Zobrazeno 1 - 10
of 66
pro vyhledávání: '"Evren, Asma"'
Autor:
Junichi Tsuchiya, Kota Yokoyama, Ken Yamagiwa, Ryosuke Watanabe, Koichiro Kimura, Mitsuhiro Kishino, Chung Chan, Evren Asma, Ukihide Tateishi
Publikováno v:
EJNMMI Physics, Vol 8, Iss 1, Pp 1-12 (2021)
Abstract Background Deep learning (DL)-based image quality improvement is a novel technique based on convolutional neural networks. The aim of this study was to compare the clinical value of 18F-fluorodeoxyglucose positron emission tomography (18F-FD
Externí odkaz:
https://doaj.org/article/e314e24010d14d578905f8dc2ce7cacb
Publikováno v:
IEEE Trans Med Imaging
IEEE transactions on medical imaging, vol 41, iss 5
IEEE transactions on medical imaging, vol 41, iss 5
Respiratory motion is one of the main sources of motion artifacts in positron emission tomography (PET) imaging. The emission image and patient motion can be estimated simultaneously from respiratory gated data through a joint estimation framework. H
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5f943794cb763be526beee978894a314
https://doi.org/10.1109/tmi.2021.3136553
https://doi.org/10.1109/tmi.2021.3136553
Publikováno v:
Med Phys
Medical physics, vol 48, iss 9
Medical physics, vol 48, iss 9
Purpose The developments of PET/CT and PET/MR scanners provide opportunities for improving PET image quality by using anatomical information. In this paper, we propose a novel co-learning three-dimensional (3D) convolutional neural network (CNN) to e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6b166543d0852eb2035efb3a3c260af6
https://doi.org/10.1002/mp.15051
https://doi.org/10.1002/mp.15051
Autor:
Mitsuhiro Kishino, Kota Yokoyama, Koichiro Kimura, Evren Asma, Ryosuke Watanabe, Ken Yamagiwa, Ukihide Tateishi, Chung Chan, Junichi Tsuchiya
Publikováno v:
EJNMMI Physics, Vol 8, Iss 1, Pp 1-12 (2021)
EJNMMI Physics
EJNMMI Physics
Background Deep learning (DL)-based image quality improvement is a novel technique based on convolutional neural networks. The aim of this study was to compare the clinical value of 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) im
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a142400ee0256f4c317047c3a957dc0c
https://doaj.org/article/e314e24010d14d578905f8dc2ce7cacb
https://doaj.org/article/e314e24010d14d578905f8dc2ce7cacb
Publikováno v:
2021 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b6cc9a2a115b7fbfd0852a4d81287419
https://doi.org/10.1109/nss/mic44867.2021.9875755
https://doi.org/10.1109/nss/mic44867.2021.9875755
Autor:
Arkadiusz, Sitek, Sangtae, Ahn, Evren, Asma, Adam, Chandler, Alvin, Ihsani, Sven, Prevrhal, Arman, Rahmim, Babak, Saboury, Kris, Thielemans
Publikováno v:
PET clinics. 16(4)
Artificial intelligence (AI) has significant potential to positively impact and advance medical imaging, including positron emission tomography (PET) imaging applications. AI has the ability to enhance and optimize all aspects of the PET imaging chai
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::051b81af9c4f9e70a983c5c442410f61
https://pubmed.ncbi.nlm.nih.gov/34353746
https://pubmed.ncbi.nlm.nih.gov/34353746
Publikováno v:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 ISBN: 9783030871987
MICCAI (3)
MICCAI (3)
A convolutional neural network (ConvNet) is usually trained and then tested using images drawn from the same distribution. To generalize a ConvNet to various tasks often requires a complete training dataset that consists of images drawn from differen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c8062fc73c2e6cb7b5df778ff08193ee
https://doi.org/10.1007/978-3-030-87199-4_3
https://doi.org/10.1007/978-3-030-87199-4_3
Publikováno v:
2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).
Deep convolutional neural networks (DCNN) have been successfully used for denoising natural images as well as medical images. In previous work we had used DCNNs for positron emission tomography (PET) image denoising and demonstrated improved performa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6855143987ed7492872cb3b57f512f89
https://doi.org/10.1109/nss/mic42677.2020.9508022
https://doi.org/10.1109/nss/mic42677.2020.9508022
Publikováno v:
2020 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).
Convolutional neural networks (CNN) are powerful tools in many medical imaging applications including denoising. However, a major concern in deploying a CNN in safety-critical areas is to access its prediction accuracy on out-of-distribution test sam
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bb63e9945fab93a9edb571c928d9f808
https://doi.org/10.1109/nss/mic42677.2020.9508035
https://doi.org/10.1109/nss/mic42677.2020.9508035
Publikováno v:
Physics in medicine and biology, vol 65, iss 15
Phys Med Biol
Phys Med Biol
PURPOSE: Artifacts caused by patient breathing and movement during PET data acquisition affect image quality. Respiratory gating is commonly used to gate the list-mode PET data into multiple bins over a respiratory cycle. Non-rigid registration of re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6e564338b0bc490f1c4580b205675c31
https://escholarship.org/uc/item/4mb4c003
https://escholarship.org/uc/item/4mb4c003