Computational Medical Image Reconstruction Techniques: A Comprehensive Review

Medical image reconstruction (MIR) is the elementary way of producing an internal 3D view of the patient. MIR is inherently ill-posed, and various approaches have been proposed to address to resolve the ill-posedness. Recent inverse problem aims to create a mathematically consistent framework for merging data-driven models, particularly based on machine learning and deep learning, with domain-specific information contained in physical-analytical models. This study aims to discuss some of the significant contributions of data-driven techniques to solve the inverse problems in MIR. This paper provides a detailed survey of MIR which includes the traditional reconstruction algorithm, machine learning and deep learning-based approaches such as GAN, autoencoder, RNN, U-net, etc., to solve inverse problems, evaluation metrics, and openly available codes used in the literature. This paper also summarises the contribution of the most recent state-of-the-art methods used in MIR. The potentially attractive strategic paths for future study and fundamental problems in MIR are also discussed.

Automated summary

This study discusses the significant contributions of data-driven techniques in solving inverse problems in medical image reconstruction (MIR). It provides a detailed survey of MIR and summarizes the latest methods used in MIR.