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A systematic review of deep learning techniques for generating synthetic CT images from MRI data Cover

A systematic review of deep learning techniques for generating synthetic CT images from MRI data

Polish Journal of Medical Physics and Engineering
Volume 31 (2025): Issue 1 (March 2025)
By: Isaac Kwesi Acquah,  Shiraz Issahaku and  Samuel Nii Adu Tagoe  
Open Access
|Apr 2025

Authors

Isaac Kwesi Acquah

ikaquah@uew.edu.gh

Department of Medical Physics, University of Ghana, Ghana
Department of Physics Education, University of Education, Winneba, Ghana

Shiraz Issahaku

Department of Medical Physics, University of Ghana, Ghana
Ghana Atomic Energy Commission, Accra, Ghana

Samuel Nii Adu Tagoe

Department of Medical Physics, University of Ghana, Ghana
Department of Radiation Oncology, National Centre for Radiotherapy and Nuclear Medicine, Korle-Bu Teaching Hospital, Ghana
DOI: https://doi.org/10.2478/pjmpe-2025-0003 | Journal eISSN: 1898-0309 | Journal ISSN: 1425-4689
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Language: English
Page range: 20 - 38
Submitted on: Sep 17, 2024
Accepted on: Jan 10, 2025
Published on: Apr 2, 2025
Published by: Polish Society of Medical Physics
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
deep learning algorithms,
synthetic CT,
magnetic resonance imaging,
radiotherapy planning,
image quality,
non-invasive imaging
Related subjects:
Medicine,
Biomedical engineering,
Physics,
Technical and applied physics,
Medical physics

© 2025 Isaac Kwesi Acquah, Shiraz Issahaku, Samuel Nii Adu Tagoe, published by Polish Society of Medical Physics
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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