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Computational Medical Image Reconstruction Techniques: A Comprehensive Review

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Abstract

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.

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  1. Computer Science and Engineering Department, Thapar Institute of Engineering and Technology (TIET), Patiala, India

    Ritu Gothwal, Shailendra Tiwari & Shivendra Shivani

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Gothwal, R., Tiwari, S. & Shivani, S. Computational Medical Image Reconstruction Techniques: A Comprehensive Review. Arch Computat Methods Eng 29, 5635–5662 (2022). https://doi.org/10.1007/s11831-022-09785-w

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