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Evaluation of generative adversarial network-based postprocessing super-resolution for lumbar spine magnetic
Yasuo Takatsu1,2,3, Kazuki Takano4, Shohei Harada5,6
1Graduate School of Medical Sciences, Fujita Health University, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan. yasuo.takatsu@fujita-hu.ac.jp.
Physical and Engineering Sciences in Medicine
|July 29, 2026
Summary
Generative adversarial network (GAN)-based super-resolution enhances T2-weighted lumbar spine MRI quality. Enhanced super-resolution GAN (ESRGAN) shows superior performance over traditional methods, improving image details and subjective ratings.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Image Processing
Background:
- T2-weighted lumbar spine MRI is crucial for diagnosing spinal conditions.
- Current super-resolution techniques may not fully restore high-frequency details.
- Generative adversarial networks (GANs) offer potential for advanced image enhancement.
Purpose of the Study:
- To evaluate the feasibility of GAN-based super-resolution for T2-weighted lumbar spine MRI.
- To compare the performance of enhanced super-resolution GAN (ESRGAN) against traditional methods.
- To assess both objective and subjective image quality improvements.
Main Methods:
- Trained ESRGAN on downsampled T2-weighted lumbar spine MRI datasets.
- Compared ESRGAN with zero-filling, bicubic, bilinear interpolation, and EDSR.
- Assessed image quality using objective metrics (FWHM, NIPS, SSIM, PSNR, RMSE) and subjective ratings.
Main Results:
- ESRGAN demonstrated performance comparable to original high-resolution images in FWHM and NIPS.
- Interpolation methods and EDSR showed significantly inferior objective performance (P < 0.05).
- ESRGAN achieved the highest subjective image quality ratings and interrater agreement.
Conclusions:
- GAN-based super-resolution, specifically ESRGAN, effectively enhances T2-weighted lumbar spine MRI.
- ESRGAN better restores high-frequency structural information than conventional methods.
- This technique holds promise for improving diagnostic accuracy in lumbar spine MRI.
