{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T03:53:49Z","timestamp":1785556429622,"version":"3.56.0"},"reference-count":40,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T00:00:00Z","timestamp":1756944000000},"content-version":"vor","delay-in-days":3,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Cascaded linear models are widely used for the development and optimization of x\u2010ray imaging systems, yet no publicly available Python implementation currently exists. We introduce CASYMIR, a flexible and open\u2010source Python package capable of modeling direct and indirect\u2010conversion x\u2010ray imaging detectors under various acquisition conditions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We employed a modular software design with generalized frequency\u2010domain expressions for each process in the detection chain, which can be implemented as serial or parallel blocks. The gain factors and other parameters are derived from the detector's characteristics, system geometry, and incident x\u2010ray spectra, all of which can be specified by the user. The signal reaching the detector is propagated throughout the detection stages by applying these process blocks, enabling the computation of the Modulation Transfer Function (MTF) and Noise Power Spectrum (NPS) at any stage of the model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Validation<\/jats:title>\n                    <jats:p>Our implementation was experimentally validated using two commercial x\u2010ray detectors: a flat\u2010panel a\u2010Se detector for digital mammography and digital breast tomosynthesis, and a flat\u2010panel scintillator (CsI) detector for dedicated breast CT. The modeled MTF had root\u2010mean\u2010square (RMS) percent errors below 6% for the a\u2010Se detector, while the normalized RMS error for the NNPS was below 3%. For the CsI detector, the RMS percent error in the MTF was 5.4%, and the normalized RMS error for the NNPS was 5.8%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Usage notes<\/jats:title>\n                    <jats:p>\n                      The CASYMIR Python package can be downloaded from\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/radboud-axti\/casymir_public\">https:\/\/github.com\/radboud\u2010axti\/casymir_public<\/jats:ext-link>\n                      , and it includes a standalone executable script suitable for modeling common commercial systems, along with an extensive README file and example files.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Potential applications<\/jats:title>\n                    <jats:p>CASYMIR is available as an open\u2010source Python package under the MIT license. Given its modular and flexible structure, it can be easily modified and integrated into other simulation\/virtual clinical trial pipelines where information about the detector's spatial resolution and noise performance is needed. The standalone version of CASYMIR may be particularly useful for running batch simulations with varying acquisition and system parameters, making it ideal for optimizing system design and acquisition techniques. Furthermore, given the package's modular structure, new processes can be implemented to simulate other detector and system designs.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.18079","type":"journal-article","created":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T07:06:58Z","timestamp":1756969618000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Software Article: A generalized cascaded linear system model implementation for x\u2010ray detectors"],"prefix":"10.1002","volume":"52","author":[{"given":"Gustavo","family":"Pacheco","sequence":"first","affiliation":[{"name":"Dept. of Medical Imaging Radboudumc  Nijmegen The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan J.","family":"Pautasso","sequence":"additional","affiliation":[{"name":"Dept. of Medical Imaging Radboudumc  Nijmegen The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Koen","family":"Michielsen","sequence":"additional","affiliation":[{"name":"Dept. of Medical Imaging Radboudumc  Nijmegen The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ioannis","family":"Sechopoulos","sequence":"additional","affiliation":[{"name":"Dept. of Medical Imaging Radboudumc  Nijmegen The Netherlands"},{"name":"Dutch Expert Centre for Screening (LRCB)  Nijmegen The Netherlands"},{"name":"Technical Medicine Centre University of Twente  Enschede The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,9,4]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16051"},{"key":"e_1_2_9_3_1","doi-asserted-by":"crossref","unstructured":"CunninghamIA YaoJ SuboticV Cascaded models and the DQE of flat\u2010panel imagers: noise aliasing secondary quantum noise and reabsorption. 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