1 research outputs found
Phase Aberration Correction for in vivo Ultrasound Localization Microscopy Using a Spatiotemporal Complex-Valued Neural Network
Ultrasound Localization Microscopy (ULM) can map microvessels at a resolution
of a few micrometers ({\mu}m). Transcranial ULM remains challenging in presence
of aberrations caused by the skull, which lead to localization errors. Herein,
we propose a deep learning approach based on recently introduced complex-valued
convolutional neural networks (CV-CNNs) to retrieve the aberration function,
which can then be used to form enhanced images using standard delay-and-sum
beamforming. Complex-valued convolutional networks were selected as they can
apply time delays through multiplication with in-phase quadrature input data.
Predicting the aberration function rather than corrected images also confers
enhanced explainability to the network. In addition, 3D spatiotemporal
convolutions were used for the network to leverage entire microbubble tracks.
For training and validation, we used an anatomically and hemodynamically
realistic mouse brain microvascular network model to simulate the flow of
microbubbles in presence of aberration. We then confirmed the capability of our
network to generalize to transcranial in vivo data in the mouse brain (n=2).
Qualitatively, vascular reconstructions using a pixel-wise predicted aberration
function included additional and sharper vessels. The spatial resolution was
evaluated by using the Fourier ring correlation (FRC). After correction, we
measured a resolution of 16.7 {\mu}m in vivo, representing an improvement of up
to 27.5 %. This work leads to different applications for complex-valued
convolutions in biomedical imaging and strategies to perform transcranial ULM