DECADE: A Temporally-Consistent Unsupervised Diffusion Model for Enhanced Rb-82 Dynamic Cardiac PET Image Denoising

2026-03-08

AI Summary

This work addresses the challenge of high noise levels in dynamic cardiac Rb-82 PET imaging, which arises from the isotope’s short half-life and the absence of paired clean-noisy data, hindering existing deep learning methods from simultaneously preserving temporal consistency and quantitative accuracy. To overcome this, the authors propose DECADE, an unsupervised diffusion-based denoising model that embeds temporal consistency constraints during both training and sampling, leveraging each noisy frame as its own guidance to achieve high-quality denoising across early-to-late dynamic frames. Notably, DECADE is the first method to maintain quantitative fidelity of critical physiological parameters—such as myocardial blood flow (MBF) and myocardial flow reserve (MFR)—without access to ground-truth clean images. Evaluated on Siemens Vision 450 and Biograph Vision Quadra scanners, DECADE significantly outperforms U-Net and other diffusion models in both dynamic image quality and parametric map accuracy.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Diffusion Models for Vision
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Rb-82 dynamic cardiac PET imaging is widely used for the clinical diagnosis of coronary artery disease (CAD), but its short half-life results in high noise levels that degrade dynamic frame quality and parametric imaging. The lack of paired clean-noisy training data, rapid tracer kinetics, and frame-dependent noise variations further limit the effectiveness of existing deep learning denoising methods. We propose DECADE (A Temporally-Consistent Unsupervised Diffusion model for Enhanced Rb-82 CArdiac PET DEnoising), an unsupervised diffusion framework that generalizes across early- to late-phase dynamic frames. DECADE incorporates temporal consistency during both training and iterative sampling, using noisy frames as guidance to preserve quantitative accuracy. The method was trained and evaluated on datasets acquired from Siemens Vision 450 and Siemens Biograph Vision Quadra scanners. On the Vision 450 dataset, DECADE consistently produced high-quality dynamic and parametric images with reduced noise while preserving myocardial blood flow (MBF) and myocardial flow reserve (MFR). On the Quadra dataset, using 15%-count images as input and full-count images as reference, DECADE outperformed UNet-based and other diffusion models in image quality and K1/MBF quantification. The proposed framework enables effective unsupervised denoising of Rb-82 dynamic cardiac PET without paired training data, supporting clearer visualization while maintaining quantitative integrity.

Problem

• Rb-82 PET
• image denoising
• dynamic cardiac imaging
• temporal consistency
• unsupervised learning

Innovation

• unsupervised diffusion model
• temporal consistency
• dynamic cardiac PET
• Rb-82 denoising
• quantitative preservation