000130786 001__ 130786
000130786 005__ 20240228145553.0
000130786 0247_ $$2doi$$a10.1002/mp.12514
000130786 0247_ $$2pmid$$apmid:28801918
000130786 0247_ $$2ISSN$$a0094-2405
000130786 0247_ $$2ISSN$$a1522-8541
000130786 0247_ $$2altmetric$$aaltmetric:112552593
000130786 037__ $$aDKFZ-2017-05864
000130786 041__ $$aeng
000130786 082__ $$a610
000130786 1001_ $$0P:(DE-He78)8fb791418d563b74d5575db1e89be4aa$$aHahn, Juliane$$b0$$eFirst author$$udkfz
000130786 245__ $$aMotion compensation in the region of the coronary arteries based on partial angle reconstructions from short-scan CT data.
000130786 260__ $$aNew York, NY$$c2017
000130786 3367_ $$2DRIVER$$aarticle
000130786 3367_ $$2DataCite$$aOutput Types/Journal article
000130786 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1525780147_20626
000130786 3367_ $$2BibTeX$$aARTICLE
000130786 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000130786 3367_ $$00$$2EndNote$$aJournal Article
000130786 520__ $$aIn order to mitigate motion-induced 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-scan reconstruction while still being able to compensate for severe artifacts.We introduce a MoCo method, which we call PAMoCo, to improve the visualization of the coronary arteries of a standard coronary CT 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-volumes, reconstructed from phase-correlated short-scan data acquired during different heart cycles. Motion-compensated 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-overlapping sectors. We model motion along the coronary artery center line obtained from segmentation and the temporal dimension by a low-degree polynomial and create a dense 3D motion vector field (MVF). The parameters defining the MVF are estimated by optimizing an image artifact measuring cost function and we employ a semi-global optimization routine by re-initializing 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 V inside an ROI and measured the maximum vessel diameter d based on cross-sectional views to compare the performances of standard reconstructions obtained via filtered backprojection (FBP) and PAMoCo 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 FBP, PAMoCo reconstructions were performed and compared to best phase FBP reconstructions and another MoCo algorithm, which is based on motion artifact metrics (MAM), via visual inspection.In case of the phantom measurement we found a strong dependence of V and d on the cardiac phase in case of the FBP reconstructions. When applying PAMoCo, V and d 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 PAMoCo reconstructions compared to the standard FBP reconstructions and also the MAM images, confirming the findings of the phantom measurement.Due to the fast reconstruction of PAMoCo images and the introduction of a new motion model, we were able to re-initialize the optimization routine at pre-selected parameter sets and thereby increase the potential of the MAM algorithm. From the phantom measurement we conclude that PAMoCo performed almost equally well in all cardiac phases and suggest applying the PAMoCo algorithm for single source systems in case of patients with high or irregular heart rates.
000130786 536__ $$0G:(DE-HGF)POF3-315$$a315 - Imaging and radiooncology (POF3-315)$$cPOF3-315$$fPOF III$$x0
000130786 588__ $$aDataset connected to CrossRef, PubMed,
000130786 7001_ $$0P:(DE-HGF)0$$aBruder, Herbert$$b1
000130786 7001_ $$0P:(DE-HGF)0$$aRohkohl, Christopher$$b2
000130786 7001_ $$aAllmendinger, Thomas$$b3
000130786 7001_ $$aStierstorfer, Karl$$b4
000130786 7001_ $$aFlohr, Thomas$$b5
000130786 7001_ $$0P:(DE-He78)f288a8f92f092ddb41d52b1aeb915323$$aKachelriess, Marc$$b6$$eLast author$$udkfz
000130786 773__ $$0PERI:(DE-600)1466421-5$$a10.1002/mp.12514$$gVol. 44, no. 11, p. 5795 - 5813$$n11$$p5795 - 5813$$tMedical physics$$v44$$x0094-2405$$y2017
000130786 909CO $$ooai:inrepo02.dkfz.de:130786$$pVDB
000130786 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)8fb791418d563b74d5575db1e89be4aa$$aDeutsches Krebsforschungszentrum$$b0$$kDKFZ
000130786 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-HGF)0$$aDeutsches Krebsforschungszentrum$$b1$$kDKFZ
000130786 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-HGF)0$$aDeutsches Krebsforschungszentrum$$b2$$kDKFZ
000130786 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)f288a8f92f092ddb41d52b1aeb915323$$aDeutsches Krebsforschungszentrum$$b6$$kDKFZ
000130786 9131_ $$0G:(DE-HGF)POF3-315$$1G:(DE-HGF)POF3-310$$2G:(DE-HGF)POF3-300$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vImaging and radiooncology$$x0
000130786 9141_ $$y2017
000130786 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bMED PHYS : 2015
000130786 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000130786 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000130786 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search
000130786 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC
000130786 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bThomson Reuters Master Journal List
000130786 915__ $$0StatID:(DE-HGF)0110$$2StatID$$aWoS$$bScience Citation Index
000130786 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000130786 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000130786 915__ $$0StatID:(DE-HGF)1110$$2StatID$$aDBCoverage$$bCurrent Contents - Clinical Medicine
000130786 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences
000130786 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5
000130786 9201_ $$0I:(DE-He78)E020-20160331$$kE020$$lMedizinische Physik in der Radiologie$$x0
000130786 9201_ $$0I:(DE-He78)E025-20160331$$kE025$$lRadiologie_Legacy_$$x1
000130786 980__ $$ajournal
000130786 980__ $$aVDB
000130786 980__ $$aI:(DE-He78)E020-20160331
000130786 980__ $$aI:(DE-He78)E025-20160331
000130786 980__ $$aUNRESTRICTED