{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T13:45:29Z","timestamp":1792071929065,"version":"4.4.0"},"reference-count":36,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"vor","delay-in-days":14,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000054","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":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Liver computed tomography perfusion (CTp) imaging enables quantitative assessment of vascular dynamics but is highly susceptible to motion\u2010induced artifacts arising from breath\u2010hold variability and involuntary patient motion. These effects cause different portions of the liver to fall within the imaging field of view across time frames and introduce in\u2010plane deformation, creating substantial challenges for image registration. Misregistration between distinct regions of interest disrupts temporal signal consistency and perfusion estimation, underscoring the need for robust registration strategies tailored for Liver CTp.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop and evaluate a contour\u2010regulated automated registration framework for correcting motion artifacts and aligning time\u2010resolved images in Liver CTp series.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Forty\u2010six CTp datasets from 23 patients were analyzed; each dataset consisted of a pre\u2010contrast static scan, a post\u2010contrast high temporal resolution cine scan (59\u201379 time frames), and a post\u2010contrast low temporal resolution helical scan (8\u201310 time frames). A 2D liver auto\u2010segmentation nnU\u2010Net model was trained on 23 whole liver contours that had been manually delineated on pre\u2010contrast static images and applied to all 46 cine and helical series. The auto\u2010segmented contours were used to regulate a two\u2010stage registration pipeline consisting of affine registration followed by TransMorph\u2010based deformable registration. The models with different parameters were trained by using intensity\u2010corrected images on 36 randomly selected cases and tested on the remaining 10 cases. Performance of multiple registration model variants were evaluated against the unregistered series by using mean surface distance (MSD) and 95th percentile Hausdorff distance (HD95) on simplified 3D surfaces reconstructed from manually placed anchor points.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Liver auto\u2010segmentation achieved mean Dice similarity coefficients of 0.92 for helical scans and 0.89 for cine scans. Structure alignment was improved significantly compared with raw data after registration. The best\u2010performing model had an auto\u2010determined contour regularization weighting factor ( of 0 or 0.8, reduced the MSD from 5.1\u00a0mm in the unregistered data to 1.0\u00a0mm and HD95 from 9.7\u00a0mm to 3.0\u00a0mm in cine scans. In helical scans, MSD decreased from 3.2\u00a0mm to 1.0\u00a0mm and HD95 from 6.6\u00a0mm to 2.9\u00a0mm.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed contour\u2010regulated, two\u2010stage registration framework improved temporal alignment in dynamic Liver CTp images, providing a robust foundation for subsequent analysis and clinical assessment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70676","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T03:26:17Z","timestamp":1789529177000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Contour\u2010regulated registration framework for Liver CT perfusion images"],"prefix":"10.1002","volume":"53","author":[{"given":"Zhan","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Radiation Physics, Division of Radiation Oncology The University of Texas MD Anderson Cancer Center  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Radiation Physics, Division of Radiation Oncology The University of Texas MD Anderson Cancer Center  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinru","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiation Physics, Division of Radiation Oncology The University of Texas MD Anderson Cancer Center  Houston Texas USA"},{"name":"Department of Radiation Oncology Baylor College of Medicine  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinyong","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Radiation Physics, Division of Radiation Oncology The University of Texas MD Anderson Cancer Center  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roya","family":"Barati","sequence":"additional","affiliation":[{"name":"Department of Radiation Physics, Division of Radiation Oncology The University of Texas MD Anderson Cancer Center  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaan S.","family":"Ng","sequence":"additional","affiliation":[{"name":"Department of Abdominal Imaging, Division of Diagnostic Imaging The University of Texas MD Anderson Cancer Center  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinzhong","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Radiation Physics, Division of Radiation Oncology The University of Texas MD Anderson Cancer Center  Houston Texas USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,15]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1001\/JAMASURG.2022.7989"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/917150"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.12120186"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330\u2010012\u20102444\u2010z"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/S00261\u2010017\u20101367\u20101"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1148\/RADIOL.11110331\/\u2010\/DC1"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1186\/S12880\u2010017\u20100202\u2010Z"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.EJRAD.2005.11.044"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/S00259\u2010001\u20100700\u20106\/METRICS"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1371\/JOURNAL.PONE.0161600"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/3\u2010540\u201045468\u20103_69"},{"key":"e_1_2_9_13_1","doi-asserted-by":"crossref","unstructured":"WangH YinY WangH GongG A modified optical flow based method for registration of 4D CT data of hepatocellular carcinoma patients.Proceedings of IEEE International Conference on Virtual Environments Human\u2010Computer Interfaces and Measurement Systems VECIMS. Published online2012:21\u201025. doi:10.1109\/VECIMS.2012.6273180","DOI":"10.1109\/VECIMS.2012.6273180"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/0004\u20103702(81)90024\u20102"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1186\/S12957\u2010020\u201002037\u2010W"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1361\u20108415(98)80022\u20104"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-05530-5_2"},{"key":"e_1_2_9_18_1","doi-asserted-by":"crossref","unstructured":"BalakrishnanG ZhaoA SabuncuMR DalcaAV GuttagJ An unsupervised learning model for deformable medical image registration.Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Published online2018:9252\u20109260. doi:10.1109\/CVPR.2018.00964","DOI":"10.1109\/CVPR.2018.00964"},{"key":"e_1_2_9_19_1","volume-title":"Medical Image Computing and Computer\u2010Assisted Intervention\u2014MICCAI","author":"Ronneberger O","year":"2015"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2023.102286"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102615"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41592\u2010020\u201001008\u2010z"},{"key":"e_1_2_9_23_1","unstructured":"KingmaDP BaJL Adam: A method for stochastic optimization.3rd International Conference on Learning Representations ICLR 2015 \u2010 Conference Track Proceedings. Published online December22 2014. Accessed December 18 2025.https:\/\/arxiv.org\/pdf\/1412.6980"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12880\u2010015\u20100068\u2010x"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.PATCOG.2009.12.023"},{"key":"e_1_2_9_26_1","doi-asserted-by":"crossref","unstructured":"LiuZ LinY CaoY et\u00a0al.Swin transformer: Hierarchical vision transformer using shifted windows.Proceedings of the IEEE International Conference on Computer Vision. Published online March 25 2021:9992\u201010002. doi:10.1109\/ICCV48922.2021.00986","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1937.10503522"},{"key":"e_1_2_9_28_1","unstructured":"NemenyiP Distribution\u2010Free Multiple Comparisons.1963."},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.NEUROIMAGE.2009.06.060"},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.adro.2025.101845"},{"key":"e_1_2_9_31_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010024\u201078917\u2010y"},{"key":"e_1_2_9_32_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010025\u201018049\u2010z"},{"key":"e_1_2_9_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/978\u20103\u2010031\u201035302\u20104_39"},{"key":"e_1_2_9_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.MEDIA.2023.102925"},{"issue":"4","key":"e_1_2_9_35_1","first-page":"143","article-title":"A comparison of SIFT, PCA\u2010SIFT and SURF","volume":"3","author":"Juan L","year":"2009","journal-title":"Int J Image Process"},{"key":"e_1_2_9_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2013.2265603"},{"key":"e_1_2_9_37_1","doi-asserted-by":"crossref","unstructured":"LoweDG Object recognition from local scale\u2010invariant features.Proceedings of the Seventh IEEE International Conference on Computer Vision.1999;2:1150\u20101157. doi:10.1109\/ICCV.1999.790410","DOI":"10.1109\/ICCV.1999.790410"}],"container-title":["Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.70676","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/mp.70676","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.70676","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T12:55:31Z","timestamp":1792068931000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/mp.70676"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":36,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2026,9]]}},"alternative-id":["10.1002\/mp.70676"],"URL":"https:\/\/doi.org\/10.1002\/mp.70676","archive":["Portico"],"relation":{},"ISSN":["0094-2405","2473-4209"],"issn-type":[{"value":"0094-2405","type":"print"},{"value":"2473-4209","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"2025-12-23","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-08-19","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-09-15","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70676"}}