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There is a need for an objective method for evaluation of vessel contrast that can be automatically and systematically applied to large sets of CT\u00a0exams.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>The purpose of this work was to develop a method to automatically segment and measure attenuation Hounsfield Unit (HU) in the portal vein (PV) in contrast\u2010enhanced abdomen CT\u00a0examinations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Input CT images were processed by a vessel enhancing filter to determine candidate PV segmentations. Multiple machine learning (ML) classifiers were evaluated for classifying a segmentation as corresponding to the PV based on segmentation shape, location, and intensity features. A public data set of 82 contrast\u2010enhanced abdomen CT examinations was used to train the method. An optimal ML classifier was selected by training and tuning on 66 out of the 82 exams (80% training split) in the public data set. The method was evaluated in terms of segmentation classification accuracy and PV attenuation measurement accuracy, compared to manually determined ground truth, on a test set of the remaining 16 exams (20% test split) held out from public data set. The method was further evaluated on a separate, independently collected test set of 21\u00a0examinations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The best classifier was found to be a random forest, with a precision of 0.892 in the held\u2010out test set to correctly identify the PV from among the input candidate segmentations. The mean absolute error of the measured PV attenuation relative to ground truth manual measurement was 13.4\u00a0HU. On the independent test set, the overall precision decreased to 0.684. However, the PV attenuation measurement remained relatively accurate with a mean absolute error of 15.2\u00a0HU.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The method was shown to accurately measure PV attenuation over a large range of attenuation values, and was validated in an independently collected dataset. The method did not require time\u2010consuming manual contouring to supervise training. The method may be applied to systematic quality control of contrast\u2010enhanced CT\u00a0examinations.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.17267","type":"journal-article","created":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T16:54:43Z","timestamp":1718902483000},"page":"5954-5964","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automatic vessel attenuation measurement for quality control of contrast\u2010enhanced CT: Validation on the portal vein"],"prefix":"10.1002","volume":"51","author":[{"given":"Kevin","family":"McCoy","sequence":"first","affiliation":[{"name":"Department of Statistics Rice University Houston Texas USA"},{"name":"Department of Biostatistics, Division of Basic Science Research The University of Texas MD Anderson Cancer Center Houston Texas USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sujay","family":"Marisetty","sequence":"additional","affiliation":[{"name":"Rice University Houston Texas USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dominique","family":"Tan","sequence":"additional","affiliation":[{"name":"The University of Texas Austin Texas USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Corey T.","family":"Jensen","sequence":"additional","affiliation":[{"name":"Department of Abdominal Imaging, Division of Diagnostic Imaging The University of Texas MD Anderson Cancer Center Houston Texas USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jeffrey H.","family":"Siewerdsen","sequence":"additional","affiliation":[{"name":"Department of Imaging Physics, Division of Diagnostic Imaging The University of Texas MD Anderson Cancer Center Houston Texas USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christine B.","family":"Peterson","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Division of Basic Science Research The University of Texas MD Anderson Cancer Center Houston Texas USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Moiz","family":"Ahmad","sequence":"additional","affiliation":[{"name":"Department of Imaging Physics, Division of Diagnostic Imaging The University of Texas MD Anderson Cancer Center Houston Texas USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,6,20]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.163.2.8037023"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2018181657"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2015141991"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1093\/oxfordjournals.rpd.a033149"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2015.08.007"},{"key":"e_1_2_8_7_1","unstructured":"ACR Manual on Contrast Media.American College of Radiology;2015. 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