{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T06:20:21Z","timestamp":1784355621209,"version":"3.55.0"},"reference-count":22,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T00:00:00Z","timestamp":1688342400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Photon counting detectors (PCDs) provide higher spatial resolution, improved contrast\u2010to\u2010noise ratio (CNR), and energy discriminating capabilities. However, the greatly increased amount of projection data in photon counting computed tomography (PCCT) systems becomes challenging to transmit through the slip ring, process, and store.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>This study proposes and evaluates an empirical optimization algorithm to obtain optimal energy weights for energy bin data compression. This algorithm is universally applicable to spectral imaging tasks including 2 and 3 material decomposition (MD) tasks and virtual monoenergetic images (VMIs). This method is simple to implement while preserving spectral information for the full range of object thicknesses and is applicable to different PCDs, for example, silicon detectors and CdTe detectors.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We used realistic detector energy response models to simulate the spectral response of different PCDs and an empirical calibration method to fit a semi\u2010empirical forward model for each PCD. We numerically optimized the optimal energy weights by minimizing the average relative Cram\u00e9r\u2013Rao lower bound (CRLB) due to the energy\u2010weighted bin compression, for MD and VMI tasks over a range of material area density  (0\u201340\u00a0g\/cm<jats:sup>2<\/jats:sup> water, 0\u20132.16\u00a0g\/cm<jats:sup>2<\/jats:sup> calcium). We used Monte Carlo simulation of a step wedge phantom and an anthropomorphic head phantom to evaluate the performance of this energy bin compression method in the projection domain and image domain, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The results show that for 2 MD, the energy bin compression method can reduce PCCT data size by 75% and 60%, with an average variance penalty of less than 17% and 3% for silicon and CdTe detectors, respectively. For 3 MD tasks with a K\u2010edge material (iodine), this method can reduce the data size by 62.5% and 40% with an average variance penalty of less than 12% and 13% for silicon and CdTe detectors, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>We proposed an energy bin compression method that is broadly applicable to different PCCT systems and object sizes, with high data compression ratio and little loss of spectral information.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16590","type":"journal-article","created":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T06:48:02Z","timestamp":1688453282000},"page":"224-238","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Empirical optimization of energy bin weights for compressing measurements with realistic photon counting x\u2010ray detectors"],"prefix":"10.1002","volume":"51","author":[{"given":"Yirong","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering Stanford University  Stanford California USA"},{"name":"Department of Radiology Stanford University  Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sen","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiology Stanford University  Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Debashish","family":"Pal","sequence":"additional","affiliation":[{"name":"Department of MICT Research GE HealthCare  Waukesha Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhye","family":"Yin","sequence":"additional","affiliation":[{"name":"Department of MICT Research GE HealthCare  Waukesha Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Norbert J.","family":"Pelc","sequence":"additional","affiliation":[{"name":"Department of Radiology Stanford University  Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adam S.","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering Stanford University  Stanford California USA"},{"name":"Department of Radiology Stanford University  Stanford California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,7,3]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/61\/4\/1572"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.6.4.043502"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2018172656"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010018\u201035888\u20101"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1097\/RLI.0000000000000418"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/abc5a5"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nima.2019.163132"},{"key":"e_1_2_8_9_1","first-page":"97831J","volume-title":"Medical Imaging 2016: Physics of Medical Imaging","author":"Shunhavanich P","year":"2016"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.811454"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2010.2061862"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.840052"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.2611555"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.7.4.043501"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/aa4e5c"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330\u2010020\u201007017\u2010y"},{"key":"e_1_2_8_17_1","first-page":"340","volume-title":"Medical Imaging 2014: Physics of Medical Imaging","author":"Rajbhandary PL","year":"2014"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.4.2.023503"},{"key":"e_1_2_8_19_1","unstructured":"KaySM Fundamentals of statistical signal processing.CERN Document Server. 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