Self-Supervised Pre-Training for Deep Image Prior-Based Robust PET Image Denoising
This work addresses the need for better PET imaging with DIP, potentially reducing scan time or radiotracer dose for patients, especially in cases of rare diseases and probes, but it is incremental as it builds on existing DIP methods.
The paper tackled the problem of improving deep image prior (DIP) for PET image denoising by proposing a self-supervised pre-training model that learns from unlabeled PET images, achieving robust and state-of-the-art denoising performance while retaining spatial details and quantification accuracy across various clinical brain PET data.
Deep image prior (DIP) has been successfully applied to positron emission tomography (PET) image restoration, enabling represent implicit prior using only convolutional neural network architecture without training dataset, whereas the general supervised approach requires massive low- and high-quality PET image pairs. To answer the increased need for PET imaging with DIP, it is indispensable to improve the performance of the underlying DIP itself. Here, we propose a self-supervised pre-training model to improve the DIP-based PET image denoising performance. Our proposed pre-training model acquires transferable and generalizable visual representations from only unlabeled PET images by restoring various degraded PET images in a self-supervised approach. We evaluated the proposed method using clinical brain PET data with various radioactive tracers ($^{18}$F-florbetapir, $^{11}$C-Pittsburgh compound-B, $^{18}$F-fluoro-2-deoxy-D-glucose, and $^{15}$O-CO$_{2}$) acquired from different PET scanners. The proposed method using the self-supervised pre-training model achieved robust and state-of-the-art denoising performance while retaining spatial details and quantification accuracy compared to other unsupervised methods and pre-training model. These results highlight the potential that the proposed method is particularly effective against rare diseases and probes and helps reduce the scan time or the radiotracer dose without affecting the patients.