# U-TTT：通过测试时训练实现泛化的PET图像去噪

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-09 08:00
- AIHOT 分数：47
- AIHOT 链接：https://aihot.virxact.com/items/cmq7rybmj014xslep1wxmef94
- 原文链接：https://arxiv.org/abs/2606.11032

## AI 摘要

现有深度学习模型在分布偏移下进行PET图像去噪时性能严重下降，根源在于固定参数范式无法适应测试数据的剂量水平或扫描仪类型变化。本文提出U-TTT，一种集成测试时训练（TTT）层的U形模型，通过自监督在推理时动态调整参数以适应每个测试实例的特征。U-TTT包含空间TTT（S-TTT）层和频率TTT（F-TTT）层构成的双域自适应机制，分别校正空间结构退化并抑制全局噪声频谱、恢复高频细节。实验表明，U-TTT在未见剂量水平和扫描仪类型等挑战性分布偏移下达到SOTA去噪性能与泛化能力。

## 正文

Existing deep learning models for Positron Emission Tomography (PET) image denoising often suffer from severe performance degradation under distribution shifts, fundamentally restricting their robust clinical deployment. This lack of generalization stems from the conventional paradigm of fixed-parameter models that cannot adapt to variations in test data (e.g., dose levels or scanner types) after training. To overcome this limitation and achieve robust generalization, we introduce U-TTT, a novel U-shaped model that integrates Test-Time Training (TTT) layers to dynamically adjust model parameters during inference through self-supervision, thereby adapting to the specific characteristics of each test instance. Furthermore, to comprehensively capture the complex degradations of 3D PET data, U-TTT features a dual-domain adaptation mechanism comprising a Spatial Test-Time Training (S-TTT) layer and a Frequency Test-Time Training (F-TTT) layer. The S-TTT layer captures and corrects spatial structural degradations, while the F-TTT layer suppresses global noise spectra and restores delicate high-frequency details. Extensive experiments demonstrate that U-TTT achieves state-of-the-art PET denoising performance and exhibits superior generalization under challenging distribution shifts, including both unseen dose levels and unseen scanners. Our code will be available at https://github.com/Yaziwel/U-TTT.