{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T04:17:17Z","timestamp":1773893837715,"version":"3.50.1"},"reference-count":54,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T00:00:00Z","timestamp":1767139200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T00:00:00Z","timestamp":1767139200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cone\u2010beam computed tomography with multi\u2010source arrays and curved\u2010panel detectors in stationary or semi\u2010stationary configurations (sCBCT) has shown promise for compact, point\u2010of\u2010care imaging systems. Such geometries are subject to high\u2010magnitude, x\u2010ray scatter with complex spatial structure, and variation between source elements. This impedes the use of traditional projection\u2010domain scatter estimators, while reconstruction artifacts hinder volume\u2010based scatter estimators.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>In this work, we propose adaptive deep scatter estimation (ADSE), an adaptive projection\u2010domain scatter estimation technique tailored for sCBCT geometries. This technique is intended to overcome the limitations of projection\u2010 and volume\u2010domain scatter estimators, which reduce their applicability in sCBCT configurations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The scatter\u2010contaminated projections from the sCBCT geometry are transformed into a view\u2010invariant surrogate CBCT geometry. A projection\u2010domain convolutional\u2013neural network\u2010based scatter estimator and scatter fluence weighting operator are applied iteratively, causing the output to converge toward accurate scatter estimates in the surrogate geometry. The final sCBCT scatter estimates are obtained by applying inverse fluence\u2010weighting and re\u2010transformation into the sCBCT geometry.<\/jats:p>\n                    <jats:p>ADSE was assessed in the projection\u2010domain and image\u2010domain via comparison with high\u2010fidelity Monte Carlo (MC) simulations performed on in\u2010silico test phantoms derived from high\u2010quality CT scans of human heads. ADSE was compared to geometry\u2010aware DSE approach trained directly on sCBCT data (gDSE), na\u00efve projection\u2010domain scatter estimation, non\u2010iterative adaptive scatter estimation with a single fluence\u2010weighting, and iterative Monte Carlo (iMC) scatter estimation. In the projection\u2010domain, ADSE was evaluated via pixel value percentage error as a function of projection angle, and global mean absolute percentage error (MAPE). Residual scatter artifacts in the image domain were quantified as the voxelwise error compared to a scatter\u2010free ground truth.<\/jats:p>\n                    <jats:p>A physical anthropomorphic head phantom was used for experimental validation on a sCBCT test bench integrating a curved\u2010panel detector. Metrics included residual cupping, CT number non\u2010uniformity, and recovery of contrast and contrast\u2010to\u2010noise\u2010ratio (CNR) of thirteen embedded spherical inserts, with sizes ranging from 2 to 12 mm and nominal contrast ranging from \u2212329 to 871 HU.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In in\u2010silico experiments, ADSE exhibited projection\u2010domain scatter magnitude MAPE of 3.88% for nontruncated projections, compared to iMC (MAPE\u00a0=\u00a04.42%) and gDSE (MAPE\u00a0=\u00a05.13%). However, when including truncated projections, MAPE for ADSE increased to 5.18%, while iMC and gDSE remained relatively consistent at 4.32% and 5.26%, respectively.<\/jats:p>\n                    <jats:p>In physical phantom experiments, the embedded spheres in uncorrected reconstructions from the test bench exhibited 58.87% contrast loss and 84.44% CNR loss, compared to an MDCT ground truth. ADSE recovered 48.67% of contrast and 25.03% of CNR, compared to 45.87% and 16.91% using iMC (gDSE, 40.45% and 21.44%). The magnitude of cupping artifacts and CT number non\u2010uniformity decreased by 79% and 71%, respectively, compared to 85% and 53% for iMC (gDSE 114% and 59%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We present ADSE, aiming to overcome the limitations of volume\u2010domain and projection\u2010domain scatter estimators that reduce their applicability in multisource sCBCT geometries with lateral truncation, angular under\u2010sampling, and large geometrical variation between projection poses. In the presented studies, ADSE outperformed geometry\u2010aware gDSE and volumetric iMC scatter estimation methods, resulting in reduced artifacts and improved image quality. The results illustrate the feasibility of performing scatter compensation in complex stationary CBCT geometries via a combination of conventional scatter estimation methods with geometrical warping operators tailored to the specific sCBCT geometry.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70231","type":"journal-article","created":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T10:46:06Z","timestamp":1767177966000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Geometry adaptive projection\u2010domain deep scatter estimation for multi\u2010source semi\u2010stationary cone\u2010beam computed tomography"],"prefix":"10.1002","volume":"53","author":[{"given":"Thomas","family":"McSkimming","sequence":"first","affiliation":[{"name":"Medical Device Research Institute College of Science and Engineering Flinders University  Adelaide South Australia Australia"},{"name":"Department of Biomedical Engineering Johns Hopkins University  Baltimore Maryland USA"},{"name":"Micro\u2010X  Adelaide South Australia Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alejandro","family":"Lopez\u2010Montes","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Johns Hopkins University  Baltimore Maryland USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anthony","family":"Skeats","sequence":"additional","affiliation":[{"name":"Micro\u2010X  Adelaide South Australia Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chris","family":"Delnooz","sequence":"additional","affiliation":[{"name":"Micro\u2010X  Adelaide South Australia Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brian","family":"Gonzales","sequence":"additional","affiliation":[{"name":"Micro\u2010X  Adelaide South Australia Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karen J.","family":"Reynolds","sequence":"additional","affiliation":[{"name":"Medical Device Research Institute College of Science and Engineering Flinders University  Adelaide South Australia Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wojciech","family":"Zbijewski","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Johns Hopkins University  Baltimore Maryland USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Egon","family":"Perilli","sequence":"additional","affiliation":[{"name":"Medical Device Research Institute College of Science and Engineering Flinders University  Adelaide South Australia Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alejandro","family":"Sisniega","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering Johns Hopkins University  Baltimore Maryland USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,12,31]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14696"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2014.2351751"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.8.5.052114"},{"key":"e_1_2_9_5_1","first-page":"179","volume-title":"Physics of Medical Imaging","author":"Park J","year":"2022"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1364\/OE.503421"},{"key":"e_1_2_9_7_1","unstructured":"GonzalesB.White Paper | X\u2010ray Imaging: Introduction to CNT Emitter Technology and Life Testing. 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