Abstract
MRI cross-modal synthesis involves generating images from one acquisition protocol using another, offering considerable clinical value by reducing scan time while maintaining diagnostic information. This paper presents a comprehensive comparison of three state-of-the-art generative models for T1-to-T2 MRI reconstruction: Pix2Pix GAN, CycleGAN, and Variational Autoencoder (VAE). Using the BraTS 2020 dataset (11,439 training and 2,000 testing slices), we evaluate these models based on established metrics including Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). Our experiments demonstrate that all models can successfully synthesize T2 images from T1 inputs, with CycleGAN achieving the highest PSNR (32.28 dB) and SSIM (0.9008), while Pix2Pix GAN provides the lowest MSE (0.005846). The VAE, though showing lower quantitative performance (MSE: 0.006949, PSNR: 24.95 dB, SSIM: 0.6573), offers advantages in latent space representation and sampling capabilities. This comparative study provides valuable insights for researchers and clinicians selecting appropriate generative models for MRI synthesis applications based on their specific requirements and data constraints.
Abstract (translated)
MRI跨模态合成涉及使用一种获取协议生成另一种协议下的图像，这在减少扫描时间的同时保持诊断信息方面具有重要的临床价值。本文详细介绍并比较了三种最先进的T1到T2 MRI重建生成模型：Pix2Pix GAN、CycleGAN和变分自动编码器（VAE）。我们利用BraTS 2020数据集（包含11,439个训练切片和2,000个测试切片），基于包括均方误差（MSE）、峰值信噪比（PSNR）以及结构相似性指数（SSIM）在内的标准指标对这些模型进行评估。实验结果表明，所有模型都能够成功地从T1输入生成T2图像，其中CycleGAN在PSNR（32.28 dB）和SSIM（0.9008）两个指标上表现最优，而Pix2Pix GAN则提供了最低的MSE值（0.005846）。虽然VAE在量化性能方面略逊一筹（MSE: 0.006949, PSNR: 24.95 dB, SSIM: 0.6573），但在潜在空间表示和采样能力上具有优势。这项对比研究为研究人员和临床医生选择适合MRI合成应用的生成模型提供了有价值的见解，可以根据其特定需求和数据限制进行选择。
URL
https://arxiv.org/abs/2602.07068
