Florian Martinez

TL;DR: Two different techniques are presented, which take advantage of fast and matrix-free formulations derived for the measurement model and its adjoint, and rely on sparsity of US images in well-chosen models to restore high-quality images from fewer raw data than state-of-the-art approaches.

TL;DR: This work proposes an approach which relies on a convolutional neural network trained exclusively on a simulated dataset for the purpose of improving images reconstructed from a single plane wave (PW) insonification, and shows that the proposed approach can be applied in real-time settings.

TL;DR: Extensive numerical evaluations demonstrate that the proposed two-step convolutional neural network (CNN)-based image reconstruction method can reconstruct images from single PWs with a quality similar to that of gold-standard synthetic aperture imaging, on a dynamic range in excess of 60 dB.

TL;DR: A convolutional neural network-based image reconstruction method combined with a speckle tracking algorithm based on cross-correlation is deployed that is capable of estimating displacements in regions where the presence of side lobe and grating lobe artifacts prevents any displacement estimation with a state-of-the-art technique that relies on conventional delay-and-sum beamforming.

TL;DR: It is demonstrated that the previously proposed convolutional neural network (CNN)-based image restoration approach, trained exclusively to improve the quality of static images, does not harm the time-coherence of consecutive frames in the context of VFI.