Medical physics · 2026
Enhancing compressed sensing and parallel imaging accelerated magnetic resonance angiography using a dual-domain projection generative adversarial network.
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Xu Libo, Bian Yueyan, Gan Kexin, Pan Boyang, Yeom Kristen W, Moseley Michael, Yang Qi, Gong Nan-Jie
Аннотация
BACKGROUND: 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.
PURPOSE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
PURPOSE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.