We examined the reliability and ease of use of a novel automated transcranial Doppler (TCD) system in comparison to a conventional TCD system. TCD ultrasound allows non-invasive monitoring of cerebral blood flow, and can predict arterial vasospasm after a subarachnoid hemorrhage (SAH). The Presto 1000 TCD system (PhysioSonics, Bellevue, WA, USA) is designed for monitoring flow through the M1 segment of the middle cerebral artery (MCA) via temporal windows. The Presto 1000 system was tested across multiple preclinical and clinical settings in parallel with a control predicate TCD system. In a phantom flow generating device, both the Presto 1000 and Spencer system (Spencer Technologies, Redmond, WA, USA) were able to detect velocities with high accuracy. In nine volunteer patients, the Presto system was able to locate the MCA in 14 out of 18 temporal windows, in an average of 12.5 s. In the SAH cohort of five patients with a total of 25 paired measurements, the mean absolute difference in flow velocities of the M1 segment, as measured by the two systems, was 17.5 cm/s. These data suggest that the Presto system offers an automated TCD that can reliably localize and detect flow of the MCA, with relative ease of use. The system carries the additional benefit of requiring minimal training for the operator, and can be used by many providers across multiple bedside settings. The mean velocities that were generated warrant further validation across an extended group of patients, and the predictive value for vasospasm should be checked against the current standard of angiography.
Vector velocity blood flow imaging gives speed and direction of blood flow at each pixel. An imaging algorithm proposed earlier [2] requires multiple angles of planewave (PW) transmissions to construct a robustly invertible model for vector velocity estimates.Here we demonstrate a vector velocity estimation approach that requires only a single planewave transmission angle. The proposed algorithm uses PW transmission and reconstruction to generate a blood motion image sequence in the B-mode flow (B-Flow) modality, at frame rates in the Doppler PRF regime. Pixel ensembles in the image sequence at point p = [x, z] and pulse t are comprised of IQ magnitude values, computed from the IQ data at each pixel p after wall filtering the ensemble. The sequence of values thus captures motion at a framerate equal to the PRF, revealing fine-scale flow dynamics as a moving texture in the blood reflectivity.Using the chain rule, spatial and temporal derivatives resulting from the space-time gradient of the image sequence couple to the texture flow velocity vector field [vx(x, z, t), vz(x, z, t)] at each pixel p and PRI t. The resulting Gauss-Markov models are solved by least squares to give the vector velocity estimates, which are formulated in the model to be constant over the estimation window.We also evaluate variants that include in the observation, lagproduct samples (autocorrelation summands) at non-zero lags, as well as instantaneous Doppler-derived axial velocity estimates.Compared to the multi-angle planewave algorithm presented in [2], this approach allows for a longer time interval for wall filtering, as the frame is not partitioned into separate segments for different planewave angles. This permits wall filters with steeper transition bands, and allows flexibility in balancing framerate and sensitivity, suggesting application to vector flow imaging of deep tissue where efficiently achieving planewave angle diversity at the target becomes difficult.Using a Philips L7-4 transducer and a Verasonics (TM) acquisition system, we evaluate single-angle PWT vector velocity imaging on a Doppler string phantom, and demonstrate it successfully on a carotid artery.
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