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Med. Biol."],"published-print":{"date-parts":[[2026,6,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Our goal was to develop a simulation platform for photon-counting CT (PCCT) imaging in mouse models of head and neck squamous cell carcinoma (HNSCC).\n                    <jats:italic>Approach.<\/jats:italic>\n                    High-resolution vasculature from an energy-integrating detector micro-CT scan of a barium-enhanced mouse was transferred to the mouse whole-body (MOBY) digital phantom using affine warps. To generate tumors with contrast agent distributions derived from real data, we trained a denoising diffusion probabilistic model (DDPM) on material-decomposed iodine- and barium-enhanced mouse tumors from a prior PCCT study. DDPM synthesized tumors were fused with the vascularized MOBY to create mouse HNSCC phantoms. We improved the accuracy of images from MATLAB PCCT simulation through an adjustment that utilizes matrix multiplication and a multi-layer perceptron (MLP) trained on matched real and simulated material phantoms. We passed MOBY HNSCC phantoms into the adjusted simulation, decomposed PCCT images into water, iodine, calcium, and barium maps, and compared these outputs to the true HNSCC phantoms using quantitative metrics from iodine- and barium-enhanced regions of the tumor.\n                    <jats:italic>Main results.<\/jats:italic>\n                    DDPM synthesized tumors had similar mean iodine and barium concentrations to real tumors. In a test set phantom, our matrix multiplication and MLP adjustment substantially reduced the root mean square error of attenuation measurements in reconstructed images from PCCT simulation. In this phantom, material decomposition of the adjusted image using a real sensitivity matrix produced similar material concentrations and cross-contamination patterns to those from real PCCT imaging. Material maps from adjusted simulations of MOBY HNSCC phantoms suggest that default PCCT settings slightly overestimated iodine content, while barium content was slightly underestimated in high barium tumors and overestimated in low barium tumors.\n                    <jats:italic>Significance.<\/jats:italic>\n                    This work established a PCCT simulation pipeline with ground truth digital mouse HNSCC phantoms, enabling evaluation of PCCT performance within a calibrated imaging configuration while minimizing radiation exposure to live mice.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ae7957","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T22:56:42Z","timestamp":1780700202000},"page":"125004","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Virtual photon-counting micro-CT platform for simulation of head and neck cancer imaging in mice"],"prefix":"10.1088","volume":"71","author":[{"given":"Rohan","family":"Nadkarni","sequence":"first","affiliation":[{"name":"Duke University Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8496-4565","authenticated-orcid":true,"given":"Darin P","family":"Clark","sequence":"additional","affiliation":[{"name":"Duke University Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex J","family":"Allphin","sequence":"additional","affiliation":[{"name":"Duke University Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Qi","sequence":"additional","affiliation":[{"name":"Duke University Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9839-2414","authenticated-orcid":true,"given":"Yvonne M","family":"Mowery","sequence":"additional","affiliation":[{"name":"UPMC Hillman Cancer Center, University of Pittsburgh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William P","family":"Segars","sequence":"additional","affiliation":[{"name":"Duke University Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1850-2522","authenticated-orcid":true,"given":"Cristian T","family":"Badea","sequence":"additional","affiliation":[{"name":"Duke University Medical Center"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,6,18]]},"reference":[{"key":"pmbae7957bib1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ade94b","type":"journal-article","article-title":"Photon-counting micro-CT scanner for deep learning-enabled small animal perfusion imaging","volume":"70","author":"Allphin","year":"2025","journal-title":"Phys. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2026-01-08","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-06-05","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-06-18","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}