3D Nephrographic Image Synthesis in CT Urography with the Diffusion Model and Swin Transformer

• URL: http://arxiv.org/abs/2502.19623v1
• Date: Wed, 26 Feb 2025 23:22:31 GMT
• Title: 3D Nephrographic Image Synthesis in CT Urography with the Diffusion Model and Swin Transformer
• Authors: Hongkun Yu, Syed Jamal Safdar Gardezi, E. Jason Abel, Daniel Shapiro, Meghan G. Lubner, Joshua Warner, Matthew Smith, Giuseppe Toia, Lu Mao, Pallavi Tiwari, Andrew L. Wentland,
• Abstract summary: The proposed approach effectively synthesizes high-quality 3D nephrographic phase images.<n>It can be used to reduce radiation dose in CTU by 33.3% without compromising image quality.
• Score: 3.8557197729550485
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Purpose: This study aims to develop and validate a method for synthesizing 3D nephrographic phase images in CT urography (CTU) examinations using a diffusion model integrated with a Swin Transformer-based deep learning approach. Materials and Methods: This retrospective study was approved by the local Institutional Review Board. A dataset comprising 327 patients who underwent three-phase CTU (mean $\pm$ SD age, 63 $\pm$ 15 years; 174 males, 153 females) was curated for deep learning model development. The three phases for each patient were aligned with an affine registration algorithm. A custom deep learning model coined dsSNICT (diffusion model with a Swin transformer for synthetic nephrographic phase images in CT) was developed and implemented to synthesize the nephrographic images. Performance was assessed using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Mean Absolute Error (MAE), and Fr\'{e}chet Video Distance (FVD). Qualitative evaluation by two fellowship-trained abdominal radiologists was performed. Results: The synthetic nephrographic images generated by our proposed approach achieved high PSNR (26.3 $\pm$ 4.4 dB), SSIM (0.84 $\pm$ 0.069), MAE (12.74 $\pm$ 5.22 HU), and FVD (1323). Two radiologists provided average scores of 3.5 for real images and 3.4 for synthetic images (P-value = 0.5) on a Likert scale of 1-5, indicating that our synthetic images closely resemble real images. Conclusion: The proposed approach effectively synthesizes high-quality 3D nephrographic phase images. This model can be used to reduce radiation dose in CTU by 33.3\% without compromising image quality, which thereby enhances the safety and diagnostic utility of CT urography.

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