{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T18:27:22Z","timestamp":1788546442603,"version":"build-2803163510"},"reference-count":31,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2017,9,22]],"date-time":"2017-09-22T00:00:00Z","timestamp":1506038400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"name":"Siemens Healthcare GmbH, Forchheim, Germany"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2017,11]]},"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>In order to mitigate motion\u2010induced artifacts, several motion compensation (MoCo) methods have been developed, which are either able to (a) compensate for severe artifacts, but utilize the data for the reconstruction of several cardiac phases, or (b) improve image quality of a single reconstruction with only moderate motion artifacts. We propose a method combining both benefits: dose efficiency by utilizing only the data needed for a single short\u2010scan reconstruction while still being able to compensate for severe artifacts.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We introduce a MoCo method, which we call <jats:styled-content style=\"fixed-case\">PAM<\/jats:styled-content>oCo, to improve the visualization of the coronary arteries of a standard coronary <jats:styled-content style=\"fixed-case\">CT<\/jats:styled-content> angiography exam by reducing motion artifacts. As a first step, we segment a region of interest covering a chosen coronary artery. We subdivide a volume covering the whole heart into several stacks, which are sub\u2010volumes, reconstructed from phase\u2010correlated short\u2010scan data acquired during different heart cycles. Motion\u2010compensated reconstruction is performed for each stack separately, based on partial angle reconstructions, which are derived by dividing the data corresponding to the segmented stack volume into several double\u2010overlapping sectors. We model motion along the coronary artery center line obtained from segmentation and the temporal dimension by a low\u2010degree polynomial and create a dense 3D motion vector field (<jats:styled-content style=\"fixed-case\">MVF<\/jats:styled-content>). The parameters defining the <jats:styled-content style=\"fixed-case\">MVF<\/jats:styled-content> are estimated by optimizing an image artifact measuring cost function and we employ a semi\u2010global optimization routine by re\u2010initializing the optimization multiple times. The algorithm was evaluated on the basis of a phantom measurement and clinical data. For the phantom measurement an artificial vessel equipped with calcified lesions mounted on a moving robot arm was measured, where typical coronary artery motion patterns for 70 bpm and 90 bpm have been applied. For analysis, we calculated the calcified volume <jats:italic>V<\/jats:italic> inside an <jats:styled-content style=\"fixed-case\">ROI<\/jats:styled-content> and measured the maximum vessel diameter <jats:italic>d<\/jats:italic> based on cross\u2010sectional views to compare the performances of standard reconstructions obtained via filtered backprojection (<jats:styled-content style=\"fixed-case\">FBP<\/jats:styled-content>) and <jats:styled-content style=\"fixed-case\">PAM<\/jats:styled-content>oCo reconstructions between 20% and 80% of the cardiac cycle. Further, the new algorithm was applied to six clinical cases of patients with heart rates between 50 bpm and 74 bpm. Standard <jats:styled-content style=\"fixed-case\">FBP<\/jats:styled-content>,<jats:styled-content style=\"fixed-case\"> PAM<\/jats:styled-content>oCo reconstructions were performed and compared to best phase <jats:styled-content style=\"fixed-case\">FBP<\/jats:styled-content> reconstructions and another MoCo algorithm, which is based on motion artifact metrics (<jats:styled-content style=\"fixed-case\">MAM<\/jats:styled-content>), via visual inspection.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In case of the phantom measurement we found a strong dependence of <jats:italic>V<\/jats:italic> and <jats:italic>d<\/jats:italic> on the cardiac phase in case of the <jats:styled-content style=\"fixed-case\">FBP<\/jats:styled-content> reconstructions. When applying <jats:styled-content style=\"fixed-case\">PAM<\/jats:styled-content>oCo, <jats:italic>V<\/jats:italic> and <jats:italic>d<\/jats:italic> became almost constant due to a better discrimination from calcium to vessel and water background and values close to the ground truth have been derived. In the clinical study we chose reconstructions showing strong motion artifacts and observed a substantially improved delineation of the coronary arteries in <jats:styled-content style=\"fixed-case\">PAM<\/jats:styled-content>oCo reconstructions compared to the standard <jats:styled-content style=\"fixed-case\">FBP<\/jats:styled-content> reconstructions and also the <jats:styled-content style=\"fixed-case\">MAM<\/jats:styled-content> images, confirming the findings of the phantom measurement.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Due to the fast reconstruction of <jats:styled-content style=\"fixed-case\">PAM<\/jats:styled-content>oCo images and the introduction of a new motion model, we were able to re\u2010initialize the optimization routine at pre\u2010selected parameter sets and thereby increase the potential of the <jats:styled-content style=\"fixed-case\">MAM<\/jats:styled-content> algorithm. From the phantom measurement we conclude that <jats:styled-content style=\"fixed-case\">PAM<\/jats:styled-content>oCo performed almost equally well in all cardiac phases and suggest applying the <jats:styled-content style=\"fixed-case\">PAM<\/jats:styled-content>oCo algorithm for single source systems in case of patients with high or irregular heart rates.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.12514","type":"journal-article","created":{"date-parts":[[2017,8,12]],"date-time":"2017-08-12T08:32:40Z","timestamp":1502526760000},"page":"5795-5813","source":"Crossref","is-referenced-by-count":31,"title":["Motion compensation in the region of the coronary arteries based on partial angle reconstructions from short\u2010scan CT data"],"prefix":"10.1002","volume":"44","author":[{"given":"Juliane","family":"Hahn","sequence":"first","affiliation":[{"name":"Siemens Healthcare GmbH Forchheim Germany"},{"name":"German Cancer Research Center Heidelberg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Herbert","family":"Bruder","sequence":"additional","affiliation":[{"name":"Siemens Healthcare GmbH Forchheim Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher","family":"Rohkohl","sequence":"additional","affiliation":[{"name":"Siemens Healthcare GmbH Forchheim Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Allmendinger","sequence":"additional","affiliation":[{"name":"Siemens Healthcare GmbH Forchheim Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karl","family":"Stierstorfer","sequence":"additional","affiliation":[{"name":"Siemens Healthcare GmbH Forchheim Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Flohr","sequence":"additional","affiliation":[{"name":"Siemens Healthcare GmbH Forchheim Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marc","family":"Kachelrie\u00df","sequence":"additional","affiliation":[{"name":"German Cancer Research Center Heidelberg Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2017,9,22]]},"reference":[{"key":"e_1_2_8_2_1","first-page":"457","article-title":"In\u2010plane coronary arterial motion velocity: measurement with electron\u2010beam CT","volume":"216","author":"Achenbach S","journal-title":"Proc Radiol 2000: Card Imaging."},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4725712"},{"key":"e_1_2_8_4_1","first-page":"9783","volume-title":"Proceedings of Medical Imaging 2016: Physics of Medical Imaging","author":"Hahn J","year":"2016"},{"key":"e_1_2_8_5_1","first-page":"185","volume-title":"Proceedings of the 4th International Conference on Image Formation in X\u2010Ray Computed Tomography","author":"Hahn J","year":"2016"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2451061791"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2011.12.005"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3301600"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12410-013-9203-7"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.598453"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.1286552"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4918580"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2011.06.066"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.1624756"},{"key":"e_1_2_8_15_1","first-page":"565","volume-title":"Proceedings of the 4th International Conference on Image Formation in X\u2010Ray Computed Tomography","author":"Nett BE","year":"2016"},{"key":"e_1_2_8_16_1","unstructured":"PackJD EdicPM ClausBEH IatrouM MillerJV.Method for computed tomography motion estimation and compensation. 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