# Learning Sub-Sampling and Signal Recovery with Applications in Ultrasound Imaging

## TL;DR

This paper introduces Deep Probabilistic Sub-sampling (DPS), a deep learning approach that learns task-specific sub-sampling patterns for ultrasound imaging, enabling efficient data acquisition and accurate reconstruction in real-time.

## Contribution

The paper presents a novel deep learning framework that jointly learns sub-sampling patterns and reconstruction models, optimized for specific tasks in ultrasound imaging.

## Key findings

- Effective in-silico sparse signal recovery from Fourier measurements.
- Successful in-vivo reconstruction of anatomical images and tissue motion from sub-sampled ultrasound data.
- Framework enables practical implementation via analog-to-digital conversion or sparse array design.

## Abstract

Limitations on bandwidth and power consumption impose strict bounds on data rates of diagnostic imaging systems. Consequently, the design of suitable (i.e. task- and data-aware) compression and reconstruction techniques has attracted considerable attention in recent years. Compressed sensing emerged as a popular framework for sparse signal reconstruction from a small set of compressed measurements. However, typical compressed sensing designs measure a (non)linearly weighted combination of all input signal elements, which poses practical challenges. These designs are also not necessarily task-optimal. In addition, real-time recovery is hampered by the iterative and time-consuming nature of sparse recovery algorithms. Recently, deep learning methods have shown promise for fast recovery from compressed measurements, but the design of adequate and practical sensing strategies remains a challenge. Here, we propose a deep learning solution termed Deep Probabilistic Sub-sampling (DPS), that learns a task-driven sub-sampling pattern, while jointly training a subsequent task model. Once learned, the task-based sub-sampling patterns are fixed and straightforwardly implementable, e.g. by non-uniform analog-to-digital conversion, sparse array design, or slow-time ultrasound pulsing schemes. The effectiveness of our framework is demonstrated in-silico for sparse signal recovery from partial Fourier measurements, and in-vivo for both anatomical image and tissue-motion (Doppler) reconstruction from sub-sampled medical ultrasound imaging data.