# Self-Supervised Pre-Training for Deep Image Prior-Based Robust PET Image Denoising

## TL;DR

This paper introduces a self-supervised pre-training approach to enhance DIP-based PET image denoising, enabling robust, high-quality images from unlabeled data, which is beneficial for rare diseases and reducing scan time or dose.

## Contribution

The study presents a novel self-supervised pre-training method that improves DIP-based PET denoising performance using only unlabeled PET images, achieving state-of-the-art results.

## Key findings

- Achieved robust denoising across various tracers and scanners.
- Retained spatial details and quantification accuracy.
- Outperformed other unsupervised and pre-training methods.

## Abstract

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.

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Source: https://tomesphere.com/paper/2302.13546