{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T23:05:13Z","timestamp":1789081513400,"version":"build-2803163510"},"reference-count":17,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2023,4,1]],"date-time":"2023-04-01T00:00:00Z","timestamp":1680307200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006955","name":"Office of Extramural Research, National Institutes of Health","doi-asserted-by":"publisher","award":["R01\u2010EB026282"],"award-info":[{"award-number":["R01\u2010EB026282"]}],"id":[{"id":"10.13039\/100006955","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006955","name":"Office of Extramural Research, National Institutes of Health","doi-asserted-by":"publisher","award":["R01\u2010EB023968"],"award-info":[{"award-number":["R01\u2010EB023968"]}],"id":[{"id":"10.13039\/100006955","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006955","name":"Office of Extramural Research, National Institutes of Health","doi-asserted-by":"publisher","award":["R21\u2010CA263660"],"award-info":[{"award-number":["R21\u2010CA263660"]}],"id":[{"id":"10.13039\/100006955","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":[[2024,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>This Special Report summarizes the 2022 AAPM Grand Challenge on Deep\u2010Learning spectral Computed Tomography (DL\u2010spectral CT) image\u00a0reconstruction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>The purpose of the challenge is to develop the most accurate image reconstruction algorithm possible for solving the inverse problem associated with a fast kilovolt switching dual\u2010energy CT scan using a three tissue\u2010map decomposition. Participants could choose to use a deep\u2010learning (DL), iterative, or a hybrid\u00a0approach.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The challenge is based on a 2D breast CT simulation, where the simulated breast phantom consists of three tissue maps: adipose, fibroglandular, and calcification distributions. The phantom specification is stochastic so that multiple realizations can be generated for DL approaches. A dual\u2010energy scan is simulated where the x\u2010ray source potential of successive views alternates between 50 and 80 kilovolts (kV). A total of 512 views are generated, yielding 256 views for each source voltage. We generate 50 and 80 kV images by use of filtered back\u2010projection (FBP) on negative logarithm processed transmission data. For participants who develop a DL approach, 1000 cases are available. Each case consists of the three 512 \u00d7 512 tissue maps, 50 and 80\u2010kV transmission data sets and their corresponding FBP images. The goal of the DL network would then be to predict the material maps from either the transmission data, FBP images, or a combination of the two. For participants developing a physics\u2010based approach, all of the required modeling parameters are made available: geometry, spectra, and tissue attenuation curves. The provided information also allows for hybrid approaches where physics is exploited as well as information about the scanned object derived from the 1000 training cases. Final testing is performed by computation of root\u2010mean\u2010square error (RMSE) for predictions on the tissue maps from 100 new\u00a0cases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Test phase submission were received from 18 research groups. Of the 18 submissions, 17 were results obtained with algorithms that involved DL. Only the second place finishing team developed a physics\u2010based image reconstruction algorithm. Both the winning and second place teams had highly accurate results where the RMSE was nearly zero to single floating point precision. Results from the top 10 also achieved a high degree of accuracy; and as a result, this special report outlines the methodology developed by each of these\u00a0groups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The DL\u2010spectral CT challenge successfully established a forum for developing image reconstruction algorithms that address an important inverse problem relevant for\u00a0spectral CT.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.16363","type":"journal-article","created":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T05:44:02Z","timestamp":1679291042000},"page":"772-785","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Report on the AAPM deep\u2010learning spectral CT Grand Challenge"],"prefix":"10.1002","volume":"51","author":[{"given":"Emil Y.","family":"Sidky","sequence":"first","affiliation":[{"name":"Department of Radiology The University of Chicago Chicago Illinois USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaochuan","family":"Pan","sequence":"additional","affiliation":[{"name":"Department of Radiology The University of Chicago Chicago Illinois USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,4]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15489"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAIT.2020.2991563"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2020.3020741"},{"key":"e_1_2_8_5_1","unstructured":"GenzelM G\u00fchringI MacdonaldJ M\u00e4rzM.Near\u2010exact recovery for tomographic inverse problems via deep learning. 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