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

Acquah Isaak Kwesi, Issahaku Shiraz, Tagoe Samuel Nii Adu

Polish Journal of Medical Physics and Engineering

2025

Vol. 31, Iss. 1

20--38

Introduction: This systematic review evaluates various studies on deep learning algorithms for generating synthetic CT images from MRI data, focusing on challenges in image quality and accuracy in current synthetic CT generation methods. Magnetic resonance imaging (MRI) is increasingly important in clinical settings due to its detailed visualization and noninvasive nature, making it a valuable tool for advancing patient care and identifying new areas for research. Materials and Methods: In this study we conducted a thorough search across several databases to identify studies published between January 2009 and January 2024 on using deep learning to generate synthetic CT (sCT) images from MRI for radiotherapy. The review focused on peer-reviewed, English-language studies and excluded unpublished, non-English, and irrelevant studies. Data on deep learning methods, input modalities, and anatomical sites were extracted and analyzed using a result-based synthesis approach. The review categorized 84 studies by anatomical site, following PRISMA guidelines for summarizing the findings. Results: The U-Net model is the most frequently used deep learning model for generating synthetic CT images from MRI data, with 34 articles highlighting its effectiveness in capturing fine details, Conditional GANs are also widely used, while Cycle-GANs and Pix2pix are effective in image translation tasks. Significant differences in performance metrics, such as MAE and PSNR, were observed across anatomical regions and models, highlighting the variability in accuracy among different deep learning approaches. Conclusion: This review underscores the need for continued refinement and standardization in deep learning approaches for medical imaging to address variability in performance metrics across anatomical regions and models.

Visualisation of inner structures using X-Ray micro tomography

Abdullah J., Hassan H., Jia X.

Zeszyty Naukowe. Elektryka / Politechnika Łódzka

2009

z. 114

149-153

X-ray microtomography (XMT) is a non-invasive imaging technique that allows visualisation of internal features within a non-transparent object. With resolution as good as 5 µm in 2D or 3D, this technique is highly superior to conventional medical computed tomography (CT) widely used in medical sectors. X-ray tomograms map the relative density distribution within the material. Therefore, if the materials in the scanned object are known, the tomograms can be used to assess where different materials are in the structure. The Centre for Computed Tomography and Industrial Imaging (CCTII) of Agensi Nuklear Malaysia, Bangi is equipped with a XMT system: Sky Scan 1172 high-resolution desk-top microtomography system. The XMT is an ideal non-invasive means to image microstructural features of various materials.