A Noise-Level-Aware Framework for PET Image Denoising

Ye Li, Jianan Cui, Junyu Chen, Guodong Zeng, Scott Wollenweber, Floris Jansen, Se In Jang, Kyungsang Kim, Kuang Gong, Quanzheng Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In PET, the amount of relative (signal-dependent) noise present in different body regions can be significantly different and is inherently related to the number of counts present in that region. The number of counts in a region depends, in principle and among other factors, on the total administered activity, scanner sensitivity, image acquisition duration, radiopharmaceutical tracer uptake in the region, and patient local body morphometry surrounding the region. In theory, less amount of denoising operations is needed to denoise a high-count (low relative noise) area than images a low-count (high relative noise) area, and vice versa. The current deep-learning-based methods for PET image denoising are predominantly trained on whole images using image appearance only and have not taken into account any prior knowledge about the spatially varying noise in PET. Our hypothesis is that by explicitly providing the relative noise level of each local area of a PET image to a deep convolutional neural network (DCNN), the DCNN learn noise-level-specific denoising features at different noise-levels and apply these features to areas with different denoising needs, thus outperforming the DCNN trained on whole images using image appearance only. To this end, we propose a noise-level-aware framework denoising framework that allows embedding of local noise level into a DCNN. The proposed is trained and tested on 30 and 15 patient PET images acquired on a GE Discovery MI PET/CT system. Our experiments showed that the increases in both PSNR and SSIM from our backbone network with relative noise level embedding (NLE) versus the same network without NLE were statistically significant with p < 0.001, and the proposed method significantly outperformed a strong baseline method by a large margin.

Original languageEnglish (US)
Title of host publicationMachine Learning for Medical Image Reconstruction - 5th International Workshop, MLMIR 2022, Held in Conjunction with MICCAI 2022, Proceedings
EditorsNandinee Haq, Patricia Johnson, Andreas Maier, Chen Qin, Tobias Würfl, Jaejun Yoo
PublisherSpringer Science and Business Media Deutschland GmbH
Pages75-83
Number of pages9
ISBN (Print)9783031172465
DOIs
StatePublished - 2022
Event5th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore
Duration: Sep 22 2022Sep 22 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13587 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Country/TerritorySingapore
CitySingapore
Period9/22/229/22/22

Keywords

  • Denoising
  • Depp learning
  • Local relative noise level
  • Neural network
  • PET

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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