Accelerating prostate rs-EPI DWI with deep learning: Halving scan time, enhancing image quality, and validating in vivo.
Authors
Affiliations (3)
Affiliations (3)
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1095 Jie Fang Avenue, Hankou, Wuhan 430030, PR China.
- Shanghai United Imaging Intelligence Co. Ltd., Shanghai 201807, PR China.
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1095 Jie Fang Avenue, Hankou, Wuhan 430030, PR China. Electronic address: [email protected].
Abstract
This study aims to evaluate the feasibility and effectiveness of deep learning-based super-resolution techniques to reduce scan time while preserving image quality in high-resolution prostate diffusion-weighted imaging (DWI) with readout-segmented echo-planar imaging (rs-EPI). We retrospectively and prospectively analyzed prostate rs-EPI DWI data, employing deep learning super-resolution models, particularly the Multi-Scale Self-Similarity Network (MSSNet), to reconstruct low-resolution images into high-resolution images. Performance metrics such as structural similarity index (SSIM), Peak signal-to-noise ratio (PSNR), and normalized root mean squared error (NRMSE) were used to compare reconstructed images against the high-resolution ground truth (HR<sub>GT</sub>). Additionally, we evaluated the apparent diffusion coefficient (ADC) values and signal-to-noise ratio (SNR) across different models. The MSSNet model demonstrated superior performance in image reconstruction, achieving maximum SSIM values of 0.9798, and significant improvements in PSNR and NRMSE compared to other models. The deep learning approach reduced the rs-EPI DWI scan time by 54.4 % while maintaining image quality comparable to HR<sub>GT</sub>. Pearson correlation analysis revealed a strong correlation between ADC values from deep learning-reconstructed images and the ground truth, with differences remaining within 5 %. Furthermore, all models showed significant SNR enhancement, with MSSNet performing best across most cases. Deep learning-based super-resolution techniques, particularly MSSNet, effectively reduce scan time and enhance image quality in prostate rs-EPI DWI, making them promising tools for clinical applications.