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arXiv · 2503.13257

Anatomically and Metabolically Informed Diffusion for Unified Denoising and Segmentation in Low-Count PET Imaging

Menghua Xia·Kuan-Yin Ko·Der-Shiun Wang·Ming-Kai Chen·Qiong Liu·Huidong Xie·Liang Guo·Wei Ji·Jinsong Ouyang·Reimund Bayerlein·Benjamin A. Spencer·Quanzheng Li·Ramsey D. Badawi·Georges El Fakhri·Chi Liu
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Abstract

Positron emission tomography (PET) image denoising, along with lesion and organ segmentation, are critical steps in PET-aided diagnosis. However, existing methods typically treat these tasks independently, overlooking inherent synergies between them as correlated steps in the analysis pipeline. In this work, we present the anatomically and metabolically informed diffusion (AMDiff) model, a unified framework for denoising and lesion/organ segmentation in low-count PET imaging. By integrating multi-task functionality and exploiting the mutual benefits of these tasks, AMDiff enables direct quantification of clinical metrics, such as total lesion glycolysis (TLG), from low-count inputs. The AMDiff model incorporates a semantic-informed denoiser based on diffusion strategy and a denoising-informed segmenter utilizing nnMamba architecture. The segmenter constrains denoised outputs via a lesion-organ-specific regularizer, while the denoiser enhances the segmenter by providing enriched image information through a denoising revision module. These components are connected via a warming-up mechanism to optimize multi-task interactions. Experiments on multi-vendor, multi-center, and multi-noise-level datasets demonstrate the superior performance of AMDiff.

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Menghua Xia, Kuan-Yin Ko, Der-Shiun Wang, Ming-Kai Chen, Qiong Liu, Huidong Xie, Liang Guo, Wei Ji, Jinsong Ouyang, Reimund Bayerlein, Benjamin A. Spencer, Quanzheng Li, Ramsey D. Badawi, Georges El Fakhri, Chi Liu. 2025-10-13. Anatomically and Metabolically Informed Diffusion for Unified Denoising and Segmentation in Low-Count PET Imaging. https://arxiv.org/abs/2503.13257

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