Contrast-Free Microvascular and Functional Brain Imaging by Sparse Deconvolution of Ultrafast Power Doppler

Ultrafast power Doppler imaging combined with singular value decomposition (SVD) clutter filtering has become a standard approach for label-free microvascular ultrasound, enabling the visualization of small vessels without microbubble contrast agents. In the absence of contrast, however, SVD filtered Doppler images remain limited by the blur of the imaging system point spread function (PSF) and by a residual noise floor that reduces sensitivity at depth, which together hinder the resolution of fine microvasculature. Here we establish a sparse deconvolution framework to SVD filtered ultrafast power Doppler images. Each Doppler frame is processed in two cascaded stages: a Split-Bregman optimization that solves a regularized least-squares problem combining a sparsity prior and a Hessian continuity prior, followed by an accelerated Richardson Lucy deconvolution with an estimated system PSF. We first validated the framework on a simulation phantom with known ground truth, and then evaluated the framework on in vivo rat-brain plane-wave acquisitions obtained with a Verasonics Vantage system and a 15-MHz linear array. Compared to conventional SVD power Doppler, sparse deconvolution improved the resolution by around 4 and 8 times to lambda/2 and lambda/4, in the lateral and axial directions respectively. We further show that decomposing the Doppler signal into velocity bands before deconvolution disentangles slow and fast flow and yields a velocity-resolved microvascular map. Finally, applying the same framework to task-evoked functional ultrasound, we show that sparse deconvolution preserves the stimulus-locked cerebral-blood-volume response measured by conventional functional ultrasound while sharpening the corresponding activation map from a diffuse cortical region to discrete penetrating vessels. These results indicate that the sparsity-prior super-resolution principles established in label-free ultrafast Doppler ultrasound, and that sparse deconvolution can serve as a practical, contrast-free post-processing front-end for super-resolution microvascular and functional imaging.