Enhancing compressed sensing and parallel imaging accelerated magnetic resonance angiography using a dual-domain projection generative adversarial network.
Authors
Affiliations (7)
Affiliations (7)
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics, East China Normal University, Shanghai, China.
- Institute of Magnetic Resonance and Molecular Imaging in Medicine, East China Normal University, Shanghai, China.
- Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
- Key Lab of Medical Engineering for Cardiovascular Disease, Ministry of Education, Beijing, China.
- RadioDynamic Medical, Shanghai, China.
- Department of Radiology, Lucile Packard Children's Hospital, Stanford, California, USA.
- Department of Radiology, Stanford University, Stanford, California, USA.
Abstract
As a key modality for cerebrovascular disease diagnosis, time-of-flight magnetic resonance angiography (TOF-MRA) necessitates high spatial resolution and sensitivity. Although acceleration of MRA is critical for reducing scan time and increasing patient throughput, achieving diagnostic-quality images at high undersampling rates remains a significant challenge. This study aims to develop a deep learning-based post-processing method that generates high-quality MRA images from highly undersampled data, improving detection sensitivity for vascular abnormalities. This prospective study employed a standard MRA scan as a baseline, followed by accelerated scans using compressed sensing (CS) with factors of 4, 6, 8, and 10, as well as sensitivity encoding (SENSE) with a factor of 4. A Dual-Domain Projection Generative Adversarial Network (DDPGAN) was proposed, taking stacked 2D patches from accelerated MRA reconstructions as input and employing a dual-headed discriminator to enhance image quality. The quantitative indicators included Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Normalized Mean Squared Error (NMSE), and Signal-to-Noise Ratio (SNR). Qualitative assessment was conducted using a 5-point reader scale focusing on image clarity and diagnostic confidence. Sixty-four patients (53 ± 16 years; 26 men) were included. DDPGAN outperformed other deep learning models across all qualitative and quantitative metrics, achieving PSNR = 31.98 dB, SSIM = 0.88, NMSE = 0.052, and SNR = 56.82 for CS×10. Qualitatively, CS×4 images improved from poor (reader1: 3.50 ± 0.50; reader2: 3.64 ± 0.48) to good/excellent (reader1: 4.64 ± 0.48; reader2: 4.93 ± 0.26), surpassing even standard scans (reader 1: 3.93 ± 0.707, reader 2: 4.71 ± 0.808). Comparative studies confirmed the robustness of DDPGAN across diverse acceleration scenarios, with significant improvements in the visualization of major cerebral vascular structures. The DDPGAN framework significantly enhances accelerated MRA image quality, enabling high detail preservation and diagnostic confidence across various acceleration scenarios. Its dual-domain design supports efficient, high-quality MRA examinations, indicating strong clinical potential particularly in the evaluation of cerebrovascular disease.