[Paper] U-TTT: Towards Generalizable PET Image Denoising via Test-Time Training

Source: arXiv - 2606.11032v1

Overview

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.

Key Contributions

This paper presents research in the following areas:

• cs.CV

Methodology

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Practical Implications

This research contributes to the advancement of cs.CV.

Authors

• Zhiwen Yang
• Jiayin Li
• Hao Lu
• Hui Zhang
• Zihua Wang
• Bingzheng Wei
• Yan Xu

Paper Information

• arXiv ID: 2606.11032v1
• Categories: cs.CV
• Published: June 9, 2026
• PDF: Download PDF