{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:56:00Z","timestamp":1760147760601,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["Z220010","81727807"],"award-info":[{"award-number":["Z220010","81727807"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Tsinghua Precision Medicine Foundation","award":["Z220010","81727807"],"award-info":[{"award-number":["Z220010","81727807"]}]},{"name":"Tsinghua University Initiative Scientific Research Program","award":["Z220010","81727807"],"award-info":[{"award-number":["Z220010","81727807"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Z220010","81727807"],"award-info":[{"award-number":["Z220010","81727807"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Gamma imagers play a key role in both industrial and medical applications. Modern gamma imagers typically employ iterative reconstruction methods in which the system matrix (SM) is a key component to obtain high-quality images. An accurate SM could be acquired from an experimental calibration step with a point source across the FOV, but at a cost of long calibration time to suppress noise, posing challenges to real-world applications. In this work, we propose a time-efficient SM calibration approach for a 4\u03c0-view gamma imager with short-time measured SM and deep-learning-based denoising. The key steps include decomposing the SM into multiple detector response function (DRF) images, categorizing DRFs into multiple groups with a self-adaptive K-means clustering method to address sensitivity discrepancy, and independently training separate denoising deep networks for each DRF group. We investigate two denoising networks and compare them against a conventional Gaussian filtering method. The results demonstrate that the denoised SM with deep networks faithfully yields a comparable imaging performance with the long-time measured SM. The SM calibration time is reduced from 1.4 h to 8 min. We conclude that the proposed SM denoising approach is promising and effective in enhancing the productivity of the 4\u03c0-view gamma imager, and it is also generally applicable to other imaging systems that require an experimental calibration step.<\/jats:p>","DOI":"10.3390\/s23052689","type":"journal-article","created":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T03:57:49Z","timestamp":1677643069000},"page":"2689","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Fast and Accurate Gamma Imaging System Calibration Based on Deep Denoising Networks and Self-Adaptive Data Clustering"],"prefix":"10.3390","volume":"23","author":[{"given":"Yihang","family":"Zhu","sequence":"first","affiliation":[{"name":"Department of Engineering Physics, Tsinghua University, Beijing 100084, China"},{"name":"Key Laboratory of Particle & Radiation Imaging, Ministry of Education, Tsinghua University, Beijing 100084, China"},{"name":"Institute for Precision Medicine, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenlei","family":"Lyu","sequence":"additional","affiliation":[{"name":"Department of Engineering Physics, Tsinghua University, Beijing 100084, China"},{"name":"Key Laboratory of Particle & Radiation Imaging, Ministry of Education, Tsinghua University, Beijing 100084, China"},{"name":"Institute for Precision Medicine, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenzhuo","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Engineering Physics, Tsinghua University, Beijing 100084, China"},{"name":"Key Laboratory of Particle & Radiation Imaging, Ministry of Education, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaqiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Engineering Physics, Tsinghua University, Beijing 100084, China"},{"name":"Key Laboratory of Particle & Radiation Imaging, Ministry of Education, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2326-5760","authenticated-orcid":false,"given":"Tianyu","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Engineering Physics, Tsinghua University, Beijing 100084, China"},{"name":"Key Laboratory of Particle & Radiation Imaging, Ministry of Education, Tsinghua University, Beijing 100084, China"},{"name":"Institute for Precision Medicine, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1364\/AO.17.000337","article-title":"Coded aperture imaging with uniformly redundant arrays","volume":"17","author":"Fenimore","year":"1978","journal-title":"Appl. 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