","family":"Garibotto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7559-5297","authenticated-orcid":false,"given":"Habib","family":"Zaidi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"2227_CR1","doi-asserted-by":"crossref","unstructured":"Jreige M, Kurian GK, Perriraz J, Potheegadoo J, Bernasconi F, Stampacchia S, et al. The diagnostic performance of functional dopaminergic scintigraphic imaging in the diagnosis of dementia with Lewy bodies: an updated systematic review. Eur J Nucl Med Mol Imaging. 2023;50(7):1988\u20132035.","DOI":"10.1007\/s00259-023-06154-y"},{"key":"2227_CR2","doi-asserted-by":"crossref","unstructured":"Morbelli S, Esposito G, Arbizu J, Barthel H, Boellaard R, Bohnen NI, et al. EANM practice guideline\/SNMMI procedure standard for dopaminergic imaging in Parkinsonian syndromes 1.0. Eur J Nucl Med Mol Imaging. 2020;47(8):1885\u2013912.","DOI":"10.1007\/s00259-020-04817-8"},{"key":"2227_CR3","doi-asserted-by":"crossref","unstructured":"Cecchin D, Zorz A, Lhommel R, Flaus A, Tylski P, Mathoux G, et al. An European consensus on [(123)I]Ioflupane acquisition and reconstruction using 3D CZT SPECT\/CT. EJNMMI Phys. 2025.","DOI":"10.1186\/s40658-025-00830-8"},{"key":"2227_CR4","doi-asserted-by":"crossref","unstructured":"Willowson KP, Bailey DL. Evolving SPECT-CT technology. Br J Radiol. 2024:in press.","DOI":"10.1093\/bjr\/tqae200"},{"issue":"4","key":"2227_CR5","doi-asserted-by":"publisher","first-page":"1656","DOI":"10.1002\/mp.14024","volume":"47","author":"H Arabi","year":"2020","unstructured":"Arabi H, Zaidi H. Spatially guided nonlocal mean approach for denoising of PET images. Med Phys. 2020;47(4):1656\u201369.","journal-title":"Med Phys."},{"key":"2227_CR6","doi-asserted-by":"crossref","unstructured":"Balaji V, Song TA, Malekzadeh M, Heidari P, Dutta J. Artificial intelligence for PET and SPECT image enhancement. Journal of Nuclear Medicine: Official Publication, Society of Nuclear Medicine. 2024;65(1):4\u201312.","DOI":"10.2967\/jnumed.122.265000"},{"issue":"1","key":"2227_CR7","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/s40658-025-00724-9","volume":"12","author":"Z Cheng","year":"2025","unstructured":"Cheng Z, Chen P, Yan J. A review of state-of-the-art resolution improvement techniques in SPECT imaging. EJNMMI Phys. 2025;12(1):9.","journal-title":"EJNMMI Phys."},{"issue":"1","key":"2227_CR8","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/s40658-022-00522-7","volume":"10","author":"ID Apostolopoulos","year":"2023","unstructured":"Apostolopoulos ID, Papandrianos NI, Feleki A, Moustakidis S, Papageorgiou EI. Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies. EJNMMI Phys. 2023;10(1):6.","journal-title":"EJNMMI Phys."},{"key":"2227_CR9","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.ejmp.2021.03.008","volume":"83","author":"H Arabi","year":"2021","unstructured":"Arabi H, AkhavanAllaf A, Sanaat A, Shiri I, Zaidi H. The promise of artificial intelligence and deep learning in PET and SPECT imaging. Phys Med. 2021;83:122\u201337.","journal-title":"Phys Med."},{"key":"2227_CR10","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. Advances in Neural Information Processing Systems. 2017;30."},{"key":"2227_CR11","doi-asserted-by":"crossref","unstructured":"Salimi Y, Shiri I, Mansouri Z, Zaidi H. Development and validation of fully automated robust deep learning models for multi-organ segmentation from whole-body CT images. Physica Medica: European Journal of Medical Physics. 2025;130.","DOI":"10.1016\/j.ejmp.2025.104911"},{"issue":"5","key":"2227_CR12","doi-asserted-by":"publisher","first-page":"723","DOI":"10.1007\/s11547-025-01989-x","volume":"130","author":"Y Salimi","year":"2025","unstructured":"Salimi Y, Mansouri Z, Sun C, Sanaat A, Yazdanpanah M, Shooli H, et al. Deep learning-based segmentation of ultra-low-dose CT images using an optimized nnU-Net model. Radiol Med. 2025;130(5):723\u201339.","journal-title":"Radiol Med."},{"issue":"3","key":"2227_CR13","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1007\/s10278-017-0037-8","volume":"31","author":"Z Yaniv","year":"2018","unstructured":"Yaniv Z, Lowekamp BC, Johnson HJ, Beare R. SimpleITK Image-Analysis Notebooks: a Collaborative Environment for Education and Reproducible Research. Journal of Digital Imaging. 2018;31(3):290\u2013303.","journal-title":"Journal of Digital Imaging."},{"key":"2227_CR14","doi-asserted-by":"crossref","unstructured":"Zamir SW, Arora A, Khan S, Hayat M, Khan FS, Yang M-H, editors. Restormer: Efficient transformer for high-resolution image restoration. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition; 2022.","DOI":"10.1109\/CVPR52688.2022.00564"},{"issue":"5","key":"2227_CR15","doi-asserted-by":"publisher","first-page":"1215","DOI":"10.1007\/s00259-023-06558-w","volume":"51","author":"A Bahloul","year":"2024","unstructured":"Bahloul A, Verger A, Lamash Y, Roth N, Dari D, Marie PY, et al. Ultra-fast whole-body bone tomoscintigraphies achieved with a high-sensitivity 360 degrees CZT camera and a dedicated deep-learning noise reduction algorithm. Eur J Nucl Med Mol Imaging. 