Efficient 3d pet image denoising improves low-dose scan quality quickly
Efficient 3D Whole-Body PET Image Denoising via Conditional Rectified Flow With Optimized Sampling Strategy
Computer Vision and Pattern Recognition
Summary
Low-dose PET scans reduce radiation but create noisy images that are hard to read. The authors designed a new 3D deep learning method that cleans up these images quickly while keeping important 3D details intact. Their approach is faster than previous 3D methods and works well even on very low radiation images. This could help doctors see clearer PET images with less risk to patients.
What this means in practice
- •For medical imaging engineers: Integrate the 3D rectified flow denoising model to improve image quality and speed for ultra-low-dose whole-body PET scans.
- •For radiology software developers: Embed optimized sampling strategies from this work to enhance 3D denoising modules while maintaining fast inference times in clinical PET applications.
Authors
Jiale Shen, Guolin Wang, Chenhao Wang, Xinhui Su, Wei Luo, Feng Yu
Abstract
Reducing radiation exposure in Positron Emission Tomography (PET) is important for patient safety; however, ultra-low-dose imaging suffers from severe noise, which may affect diagnostic interpretation without appropriate image enhancement. While current 3D deep generative models, particularly diffusion models, have shown strong reconstruction fidelity, their practical use can be limited by long inference times. In contrast, faster 2D-based alternatives may have difficulty maintaining volumetric consistency, an important consideration for whole-body PET imaging analysis. To bridge this gap, we propose a one-pass conditional 3D rectified flow (3D Flow) framework for whole-body PET image denoising that incorporates a novel optimized non-uniform sampling strategy. The model is trained with a one-pass linear-interpolant velocity-matching objective. This approach reconstructs a full 3D volume in approximately 30 seconds in our implementation, compared with multi-hour inference for the evaluated 3D DDPM baseline. Evaluations including zero-shot transfer to an independent clinical dataset show that our model achieves favorable global image quality and lesion conspicuity compared with the evaluated 3D DDPM and DDIM baselines, including on challenging short-acquisition data. Furthermore, the proposed method shows promising zero-shot transfer performance across the evaluated datasets and unseen dose levels (down to 1/100 of the standard dose), with artifact-focused visual comparisons supporting the need for further lesion-level validation. By balancing reconstruction fidelity and computational efficiency, this work presents a candidate approach for ultra-low-dose whole-body PET image denoising.