Whitehead JF, Periyasamy S, Laeseke PF, Speidel MA, Wagner MG. Motion-compensation approach for quantitative digital subtraction angiography and its effect on
in-vivo blood velocity measurement.
J Med Imaging (Bellingham) 2024;
11:013501. [PMID:
38188936 PMCID:
PMC10765039 DOI:
10.1117/1.jmi.11.1.013501]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 11/13/2023] [Accepted: 12/18/2023] [Indexed: 01/09/2024] Open
Abstract
Purpose
Quantitative monitoring of flow-altering interventions has been proposed using algorithms that quantify blood velocity from time-resolved two-dimensional angiograms. These algorithms track the movement of contrast oscillations along a vessel centerline. Vessel motion may occur relative to a statically defined vessel centerline, corrupting the blood velocity measurement. We provide a method for motion-compensated blood velocity quantification.
Approach
The motion-compensation approach utilizes a vessel segmentation algorithm to perform frame-by-frame vessel registration and creates a dynamic vessel centerline that moves with the vasculature. Performance was evaluated in-vivo through comparison with manually annotated centerlines. The method was also compared to a previous uncompensated method using best- and worst-case static centerlines chosen to minimize and maximize centerline placement accuracy. Blood velocities determined through quantitative DSA (qDSA) analysis for each centerline type were compared through linear regression analysis.
Results
Centerline distance errors were 0.3 ± 0.1 mm relative to gold standard manual annotations. For the uncompensated approach, the best- and worst-case static centerlines had distance errors of 1.1 ± 0.6 and 2.9 ± 1.2 mm , respectively. Linear regression analysis found a high R -squared between qDSA-derived blood velocities using gold standard centerlines and motion-compensated centerlines (R 2 = 0.97 ) with a slope of 1.15 and a small offset of - 0.6 cm / s . The use of static centerlines resulted in low coefficients of determination for the best case (R 2 = 0.35 ) and worst-case (R 2 = 0.20 ) scenarios, with slopes close to zero.
Conclusions
In-vivo validation of motion-compensated qDSA analysis demonstrated improved velocity quantification accuracy in vessels with motion, addressing an important clinical limitation of the current qDSA algorithm.
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