{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T14:17:49Z","timestamp":1776694669034,"version":"3.51.2"},"reference-count":33,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2019,11,6]],"date-time":"2019-11-06T00:00:00Z","timestamp":1572998400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100003621","name":"Ministry of Science, ICT and Future Planning","doi-asserted-by":"publisher","award":["2019R1F1A1062775"],"award-info":[{"award-number":["2019R1F1A1062775"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003621","name":"Ministry of Science, ICT and Future Planning","doi-asserted-by":"publisher","award":["2018R1C1B6008149"],"award-info":[{"award-number":["2018R1C1B6008149"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>The purpose of this study was to investigate the feasibility of two\u2010dimensional (2D) dose distribution deconvolution using convolutional neural networks (CNNs) instead of an analytical approach for an in\u2010house scintillation detector that has a detector\u2010interface artifact in the penumbra region.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Datasets of 2D dose distributions were acquired from a medical linear accelerator of Novalis Tx. The datasets comprise two different sizes of square radiation fields and 13 clinical intensity\u2010modulated radiation treatment (IMRT) plans. These datasets were divided into two datasets (training and test) to train and validate the developed network, called PenumbraNet, which is a shallow linear CNN. The PenumbraNet was trained to transform the measured dose distribution [M(x, y)] to calculated distribution [D(x, y)] by the treatment planning system. After training of the PenumbraNet was completed, the performance was evaluated using test data, which were 10\u00a0\u00d7\u00a010\u00a0cm<jats:sup>2<\/jats:sup> open field and ten clinical IMRT cases. The corrected dose distribution [C(x, y)] was evaluated against D(x, y) with 2%\/2\u00a0mm and 3%\/3\u00a0mm criteria of the gamma index for each field. The M(x, y) and deconvolved dose distribution with the analytically obtained kernel using Wiener filtering [A(x, y)] were also evaluated for comparison. In addition, we compared the performance of the shallow depth of linear PenumbraNet with that of nonlinear PenumbraNet and a deep nonlinear PenumbraNet within the same training epoch.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The mean gamma passing rates were 84.77% and 95.81% with 3%\/3\u00a0mm gamma criteria for A(x, y) and C(x, y) of the PenumbraNet, respectively. The mean gamma pass rates of nonlinear PenumbraNet and the deep depth of nonlinear PenumbraNet were 96.62%, 93.42% with 3%\/3\u00a0mm gamma criteria, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>We demonstrated the feasibility of the PenumbraNets for 2D dose distribution deconvolution. The nonlinear PenumbraNet which has the best performance improved the gamma passing rate by 11.85% from the M(x, y) at 3%\/3\u00a0mm gamma criteria.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.13869","type":"journal-article","created":{"date-parts":[[2019,10,17]],"date-time":"2019-10-17T14:34:50Z","timestamp":1571322890000},"page":"5833-5847","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Feasibility of two\u2010dimensional dose distribution deconvolution using convolution neural networks"],"prefix":"10.1002","volume":"46","author":[{"given":"Wonjoong","family":"Cheon","sequence":"first","affiliation":[{"name":"Department of Health Sciences and Technology SAIHST Sungkyunkwan University Seoul 06351 Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sung Jin","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Samsung Medical Center Seoul 06351 Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kyuseok","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Radiation Convergence Engineering Yonsei University Wonju 26493 Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Moonhee","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Health Sciences and Technology SAIHST Sungkyunkwan University Seoul 06351 Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinhyeop","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Health Sciences and Technology SAIHST Sungkyunkwan University Seoul 06351 Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kwanghyun","family":"Jo","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Samsung Medical Center Seoul 06351 Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sungkoo","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Samsung Medical Center Seoul 06351 Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyosung","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Radiation Convergence Engineering Yonsei University Wonju 26493 Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youngyih","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Health Sciences and Technology SAIHST Sungkyunkwan University Seoul 06351 Korea"},{"name":"Department of Radiation Oncology School of Medicine Samsung Medical Center Sungkyunkwan University Seoul 06351 Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2019,11,6]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3190392"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/54\/9\/015"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3284529"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/60\/16\/6177"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.1544678"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13230"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1137\/040616024"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.2128086"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/45\/3\/306"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/47\/20\/306"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/48\/6\/302"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.598182"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4944783"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.3938\/jkps.67.57"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/55\/14\/003"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13264"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2713099"},{"key":"e_1_2_8_19_1","doi-asserted-by":"crossref","unstructured":"KulkarniK LohitS TuragaP KervicheR AshokA.ReconNet: non\u2010iterative reconsturction of images from compressively sensed measurements. 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