13 March 2018 Precision blood flow measurements in vascular networks with conservation constraints
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Abstract
In-vivo blood flow measurement, either catheter based or derived from medical images, has become increasingly used for clinical decision making. Most methods focus on a single vascular segment, catheter or simulations, due to mechanical and computational complexity. Accuracy of blood flow measurements in vascular segments are improved by considering the constraint of blood flow conservation across the whole network. Image derived blood flow measurements for individual vessels are made with a variety of techniques including ultrasound, MR, 2D DSA, and 4D-DSA. Time resolved DSA (4D) volumes are derived from 3D-DSA acquisitions and offer one such environment to measure the blood flow and respective measurement uncertainty in a vascular network automatically without user intervention. Vessel segmentation in the static DSA volume allows a mathematical description of the vessel connectivity and flow propagation direction. By constraining the allowable values of flow afforded by the measurement uncertainty and enforcing flow conservation at each junction, a reduction in the effective number of degrees of freedom in the vascular network can be made. This refines the overall measurement uncertainty in each vessel segment and provides a more robust measure of flow. Evaluations are performed with a simulated vascular network and with arterial segments in canine subjects and human renal 4D-DSA datasets. Results show a 30% reduction in flow uncertainty from a renal arterial case and a 2.5-fold improvement in flow uncertainty in some canine vessels. This method of flow uncertainty reduction may provide a more quantitative approach to treatment planning and evaluation in interventional radiology.
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Gabe Shaughnessy, Carson Hoffman, Sebastian Schafer, Paul Laeseke, Charles A. Mistretta, Charles M. Strother, "Precision blood flow measurements in vascular networks with conservation constraints ", Proc. SPIE 10576, Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling, 105762Q (13 March 2018); doi: 10.1117/12.2293637; https://doi.org/10.1117/12.2293637
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