UniPET: a universal network for high-quality PET image denoising across varied dose reduction factors

Hugging Face Daily Papers Papers
pet-image denoising domain-generalization deep-learning medical-imaging image-denoising

Summary

UniPET is a universal network for PET image denoising that handles varying dose reduction factors using domain generalization and region-aware learning, achieving state-of-the-art performance.

Most existing deep learning-based PET image denoising methods assume a fixed and known dose reduction factor (DRF) for low-dose PET images. However, these methods encounter significant performance degradation when the DRF varies beyond the assumed one in practical applications. To address the challenge posed by varied DRFs, several preliminary studies focus on the task of universal PET image denoising, aiming to train a universal model over low-dose data across DRFs. Nonetheless, these vanilla universal models often struggle with misaligned styles present in different DRF data, leading to the style elimination issue with a significant over-smoothing effect. To deal with this issue, we innovatively introduce domain generalization to PET image denoising and propose a universal PET image denoising network (UniPET) to achieve high-quality PET image denoising across diverse DRFs. UniPET comprises two primary innovations: a style alignment network (SAN) and a region-aware learning strategy (RALS). Specifically, SAN utilizes style alignment techniques derived from domain generalization to align and recover styles across different DRFs, ensuring the model's generalizability across various DRFs while effectively preserving styles. Furthermore, to enhance style recovery, RALS distinguishes between flat and stylized regions, exclusively conducting adversarial learning on the latter, thereby more effectively guiding the model's focus towards learning stylized regions. It is demonstrated that our proposed UniPET can adaptively recover different DRF styles and achieve high-quality PET image denoising across DRFs. Comprehensive experiments show that UniPET exhibits comparable performance to individual DRF-specific models at specific DRFs and realizes state-of-the-art performance in universal PET image denoising quantitatively, perceptually, and clinically.
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Paper page - UniPET: a universal network for high-quality PET image denoising across varied dose reduction factors

Source: https://huggingface.co/papers/2606.11131

Abstract

A universal PET image denoising framework addresses variability in dose reduction factors through domain generalization techniques and region-aware learning strategies.

Most existing deep learning-based PET image denoising methods assume a fixed and knowndose reduction factor(DRF) for low-dose PET images. However, these methods encounter significant performance degradation when the DRF varies beyond the assumed one in practical applications. To address the challenge posed by varied DRFs, several preliminary studies focus on the task ofuniversal PET image denoising, aiming to train a universal model over low-dose data across DRFs. Nonetheless, these vanilla universal models often struggle with misaligned styles present in different DRF data, leading to thestyle elimination issuewith a significantover-smoothing effect. To deal with this issue, we innovatively introducedomain generalizationto PET image denoising and propose auniversal PET image denoisingnetwork (UniPET) to achieve high-quality PET image denoising across diverse DRFs. UniPET comprises two primary innovations: astyle alignment network(SAN) and aregion-aware learning strategy(RALS). Specifically, SAN utilizes style alignment techniques derived fromdomain generalizationto align and recover styles across different DRFs, ensuring the model’s generalizability across various DRFs while effectively preserving styles. Furthermore, to enhance style recovery, RALS distinguishes between flat and stylized regions, exclusively conductingadversarial learningon the latter, thereby more effectively guiding the model’s focus towards learning stylized regions. It is demonstrated that our proposed UniPET can adaptively recover different DRF styles and achieve high-quality PET image denoising across DRFs. Comprehensive experiments show that UniPET exhibits comparable performance to individual DRF-specific models at specific DRFs and realizes state-of-the-art performance inuniversal PET image denoisingquantitatively, perceptually, and clinically.

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