Accurate Transmission-Less Attenuation Correction Method for Amyloid-beta Brain PET Using Deep Neural Network
- Authors
- Choi, Bo-Hye; Hwang, Donghwi; Kang, Seung-Kwan; Kim, Kyeong-Yun; Choi, Hongyoon; Seo, Seongho; Lee, Jae-Sung
- Issue Date
- Aug-2021
- Publisher
- MDPI
- Keywords
- attenuation correction; Alzheimer’s disease; Amyloid; Convolutional neural network; Positron emission tomography
- Citation
- ELECTRONICS, v.10, no.15
- Journal Title
- ELECTRONICS
- Volume
- 10
- Number
- 15
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/81859
- DOI
- 10.3390/electronics10151836
- ISSN
- 2079-9292
- Abstract
- The lack of physically measured attenuation maps (μ-maps) for attenuation and scatter correction is an important technical challenge in brain-dedicated stand-alone positron emission to-mography (PET) scanners. The accuracy of the calculated attenuation correction is limited by the nonuniformity of tissue composition due to pathologic conditions and the complex structure of fa-cial bones. The aim of this study is to develop an accurate transmission-less attenuation correction method for amyloid-β (Aβ) brain PET studies. We investigated the validity of a deep convolutional neural network trained to produce a CT-derived μ-map (μ-CT) from simultaneously reconstructed activity and attenuation maps using the MLAA (maximum likelihood reconstruction of activity and attenuation) algorithm for Aβ brain PET. The performance of three different structures of U-net models (2D, 2.5D, and 3D) were compared. The U-net models generated less noisy and more uniform μ-maps than MLAA μ-maps. Among the three different U-net models, the patch-based 3D U-net model reduced noise and cross-talk artifacts more effectively. The Dice similarity coefficients between the μ-map generated using 3D U-net and μ-CT in bone and air segments were 0.83 and 0.67. All three U-net models showed better voxel-wise correlation of the μ-maps compared to MLAA. The patch-based 3D U-net model was the best. While the uptake value of MLAA yielded a high percentage error of 20% or more, the uptake value of 3D U-nets yielded the lowest percentage error within 5%. The proposed deep learning approach that requires no transmission data, anatomic image, or atlas/template for PET attenuation correction remarkably enhanced the quantitative accuracy of the simultaneously estimated MLAA μ-maps from Aβ brain PET. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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