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2 Polish Journal of Medical Physics and Engineering

2 2025

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Deep Learning-Based Synthetic-CT Generation from MRI for Enhanced Precision in MRI-Only Radiotherapy Dose Planning

Acquah Isaac Kwesi, Issahaku Shiraz, Tagoe Samuel Nii Adu, Sackey Theophilus Akumea

Polish Journal of Medical Physics and Engineering

2025

Vol. 31, Iss. 3

219--226

Introduction: Radiotherapy aims to precisely target tumors while sparing healthy tissue, traditionally relying on CT imaging for accurate dose planning. However, CT has limitations in soft tissue contrast and exposes patients to ionizing radiation. MRI offers superior soft tissue contrast without radiation but lacks electron density information, restricting its use in dose planning. This study addresses this gap by developing deep learning models to generate pseudo-CT images from MRI, enabling MRI-only workflows in radiotherapy. Methodology: Paired MRI and CT scans from 12 subjects were processed using normalization, alignment, and masking. Four deep learning architectures (U-Net, Pix2Pix, CycleGAN, and conditional GAN (cGAN)) were trained to generate synthetic CT images from MRI data. Model performance was evaluated using metrics including mean absolute error (MAE), mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Pearson correlation coefficient (PCC). Results: Pix2Pix achieved the highest SSIM and PSNR, indicating strong structural preservation and reduced noise. It also had the lowest MAE and MSE, showing high accuracy in synthetic-CT generation. The cGAN model scored highest in PCC, highlighting its effective intensity alignment with real CT data. Statistical tests confirmed Pix2Pix’s superior performance, though CycleGAN and cGAN also showed notable results in alignment accuracy. Conclusion: Deep learning models, particularly Pix2Pix, can generate reliable pseudo-CT images from MRI, supporting MRI-only radiotherapy planning. This approach reduces radiation exposure and may streamline radiotherapy work-flows, offering a promising advance for patient-centered cancer care and MRI-only radiotherapy workflows.

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.