  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.
