{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T14:19:04Z","timestamp":1788272344600,"version":"build-2803163510"},"reference-count":39,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T00:00:00Z","timestamp":1784246400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T00:00:00Z","timestamp":1784246400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS\u20102023\u201000249223"],"award-info":[{"award-number":["RS\u20102023\u201000249223"]}],"id":[{"id":"10.13039\/501100003725","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":[[2026,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Selecting energy thresholds in photon counting CT (PCCT) is crucial for reducing noise in material decomposition. Theoretically, it can be determined by analyzing the Cram\u00e9r\u2013Rao lower bound (CRLB). However, the CRLB analysis is challenging in practice without complete knowledge of system\u00a0models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aims to develop a practical method for selecting semi\u2010optimal energy thresholds in PCCT without the system\u00a0models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We proposed approximate CRLB approaches, linear and nonlinear. It only needs to generate look\u2010up tables of measured counts using a threshold scan for reference slab phantoms, which roughly model the largest basis line integrals in the object. We also introduced total noise, which summarizes the noise in the basis line integrals and can be calculated from the look\u2010up table; one can select energy thresholds that yield the minimum total noise. The proposed methods were validated using the PcTK toolbox in simulation studies for two\u2010 and three\u2010material decomposition for various numbers of energy thresholds. The proposed methods were compared to the theoretically optimal CRLB analysis and the condition of equally distributed counts (EDC). It was also validated using a bench\u2010top PCCT system for two\u2010material decomposition with two energy\u00a0thresholds.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In simulation studies, all methods showed similar total noises for two\u2010material decomposition. For the three\u2010material decomposition, the proposed methods showed lower or similar noise levels compared to the EDC condition. All methods showed higher noise than the optimal one, except the proposed nonlinear approach, which exhibited the optimal noise level at four energy bins. In experiments, the proposed linear method achieved a near\u2010optimal performance in a two\u2010material decomposition, while the EDC condition increased noise levels by  and  for epoxy resin and CaHA,\u00a0respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed energy threshold selection methods are practical and effective: they require only a threshold scan of the reference phantoms and are computationally efficient. It outperformed the existing EDC condition in both simulation and experimental studies. It also showed a near\u2010optimal noise level across various conditions, except in the three\u2010material with three\u2010energy\u2010bin\u00a0case.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70580","type":"journal-article","created":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T16:48:45Z","timestamp":1784306925000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Approximate Cram\u00e9r\u2013Rao lower bound analyses for semi\u2010optimal energy thresholds selection in photon counting CT"],"prefix":"10.1002","volume":"53","author":[{"given":"Okkyun","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Robotics and Mechatronics Engineering Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,17]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4820371"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/abc5a5"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/54\/5\/014"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2010.2061862"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nima.2019.163132"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.70007"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3019461"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.11.S1.S12807"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16590"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2011.2142188"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2746269"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ac0cc1"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TRPMS.2020.3003251"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.2007892"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2914370"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.2512957"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.2512944"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.2549055"},{"key":"e_1_2_9_20_1","volume-title":"Principles of Mathematical Analysis","author":"Rudin W","year":"1976"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1214\/ss\/1177011926"},{"key":"e_1_2_9_22_1","volume-title":"Fundamentals of Statistical Signal Processing: Estimation Theory","author":"Kay SM","year":"1993"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-013-9622-7"},{"key":"e_1_2_9_24_1","first-page":"556","article-title":"DeepOrgan: multi\u2010level deep convolutional networks for automated pancreas segmentation","volume":"9349","author":"Roth HR","year":"2015","journal-title":"MICCAI"},{"key":"e_1_2_9_25_1","unstructured":"RothHR FaragA TurkbeyEB LuL LiuJ SummersRM.Data from pancreas\u2010CT.The Cancer Imaging Archive(2016). 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