Neural network-based quantitative reconstructions of PET without attenuation correction

2021 ◽  
Author(s):  
Linlin Zhao ◽  
Huafeng Liu
Keyword(s):  
Neural Network ◽  
Attenuation Correction ◽  
Quantitative Reconstructions

scholarly journals Accurate Transmission-Less Attenuation Correction Method for Amyloid-β Brain PET Using Deep Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1836
Author(s):  
Bo-Hye Choi ◽  
Donghwi Hwang ◽  
Seung-Kwan Kang ◽  
Kyeong-Yun Kim ◽  
Hongyoon Choi ◽  
...  
Keyword(s):  
Neural Network ◽  
Attenuation Correction ◽  
Complex Structure ◽  
Scatter Correction ◽  
Amyloid Β ◽  
Correction Method ◽  
Percentage Error ◽  
Similarity Coefficients ◽  
Brain Pet ◽  
Positron Emission

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 tomography (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 facial 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.

scholarly journals Synthesis of Patient-Specific Transmission Data for PET Attenuation Correction for PET/MRI Neuroimaging Using a Convolutional Neural Network

2018 ◽  
Vol 60 (4) ◽  
pp. 555-560 ◽  
Author(s):  
Karl D. Spuhler ◽  
John Gardus ◽  
Yi Gao ◽  
Christine DeLorenzo ◽  
Ramin Parsey ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Attenuation Correction ◽  
Patient Specific ◽  
Transmission Data

A Transfer Fuzzy Clustering and Neural Network Based Tissue Segmentation Method During PET/MR Attenuation Correction

2019 ◽  
Vol 9 (7) ◽  
pp. 1491-1497 ◽  
Author(s):  
Kaifa Zhao ◽  
Leyuan Zhou ◽  
Pengjiang Qian ◽  
Yang Ding ◽  
Yizhang Jiang ◽  
...  
Keyword(s):  
Neural Network ◽  
Attenuation Correction ◽  
Fuzzy Clustering ◽  
Segmentation Method ◽  
Tissue Segmentation

scholarly journals Attenuation correction using 3D deep convolutional neural network for brain 18F-FDG PET/MR: Comparison with Atlas, ZTE and CT based attenuation correction

PLoS ONE ◽  
2019 ◽  
Vol 14 (10) ◽  
pp. e0223141 ◽  
Author(s):  
Paul Blanc-Durand ◽  
Maya Khalife ◽  
Brian Sgard ◽  
Sandeep Kaushik ◽  
Marine Soret ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Attenuation Correction ◽  
Fdg Pet ◽  
Deep Convolutional Neural Network ◽  
18F Fdg

scholarly journals Attenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images

2018 ◽  
Vol 63 (12) ◽  
pp. 125011 ◽  
Author(s):  
Kuang Gong ◽  
Jaewon Yang ◽  
Kyungsang Kim ◽  
Georges El Fakhri ◽  
Youngho Seo ◽  
...  
Keyword(s):  
Neural Network ◽  
Attenuation Correction ◽  
Pet Imaging ◽  
Deep Neural Network ◽  
Mr Images ◽  
Brain Pet

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural
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