TY - GEN
T1 - A Noise-Level-Aware Framework for PET Image Denoising
AU - Li, Ye
AU - Cui, Jianan
AU - Chen, Junyu
AU - Zeng, Guodong
AU - Wollenweber, Scott
AU - Jansen, Floris
AU - Jang, Se In
AU - Kim, Kyungsang
AU - Gong, Kuang
AU - Li, Quanzheng
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Denoising
KW - Depp learning
KW - Local relative noise level
KW - Neural network
KW - PET
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U2 - 10.1007/978-3-031-17247-2_8
DO - 10.1007/978-3-031-17247-2_8
M3 - Conference contribution
AN - SCOPUS:85140432150
SN - 9783031172465
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 75
EP - 83
BT - Machine Learning for Medical Image Reconstruction - 5th International Workshop, MLMIR 2022, Held in Conjunction with MICCAI 2022, Proceedings
A2 - Haq, Nandinee
A2 - Johnson, Patricia
A2 - Maier, Andreas
A2 - Qin, Chen
A2 - Würfl, Tobias
A2 - Yoo, Jaejun
PB - Springer Science and Business Media Deutschland GmbH
T2 - 5th 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
Y2 - 22 September 2022 through 22 September 2022
ER -