Related Experiment Video
Updated: Dec 17, 2025
Imaging and Quantification of the Hepatic Vasculature of Mice Using Ultrafast Doppler Ultrasound
Published on: July 19, 2024
Three-dimensional US for Quantification of Volumetric Blood Flow: Multisite Multisystem Results from within the
Oliver D Kripfgans1, Stephen Z Pinter1, Cristel Baiu1
1From the Department of Radiology, Michigan Medicine, University of Michigan, 1301 Catherine St, Med Sci I R3218D, Ann Arbor, MI 48109-5667 (O.D.K., S.Z.P., P.L.C., J.M.R., J.B.F.); Sun Nuclear, Middleton, Wis (C.B.); University of Washington, Seattle, Wash (M.F.B.) Department of Radiology, Mayo Clinic College of Medicine, Rochester, Minn (S.C.); Canon Medical Systems USA, Tustin, Calif (T.N.E.); Department of Ultrasound in Research and Education, Rocky Vista University, Ivins, Utah (J.G.); Department of Diagnostic Radiology, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio (N.O.); Department of Radiology, University of Alabama at Birmingham, Birmingham, Ala (M.L.R., M.E.L.); Siemens Healthcare, Issaquah, Wash (A.M.); and Department of Medical Physics, University of Wisconsin-Madison, Madison, Wis (J.A.Z.).
Abstract:
Background Quantitative blood flow (QBF) measurements that use pulsed-wave US rely on difficult-to-meet conditions. Imaging biomarkers need to be quantitative and user and machine independent. Surrogate markers (eg, resistive index) fail to quantify actual volumetric flow. Standardization is possible, but relies on collaboration between users, manufacturers, and the U.S. Food and Drug Administration. Purpose To evaluate a Quantitative Imaging Biomarkers Alliance-supported, user- and machine-independent US method for quantitatively measuring QBF. Materials and Methods In this prospective study (March 2017 to March 2019), three different clinical US scanners were used to benchmark QBF in a calibrated flow phantom at three different laboratories each. Testing conditions involved changes in flow rate (1-12 mL/sec), imaging depth (2.5-7 cm), color flow gain (0%-100%), and flow past a stenosis. Each condition was performed under constant and pulsatile flow at 60 beats per minute, thus yielding eight distinct testing conditions. QBF was computed from three-dimensional color flow velocity, power, and scan geometry by using Gauss theorem. Statistical analysis was performed between systems and between laboratories. Systems and laboratories were anonymized when reporting results. Results For systems 1, 2, and 3, flow rate for constant and pulsatile flow was measured, respectively, with biases of 3.5% and 24.9%, 3.0% and 2.1%, and -22.1% and -10.9%. Coefficients of variation were 6.9% and 7.7%, 3.3% and 8.2%, and 9.6% and 17.3%, respectively. For changes in imaging depth, biases were 3.7% and 27.2%, -2.0% and -0.9%, and -22.8% and -5.9%, respectively. Respective coefficients of variation were 10.0% and 9.2%, 4.6% and 6.9%, and 10.1% and 11.6%. For changes in color flow gain, biases after filling the lumen with color pixels were 6.3% and 18.5%, 8.5% and 9.0%, and 16.6% and 6.2%, respectively. Respective coefficients of variation were 10.8% and 4.3%, 7.3% and 6.7%, and 6.7% and 5.3%. Poststenotic flow biases were 1.8% and 31.2%, 5.7% and -3.1%, and -18.3% and -18.2%, respectively. Conclusion Interlaboratory bias and variation of US-derived quantitative blood flow indicated its potential to become a clinical biomarker for the blood supply to end organs. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Forsberg in this issue.
