A General Framework for Inverse Problem Solving using Self-Supervised Deep Learning: Validations in Ultrasound and Photoacoustic Image Reconstruction

TL;DR

This paper introduces a self-supervised deep learning framework for solving inverse problems in medical imaging, enabling accurate reconstructions without ground truth labels, validated in ultrasound and photoacoustic imaging.

Contribution

A novel self-supervised learning framework for inverse problems that eliminates the need for ground truth labels, applicable to various medical imaging modalities.

Findings

Improved reconstruction accuracy over conventional methods.

Reduced computational time in image reconstruction tasks.

Validated across ultrasound and photoacoustic imaging applications.

Abstract

The image reconstruction process in medical imaging can be treated as solving an inverse problem. The inverse problem is usually solved using time-consuming iterative algorithms with sparsity or other constraints. Recently, deep neural network (DNN)-based methods have been developed to accelerate the inverse-problem-solving process. However, these methods typically adopt supervised learning scheme, which requires ground truths, or labels of the solutions, for training. In many applications, it would be challenging or even impossible to obtain the ground truth, such as the tissue reflectivity function in ultrasound beamforming. In this study, a general framework based on self-supervised learning (SSL) scheme is proposed to train a DNN to solve the inverse problems. In this way, the measurements can be used as both the inputs and the labels during the training of DNN. The proposed SSL method is applied to four typical linear inverse problems for validation, i.e., plane wave ultrasound and photoacoustic image reconstructions, compressed sensing-based synthetic transmit aperture dataset recovery and deconvolution in ultrasound localization microscopy. Results show that, using the proposed framework, the trained DNN can achieve improved reconstruction accuracy with reduced computational time, compared with conventional methods.