End-to-end optimization of sparse ultrasound linear probes

TL;DR

This paper introduces an end-to-end learning framework that optimizes sparse ultrasound probe configurations and image reconstruction, achieving high-quality imaging with fewer active elements through physics-guided deep learning.

Contribution

It presents a novel differentiable optimization framework combining physical models and neural networks to design sparse ultrasound probes with maintained image quality.

Findings

Learned configurations preserve resolution with half the active elements.

The approach achieves compact, cost-efficient probe design.

It is extendable to 3D volumetric imaging.

Abstract

Ultrasound imaging faces a trade-off between image quality and hardware complexity caused by dense transducers. Sparse arrays are one popular solution to mitigate this challenge. This work proposes an end-to-end optimization framework that jointly learns sparse array configuration and image reconstruction. The framework integrates a differentiable Image Formation Model with a HARD Straight Thought Estimator (STE) selection mask, unrolled Iterative Soft-Thresholding Algorithm (ISTA) deconvolution, and a residual Convolutional Neural Network (CNN). The objective combines physical consistency (Point Spread Function (PSF) and convolutional formation model) with structural fidelity (contrast, Side-Lobe-Ratio (SLR), entropy, and row diversity). Simulations using a 3.5\,MHz probe show that the learned configuration preserves axial and lateral resolution with half of the active elements. This physics-guided, data-driven approach enables compact, cost-efficient ultrasound probe design without sacrificing image quality, and it is expandable to 3-D volumetric imaging.