An improved algorithm for femoropopliteal artery centerline restoration using prior knowledge of shapes and image space data.
Accurate arterial centerline extraction is essential for comprehensive visualization in CT Angiography. Time consuming manual tracking is needed when automated methods fail to track centerlines through severely diseased and occluded vessels. A previously described algorithm, Partial Vector Space Projection (PVSP), which uses vessel shape information from a database to bridge occlusions of the femoropopliteal artery, has a limited accuracy in long (>100 mm) occlusions. In this article we introduce a new algorithm, Intermediate Point Detection (IPD), which uses calcifications in the occluded artery to provide additional information about the location of the centerline to facilitate improvement in PVSP performance. It identifies calcified plaque in image space to find the most useful point within the occlusion to improve the estimate from PVSP. In this algorithm candidates for calcified plaque are automatically identified on axial CT slices in a restricted region around the estimate obtained from PVSP. A modified Canny edge detector identifies the edge of the calcified plaque and a convex polygon fit is used to find the edge of the calcification bordering the wall of the vessel. The Hough transform for circles estimates the center of the vessel on the slice, which serves as a candidate intermediate point. Each candidate is characterized by two scores based on radius and relative position within the occluded segment, and a polynomial function is constructed to define a net score representing the potential benefit of using this candidate for improving the centerline. We tested our approach in 44 femoropopliteal artery occlusions of lengths up to 398 mm in 30 patients with peripheral arterial occlusive disease. Centerlines were tracked manually by four-experts, twice each, with their mean serving as the reference standard. All occlusions were first interpolated with PVSP using a database of femoropopliteal arterial shapes obtained from a total of 60 subjects. Occlusions longer than 80 mm (N = 20) were then processed with the IPD algorithm, provided calcifications were found (N = 14). We used the maximum point-wise distance of an interpolated curve from the reference standard as our error metric. The IPD algorithm significantly reduced the average error of the initial PVSP from 2.76 to 1.86 mm (p < 0.01). The error was less than the clinically desirable 3 mm (smallest radius of the femoropopliteal artery) in 13 of 14 occlusions. The IPD algorithm achieved results within the range of the human readers in 11 of 14 cases. We conclude that the additional use of sparse but specific image space information, such as calcified atherosclerotic plaque, can be used to substantially improve the performance of a previously described knowledge-based method to restore the centerlines of femoropopliteal arterial occlusions.
Rakshe
Tejas
T
Department of Electrical Engineering, 318 Campus Drive, James H. Clark Center MC:5450, Stanford University, Stanford, California 94305, USA. rakshe@stanfordalumni.org
Fleischmann
Dominik
D
Rosenberg
Jarrett
J
Roos
Justus E
JE
Straka
Matus
M
Napel
Sandy
S
D000465:Algorithms; D001185:Artificial Intelligence; D050197:Atherosclerosis / Q000473:pathology / Q000628:therapy; D003287:Contrast Media / Q000494:pharmacology; D017023:Coronary Angiography / Q000379:methods; D005263:Femoral Artery / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional; D016011:Normal Distribution; D010363:Pattern Recognition, Automated; D019047:Phantoms, Imaging; D011150:Popliteal Artery / Q000473:pathology*; D025341:Principal Component Analysis; D014057:Tomography, X-Ray Computed / Q000379:methods
D003287:Contrast Media