选错特征层会损伤脑部弥散MRI的图像质量与结构一致性
Layer Selection in Feature-Based Losses Affects Image Quality and Microstructural Consistency in Deep Learning Super-Resolution of Brain Diffusion MRI
- 用VGG16不同层的特征损失做超分辨率，深层层易出网格伪影
- 最浅层特征损失在9倍超分下仍保持与真实数据高度一致
- 对扩散MRI超分辨，浅层特征损失更适合作为损失函数
临床高分辨率弥散MRI受限于硬件和扫描时间，推动了计算超分辨率的发展。本研究探究基于特征的损失函数在深度学习超分辨率中对弥散信号一致性的影响。利用人类连接组计划的7T数据生成低/高分辨率弥散加权图像（DWI）对，训练2D UNet进行超分辨率。消融与隔离实验评估VGG16不同层作为特征损失的效果，对比基于像素的L1损失。结果表明，使用深层特征层会产生网格状伪影，且在定量各向异性（QA）和部分各向异性（FA）等扩散参数中持续存在；而使用最浅层特征损失则未出现此类伪影。该方案在9倍超分辨率下仍与真实数据高度一致。图像信噪比（SNR）和VGG16层深度共同调节伪影的出现与严重程度，强调在扩散MRI及其他应用中需谨慎选择特征层。
原文摘要 · Abstract (English)
Clinical application of high-resolution diffusion MRI is hindered by hardware limitations and prohibitive scan times, motivating computational super-resolution. This study investigates the efficacy of a feature-based loss function in preserving diffusion signal consistency in deep learning super-resolution. Using 7T data from the human connectome project to generate pairs of low- and high-resolution diffusion weighted images (DWI), we trained UNets for 2D super-resolution. Ablation and isolation studies evaluated different VGG16-layers for feature-based losses against an image-based L1 baseline. Deeper layers and combinations thereof resulted in grid-like artifacts in super-resolution DWIs, which persisted in diffusion parameters like quantitative and fractional anisotropy. No such artifacts were present when using the shallowest layer. Downstream analysis for this layer showed great consistency with the ground truth, even for 9-fold super-resolution. Image SNR and used VGG16-layer depths modulated artifact appearance and severity, mandating careful selection of contributing layers for application in and beyond diffusion MRI.
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