Federated Transfer Learning for Low-Dose PET Denoising: A Pilot Study With Simulated Heterogeneous Data

Abstract

Positron emission tomography (PET) with a reduced injection dose, i.e., low-dose PET, is an efficient way to reduce radiation dose. However, low-dose PET reconstruction suffers from a low signal-to-noise ratio (SNR), affecting diagnosis and other PET-related applications. Recently, deep learning-based PET denoising methods have demonstrated superior performance in generating high-quality reconstruction. However, these methods require a large amount of representative data for training, which can be difficult to collect and share due to medical data privacy regulations. Moreover, low-dose PET data at different institutions may use different low-dose protocols, leading to nonidentical data distribution. While previous federated learning (FL) algorithms enable multiinstitution collaborative training without the need of aggregating local data, it is challenging for previous methods to address the large domain shift caused by different low-dose PET settings, and the application of FL to PET is still underexplored. In this work, we propose a federated transfer learning (FTL) framework for low-dose PET denoising using heterogeneous low-dose data. Our experimental results on simulated multiinstitutional data demonstrate that our method can efficiently utilize heterogeneous low-dose data without compromising data privacy for achieving superior low-dose PET

Deep learning-based PET image denoising and reconstruction: a review

“…The effectiveness of denoising diffusion probabilistic models [162] for PET image denoising has also been investigated [163,164]. From the viewpoint of personal information protection, federated learning, which enables decentralized learning without the need to export clinical data, is beginning to be applied to PET image denoising [165,166]. In addition, uncertainty estimation [167,168] and noise-aware networks [169][170][171] can provide additional value to conventional denoising methods.…”

Artificial Intelligence for PET and SPECT Image Enhancement

“…A personalized denoising strategy has been proposed that uses different noise levels for training and incorporates a weighting factor that is based on the noise level in a task-dependent manner (37). A federated learning framework for PET image denoising was successfully tested with a simulated dataset with different noise settings corresponding to protocols from different institutions (38). Generalizability concerns also emphasize methods that can be adapted across scanners and tracers.…”