Adaptive Whole-Body PET Image Denoising Using 3D Diffusion Models with ControlNet

TL;DR

This paper introduces a novel 3D ControlNet-based diffusion model for adaptive whole-body PET image denoising, effectively handling variability in clinical settings and outperforming existing methods in quality and metrics.

Contribution

It presents a fine-tuning approach for large diffusion models using ControlNet to adapt to diverse PET imaging conditions, a novel application in medical image denoising.

Findings

Outperforms state-of-the-art PET denoising methods in visual and quantitative assessments.

Demonstrates effective adaptation to different clinical PET acquisition protocols.

Shows improved image quality and noise reduction in diverse clinical datasets.

Abstract

Positron Emission Tomography (PET) is a vital imaging modality widely used in clinical diagnosis and preclinical research but faces limitations in image resolution and signal-to-noise ratio due to inherent physical degradation factors. Current deep learning-based denoising methods face challenges in adapting to the variability of clinical settings, influenced by factors such as scanner types, tracer choices, dose levels, and acquisition times. In this work, we proposed a novel 3D ControlNet-based denoising method for whole-body PET imaging. We first pre-trained a 3D Denoising Diffusion Probabilistic Model (DDPM) using a large dataset of high-quality normal-dose PET images. Following this, we fine-tuned the model on a smaller set of paired low- and normal-dose PET images, integrating low-dose inputs through a 3D ControlNet architecture, thereby making the model adaptable to denoising tasks in diverse clinical settings. Experimental results based on clinical PET datasets show that the proposed framework outperformed other state-of-the-art PET image denoising methods both in visual quality and quantitative metrics. This plug-and-play approach allows large diffusion models to be fine-tuned and adapted to PET images from diverse acquisition protocols.