MRI超分辨率与RCDM/WaveMix及任务感知分割

中文摘要 AI 辅助

1.5T脑部MRI的超分辨率和质量增强通常使用图像保真度指标进行验证，尽管其目的是改善下游分析。我们研究增强是否能改善组织分割，以及针对哪些分割器有效。我们提出了一种无配对、物理引导的训练流程，用于轻量级（≤250万参数）循环卷积增强器：一个六模块随机1.5T退化算子，一个残差对抗网络，在不移动解剖结构的情况下添加扫描仪特定纹理，以及一个带有反恒等惩罚的循环一致目标，该惩罚排除了复制解。然后，我们在相同受试者的原始或增强1.5T图像上，使用相同标签和受试者级划分，从头训练U-Net、Swin-UNet和小波令牌混合分割器，涵盖三种增强器变体和两个数据集。在ABIDE上（41个留出受试者，FreeSurfer标签），增强显著改善了小波分割器（平均Dice +0.014，Wilcoxon p=3.5×10⁻⁵；脑脊液 +0.018，灰质 +0.013），显著降低了U-Net的性能（-0.008，p=5.1×10⁻⁴），而Swin-UNet保持不变。在IXI上，其标签来自FSL-FAST，增强降低了所有九种配对组合的Dice，几乎完全通过脑脊液实现；我们将此归因于标签中空间上不合理的脑脊液体素，这些体素惩罚了更平滑的预测。因此，低场MRI的增强应根据每个下游模型和可靠标签进行验证。

英文摘要

Super-resolution and quality enhancement of 1.5\,T brain MRI are normally validated with image-fidelity metrics, although their purpose is to improve downstream analysis. We study whether enhancement improves tissue segmentation, and for which segmenters. We propose an unpaired, physics-guided training pipeline for a lightweight ($\le$2.5\,M parameter) recurrent convolutional enhancer: a six-module stochastic 1.5\,T degradation operator, a residual adversarial network that adds scanner-specific texture without moving anatomy, and a cycle-consistent objective with an anti-identity penalty that rules out the copy solution. We then train U-Net, Swin-UNet and wavelet token-mixing segmenters \citep{jeevan2023wavemix} from scratch on either raw or enhanced 1.5\,T images of the same subjects, using identical labels and subject-level splits, for three enhancer variants and two datasets. On ABIDE (41 held-out subjects, FreeSurfer labels) enhancement significantly improves the wavelet segmenter (mean Dice $+0.014$, Wilcoxon $p=3.5\times10^{-5}$; CSF $+0.018$, grey matter $+0.013$), significantly degrades the U-Net ($-0.008$, $p=5.1\times10^{-4}$) and leaves Swin-UNet unchanged. On IXI, whose labels come from FSL-FAST, enhancement lowers Dice for all nine pairings, almost entirely through CSF; we trace this to spatially implausible CSF voxels in the labels that penalise smoother predictions. Enhancement of low-field MRI should therefore be validated per downstream model and against reliable labels.

发表机构

• Indian Institute of Technology Bombay（印度理工学院孟买分校）

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