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Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Computational Medical Image Reconstruction Techniques: A Comprehensive Review
https://orcid.org/0000-0001-7209-0437
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.
Computational Medical Image Reconstruction Techniques: A Comprehensive Review
https://orcid.org/0000-0001-7209-0437
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.
Computational Medical Image Reconstruction Techniques: A Comprehensive Review
https://orcid.org/0000-0001-7209-0437
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.
Computational Medical Image Reconstruction Techniques: A Comprehensive Review
Gothwal, Ritu (Autor:in) / Tiwari, Shailendra (Autor:in) / Shivani, Shivendra (Autor:in)
2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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