Advancing Medical Imaging Through Generative Adversarial Networks: A Comprehensive Review and Future Prospects

Mamo, Abiy Abinet; Gebresilassie, Bealu Girma; Mukherjee, Aniruddha; Hassija, Vikas; Chamola, Vinay

doi:10.1007/s12559-024-10291-3

Cogn Comput

2024

DOI: 10.1007/s12559-024-10291-3

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Advancing Medical Imaging Through Generative Adversarial Networks: A Comprehensive Review and Future Prospects

Abiy Abinet Mamo,

Bealu Girma Gebresilassie,

Aniruddha Mukherjee

et al.

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MRI Super-Resolution Analysis via MRISR: Deep Learning for Low-Field Imaging

Li,

Yang,

Bian

et al. 2024

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This paper presents a novel MRI super-resolution analysis model, MRISR. Through the utilization of generative adversarial networks for the estimation of degradation kernels and the injection of noise, we have constructed a comprehensive dataset of high-quality paired high- and low-resolution MRI images. The MRISR model seamlessly integrates VMamba and Transformer technologies, demonstrating superior performance across various no-reference image quality assessment metrics compared with existing methodologies. It effectively reconstructs high-resolution MRI images while meticulously preserving intricate texture details, achieving a fourfold enhancement in resolution. This research endeavor represents a significant advancement in the field of MRI super-resolution analysis, contributing a cost-effective solution for rapid MRI technology that holds immense promise for widespread adoption in clinical diagnostic applications.

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MRI Super-Resolution Analysis via MRISR: Deep Learning for Low-Field Imaging

Li,

Yang,

Bian

et al. 2024

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Advancing Medical Imaging Through Generative Adversarial Networks: A Comprehensive Review and Future Prospects

Cited by 1 publication

References 126 publications

MRI Super-Resolution Analysis via MRISR: Deep Learning for Low-Field Imaging

MRI Super-Resolution Analysis via MRISR: Deep Learning for Low-Field Imaging

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