2024;51(5):1215\u201320.","journal-title":"Eur J Nucl Med Mol Imaging."},{"issue":"1","key":"2227_CR16","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1186\/s12880-024-01422-1","volume":"24","author":"N Qi","year":"2024","unstructured":"Qi N, Pan B, Meng Q, Yang Y, Ding J, Yuan Z, et al. Clinical performance of deep learning-enhanced ultrafast whole-body scintigraphy in patients with suspected malignancy. BMC Med Imaging. 2024;24(1):236.","journal-title":"BMC Med Imaging."},{"issue":"1","key":"2227_CR17","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1186\/s40658-022-00472-0","volume":"9","author":"B Pan","year":"2022","unstructured":"Pan B, Qi N, Meng Q, Wang J, Peng S, Qi C, et al. Ultra high speed SPECT bone imaging enabled by a deep learning enhancement method: a proof of concept. EJNMMI Phys. 2022;9(1):43.","journal-title":"EJNMMI Phys."},{"issue":"supplement 2","key":"2227_CR18","volume":"65","author":"Y Salimi","year":"2024","unstructured":"Salimi Y, Sanaat A, Mansouri Z, Teixeira E, Zaidi H. Deep learning-guided improvement of brain SPECT image quality on CZT-based camera. Journal of Nuclear Medicine. 2024;65(supplement 2):241750.","journal-title":"Journal of Nuclear Medicine."},{"key":"2227_CR19","unstructured":"Salimi Y, Mansouri Z, Sanaat A, Zaidi H, editors. Fast Brain SPECT Imaging: Leveraging Cross-Tracer Transfer Learning for deep learning Denoising of 99m Tc-ECD Images. Eur J Nucl Med Mol Imaging, 2024;51(Suppl. 1):S781."},{"issue":"7","key":"2227_CR20","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1109\/TRPMS.2024.3398360","volume":"8","author":"H Wang","year":"2024","unstructured":"Wang H, Jiang H, Chen G, Du Y, Lu Z, Hu Z, et al. Deep-Learning-Based Cross-Modality Striatum Segmentation for Dopamine Transporter SPECT in Parkinson\u2019s Disease. Ieee T Radiat Plasma. 2024;8(7):752\u201361.","journal-title":"Ieee T Radiat Plasma."},{"key":"2227_CR21","doi-asserted-by":"crossref","unstructured":"Ishiwata Y, Horie K, Aritome K, Aoki R, Iizuka H, Aso S, et al. Accelerating imaging: deep learning for enhanced 123I-ioflupane SPECT efficiency. Japanese Journal of Radiology. 2025.","DOI":"10.1007\/s11604-025-01933-z"},{"issue":"1","key":"2227_CR22","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1186\/s40658-024-00655-x","volume":"11","author":"E Wikberg","year":"2024","unstructured":"Wikberg E, Essen MV, Ryden T, Svensson J, Gjertsson P, Bernhardt P. Improvements of (177)Lu SPECT images from sparsely acquired projections by reconstruction with deep-learning-generated synthetic projections. EJNMMI Phys. 2024;11(1):53.","journal-title":"EJNMMI Phys."},{"issue":"6","key":"2227_CR23","doi-asserted-by":"publisher","first-page":"2638","DOI":"10.1002\/mp.13517","volume":"46","author":"J Schaefferkoetter","year":"2019","unstructured":"Schaefferkoetter J, Nai YH, Reilhac A, Townsend DW, Eriksson L, Conti M. Low dose positron emission tomography emulation from decimated high statistics: A clinical validation study. Med Phys. 2019;46(6):2638\u201345.","journal-title":"Med Phys."}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02227-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-026-02227-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02227-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T04:21:07Z","timestamp":1788236467000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-026-02227-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,31]]},"references-count":23,"alternative-id":["2227"],"URL":"https:\/\/doi.org\/10.1007\/s10278-026-02227-0","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,31]]},"assertion":[{"value":"18 June 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 August 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 August 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 August 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This retrospective study was approved by the institutional ethics committee. Patients\u2019 consent was waived owing to the retrospective nature of the study.","order":1,"name":"Ethics","label":"Ethics Approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Informed consent was waived in this retrospective study.","order":2,"name":"Ethics","label":"Consent to Participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors approve the manuscript and give their consent for submission and publication.","order":3,"name":"Ethics","label":"Consent for Publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Prof. Zaidi received funding from General Electric Healthcare. The other authors have nothing to declare.","order":4,"name":"Ethics","label":"Competing Interests","group":{"name":"EthicsHeading","label":"Declarations"}}]}}