提升扩散MRI图像质量，关键在于同时优化带扩散加权与非加权参考图的比值。
Enhancing Diffusion-Weighted Images (DWI) for Diffusion MRI: Is it Enough without Non-Diffusion-Weighted B=0 Reference?
- 提出比值损失，直接优化DWI与b=0图像的比率关系。
- 比值误差下降显著，扩散指标计算更准确。
- 适合需要高精度扩散参数的临床研究与超分辨率重建。
弥散磁共振成像（dMRI）对脑微结构研究至关重要，但高分辨率成像受限于采集时间与信噪比之间的权衡。传统方法仅优化扩散加权图像（DWIs），忽略其与非扩散加权（b=0）参考图像的关系。然而，表观扩散系数（ADC）、各向异性分数（FA）和平均弥散度（MD）等扩散指标依赖于每幅DWI与b=0图像的比值，这对临床观察和诊断至关重要。本研究发现，仅使用像素级均方误差（MSE）损失增强DWIs会导致生成图像与b=0图像的比值误差发散。为此，我们提出一种新型比值损失，即预测值与真实值对数比（log(DWI/b=0)）的MSE损失。实验表明，引入该损失能显著改善比值误差收敛性，降低比值MSE，轻微提升生成图像的峰值信噪比（PSNR），从而实现更好的dMRI超分辨率，并更优保留基于b=0比值的特征，提升扩散指标的可靠性。
原文摘要 · Abstract (English)
Diffusion MRI (dMRI) is essential for studying brain microstructure, but high-resolution imaging remains challenging due to the inherent trade-offs between acquisition time and signal-to-noise ratio (SNR). Conventional methods often optimize only the diffusion-weighted images (DWIs) without considering their relationship with the non-diffusion-weighted (b=0) reference images. However, calculating diffusion metrics, such as the apparent diffusion coefficient (ADC) and diffusion tensor with its derived metrics like fractional anisotropy (FA) and mean diffusivity (MD), relies on the ratio between each DWI and the b=0 image, which is crucial for clinical observation and diagnostics. In this study, we demonstrate that solely enhancing DWIs using a conventional pixel-wise mean squared error (MSE) loss is insufficient, as the error in ratio between generated DWIs and b=0 diverges. We propose a novel ratio loss, defined as the MSE loss between the predicted and ground-truth log of DWI/b=0 ratios. Our results show that incorporating the ratio loss significantly improves the convergence of this ratio error, achieving lower ratio MSE and slightly enhancing the peak signal-to-noise ratio (PSNR) of generated DWIs. This leads to improved dMRI super-resolution and better preservation of b=0 ratio-based features for the derivation of diffusion metrics.
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