Abstract Background T1-weighted MRI is crucial for detecting myocardial amyloidosis, where native T1 values are significantly elevated 1. However, its clinical application is limited by motion artifacts, lengthy acquisition times, and variations in imaging protocols. OS-CMR provides functional insights with embedded T1 information, offering a potential non-invasive alternative2. This study introduces an advanced Residual Generative Adversarial Network (R-GAN) to synthesize high-fidelity T1 parametric maps from OS-CMR, enabling AI-assisted myocardial amyloidosis diagnosis. Purpose This study aims to develop and validate an R-GAN model for synthesizing T1 parametric maps from OS-CMR, assess its performance against Pix2Pix, and evaluate its agreement with ground-truth T1 maps Methods Two separate datasets of 1,481 matched OS-CMR and T1-weighted images was used. Preprocessing included normalization, augmentation, and image registration. The proposed R-GAN, enhanced with residual blocks, was trained and compared to Pix2Pix using PSNR, SSIM, and PCC metrics. Validation included signal intensity analysis and Extended Phase Graph (EPG) simulations, with T1 curve evaluations conducted using Python scripts and CVI42 software. Results R-GAN with augmentation outperformed other models, achieving the highest SSIM (0.810), PSNR (16.1), and PCC (0.748). In contrast, Pix2Pix yielded lower SSIM (0.254) and PCC (0.231), with the lowest performance observed in Pix2Pix without augmentation (SSIM = 0.158, PCC = 0.03)(Table1). EPG simulation display the best-matching original T1 curve with synthesis curve which had a Pearson correlation of 0.99, indicating agreement between the synthesized and ground-truth T1 maps (Figure1) Conclusion The proposed R-GAN demonstrates the feasibility of generating high-fidelity T1 maps from OS-CMR, offering a non-invasive approach for detecting myocardial amyloidosis. The model significantly outperforms Pix2Pix, particularly with augmentation, and shows strong agreement with original T1 maps. Future work will focus on validating the approach across diverse datasets and optimizing its clinical applicabilityFigure 1.R-GAN method Table1.Metrics
lotfikazemi et al. (2025) studied this question.