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Tracking blood flow changes in the brains of neonates using angular-coherence-based power doppler
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Paper Abstract

Reliable blood flow measurements in the neonatal brain are difficult to obtain with conventional Power Doppler (PD) due to small vessel size, slow flow, and strong reverberation from the cranium. Under such imaging conditions, it is important to use long ensemble lengths and to reduce the acoustic noise in order to separate the slow-flow signal from the stationary-tissue clutter. We have recently developed the short-lag angular coherence (SLAC) beamforming method to reduce noise in the Doppler data, and used it to track blood-flow changes in the brains of neonates. SLAC suppresses the incoherent portion of the beam-summed signals and utilizes Fourier beamforming for fast processing of large Doppler ensembles. To remove stationary tissue signal from the data, we have also utilized spatiotemporal filtering prior to the SLAC processing step. The matching frames of SLAC-based PD and conventional PD were reconstructed from the same Doppler data captured on the neonatal brain vasculature over 4 cm depth. To achieve a fair comparison, the Doppler signal of each modality was normalized by its respective noise profile measured as a function of depth from a stationary speckle phantom. The SLAC images showed better delineation of small vessels, and the vessel SNR was measured to be up to 2 dB higher in SLAC images than in matching PD images. To demonstrate the quantitative aspect of SLAC-based PD, we have also created matched conventional PD and SLAC-based PD videos from the ten-second Doppler scans of neonatal brains. For the vasculature of interest, integrated pixel intensity was computed as a function of time. SLAC-based PD was able to capture changes in the cortical flow, and it closely followed the corresponding conventional PD signal for the duration of the acquisition. No external stimuli were applied during the scans. Normalized cross-correlation between the two signals was 0.991.

Paper Details

Date Published: 15 March 2019
PDF: 6 pages
Proc. SPIE 10955, Medical Imaging 2019: Ultrasonic Imaging and Tomography, 109550G (15 March 2019); doi: 10.1117/12.2513554
Show Author Affiliations
Marko Jakovljevic, Stanford Univ. School of Medicine (United States)
Byung Jason Yoon , Stanford Univ. School of Medicine (United States)
Lotfi Abou-Elkacem, Stanford Univ. School of Medicine (United States)
Erika Rubesova, Stanford Univ. School of Medicine (United States)
Jeremy Dahl, Stanford Univ. School of Medicine (United States)


Published in SPIE Proceedings Vol. 10955:
Medical Imaging 2019: Ultrasonic Imaging and Tomography
Brett C. Byram; Nicole V. Ruiter, Editor(s)

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