# A Low-Rank and Joint-Sparse Model for Ultrasound Signal Reconstruction

## TL;DR

This paper introduces a low-rank and joint-sparse model for ultrasound signal reconstruction that effectively reduces data requirements while maintaining high image quality, addressing data transfer and storage bottlenecks in dense sensor arrays.

## Contribution

The paper proposes a novel low-rank and joint-sparse modeling approach that leverages inter-channel correlations for efficient ultrasound signal reconstruction from limited samples.

## Key findings

- Can recover high-quality images from 10% of the samples
- Effective exploitation of inter-channel correlations
- Reduces data transfer and storage needs

## Abstract

With the introduction of very dense sensor arrays in ultrasound (US) imaging, data transfer rate and data storage became a bottleneck in ultrasound system design. To reduce the amount of sampled channel data, we propose to use a low-rank and joint-sparse model to represent US signals and exploit the correlations between adjacent receiving channels. Results show that the proposed method is adapted to the ultrasound signals and can recover high quality image approximations from as low as 10% of the samples.