Automatic Aberration Correction for Transcranial Functional and Super-Resolution Ultrasound Imaging in Rodents and Nonhuman Primates
Paul Xing, Antoine Malescot, Eric Martineau, Stephan Quessy, Ravi L. Rungta, Numa Dancause, Jean Provost

TL;DR
This paper introduces a differentiable beamforming framework that automatically corrects skull-induced aberrations in transcranial ultrasound imaging, significantly improving resolution and functional measurements in rodents and primates.
Contribution
The authors develop a novel, automated aberration correction method using differentiable beamforming optimized with angular coherence, applicable to 2D and 3D transcranial ultrasound imaging.
Findings
Enhanced resolution of mouse and primate brains in vivo.
Improved sensitivity in transcranial functional ultrasound and ULM measurements.
Effective correction of skull-induced artifacts in 3D NHP imaging.
Abstract
Skull-induced aberrations remain a major drawback of transcranial ultrasound localization microscopy (ULM), degrading sensitivity and spatial accuracy through microbubble mislocalization, false detections, and imaging artifacts, such as disconnected or duplicated vessels. Here, we present a differentiable beamforming framework for automatic aberration correction in transcranial Doppler and ULM. Our approach uses spatially distributed delay-based parameterization of the aberration that is optimized in a closed-loop manner using angular coherence as an objective function. We demonstrate robust improvements of transcranial ULM, in vivo, with enhanced resolution of both mouse and nonhuman primate (NHP) brains. We also extended differentiable beamforming to functional measurements, with improvements in the sensitivity of transcranial functional ultrasound (fUS) and ULM based hemodynamic…
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