Image enhancement for accelerated MRI using a joint GAN and diffusion model framework.
Magnetic resonance imaging (MRI) is a defining feature of magnetic resonance image-guided radiotherapy (MRigRT), providing superior soft-tissue contrast compared to conventional cone beam CT (CBCT). However, conventional MRI protocols typically require prolonged acquisition times. High-speed scanning techniques implemented to improve clinical workflow efficiency often produce low-quality images, characterized by reduced resolution and blurring, which compromise the accuracy of downstream tasks. Although deep learning-based enhancement methods have shown promise, most fail to adequately correct residual errors between predicted and actual images, introducing uncertainties in clinical decisions.
This study aims to propose and evaluate a deep learning-based approach to enhance the image quality of accelerated MRI acquisitions, with the dual objectives of ensuring registration accuracy for precise tumor targeting, and shortening in-room time to minimize patient discomfort on the treatment couch.
We acquired 72 paired 3D T2-weighted MRI scans from 62 glioma patients using a 1.5T Unity MR-Linac, with each pair comprising a standard protocol scan and an accelerated protocol scan. We proposed Residual Refinement Enhancement Network (RRENet), a novel end-to-end deep learning framework that combines generative adversarial network (GAN) and diffusion model (DM) for accelerated MRI enhancement. The framework operates in a two-stage process: first, a GAN-based predictor generates the low-frequency content; subsequently, a DM-based module refines the output by estimating residual errors. To further improve performance, an additional High-frequency Separation Training Module (HSTM) was incorporated to preserve fine anatomical details. Image quality was assessed using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and root mean square error (RMSE). Clinical applicability was evaluated through rigid registration analysis with commercial software.
RRENet demonstrated superior performance compared to competing methods, achieving a 70% reduction in imaging time (vs. standard protocol) while maintaining high quality (SSIM = 92.27± $\pm$ ± $\pm$ ≤ $\le$ ≤ $\le$ ∘ $^\circ$ rotation) to planning CT, ensuring the accuracy of patient setup correction.
The proposed method can generate high-quality images from high-speed scanning sequences, reducing acquisition time while ensuring precise tumor targeting through reliable image registration.
This study aims to propose and evaluate a deep learning-based approach to enhance the image quality of accelerated MRI acquisitions, with the dual objectives of ensuring registration accuracy for precise tumor targeting, and shortening in-room time to minimize patient discomfort on the treatment couch.
We acquired 72 paired 3D T2-weighted MRI scans from 62 glioma patients using a 1.5T Unity MR-Linac, with each pair comprising a standard protocol scan and an accelerated protocol scan. We proposed Residual Refinement Enhancement Network (RRENet), a novel end-to-end deep learning framework that combines generative adversarial network (GAN) and diffusion model (DM) for accelerated MRI enhancement. The framework operates in a two-stage process: first, a GAN-based predictor generates the low-frequency content; subsequently, a DM-based module refines the output by estimating residual errors. To further improve performance, an additional High-frequency Separation Training Module (HSTM) was incorporated to preserve fine anatomical details. Image quality was assessed using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and root mean square error (RMSE). Clinical applicability was evaluated through rigid registration analysis with commercial software.
RRENet demonstrated superior performance compared to competing methods, achieving a 70% reduction in imaging time (vs. standard protocol) while maintaining high quality (SSIM = 92.27
The proposed method can generate high-quality images from high-speed scanning sequences, reducing acquisition time while ensuring precise tumor targeting through reliable image registration.