title: Best-Quality Vessel Identification Using Vessel Quality Measure in Multiple-Phase Coronary CT Angiography.
abstract: The detection of stenotic plaques strongly depends on the quality of the coronary arterial tree imaged with coronary CT angiography (cCTA). However, it is time consuming for the radiologist to select the best-quality vessels from the multiple-phase cCTA for interpretation in clinical practice. We are developing an automated method for selection of the best-quality vessels from coronary arterial trees in multiple-phase cCTA to facilitate radiologist's reading or computerized analysis. Our automated method consists of vessel segmentation, vessel registration, corresponding vessel branch matching, vessel quality measure (VQM) estimation, and automatic selection of best branches based on VQM. For every branch, the VQM was calculated as the average radial gradient. An observer preference study was conducted to visually compare the quality of the selected vessels. 167 corresponding branch pairs were evaluated by two radiologists. The agreement between the first radiologist and the automated selection was 76% with kappa of 0.49. The agreement between the second radiologist and the automated selection was also 76% with kappa of 0.45. The agreement between the two radiologists was 81% with kappa of 0.57. The observer preference study demonstrated the feasibility of the proposed automated method for the selection of the best-quality vessels from multiple cCTA phases.
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Lubomir
authors.identifier: 0000-0003-2069-8066
authors.initials: L
authors.lastname: Hadjiiski
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Jordan
authors.initials: J
authors.lastname: Liu
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Heang-Ping
authors.initials: HP
authors.lastname: Chan
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Chuan
authors.initials: C
authors.lastname: Zhou
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Jun
authors.initials: J
authors.lastname: Wei
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Aamer
authors.initials: A
authors.lastname: Chughtai
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Jean
authors.initials: J
authors.lastname: Kuriakose
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Prachi
authors.initials: P
authors.lastname: Agarwal
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
authors.forename: Ella
authors.initials: E
authors.lastname: Kazerooni
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001331:Automation; D000072226:Computed Tomography Angiography; D003251:Constriction, Pathologic / Q000503:physiopathology; D017023:Coronary Angiography; D003331:Coronary Vessels / Q000000981:diagnostic imaging* / Q000503:physiopathology; D004562:Electrocardiography; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D015588:Observer Variation; D010363:Pattern Recognition, Automated; D058226:Plaque, Atherosclerotic; D011857:Radiographic Image Interpretation, Computer-Assisted; D011871:Radiology; D015203:Reproducibility of Results