Zobrazeno 1 - 10
of 53
pro vyhledávání: '"Abolfazl Mehranian"'
Autor:
Meghi Dedja, Abolfazl Mehranian, Kevin M. Bradley, Matthew D. Walker, Patrick A. Fielding, Scott D. Wollenweber, Robert Johnsen, Daniel R. McGowan
Publikováno v:
EJNMMI Physics, Vol 11, Iss 1, Pp 1-12 (2024)
Abstract Background Investigate the potential benefits of sequential deployment of two deep learning (DL) algorithms namely DL-Enhancement (DLE) and DL-based time-of-flight (ToF) (DLT). DLE aims to enhance the rapidly reconstructed ordered-subset-exp
Externí odkaz:
https://doaj.org/article/02d146a8ba474501ac1bed66b6abd0d2
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030982522
Segmentation of head and neck (H\&N) tumours and prediction of patient outcome are crucial for patient's disease diagnosis and treatment monitoring. Current developments of robust deep learning models are hindered by the lack of large multi-centre, m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1f6aa5eb916946ca13870fad92740ba9
https://ora.ox.ac.uk/objects/uuid:c6ed503f-906d-454b-a86f-83b8f90ee65c
https://ora.ox.ac.uk/objects/uuid:c6ed503f-906d-454b-a86f-83b8f90ee65c
Autor:
Sam Ellis, Julia A. Schnabel, Andrew J. Reader, Abolfazl Mehranian, Guillaume Corda, Casper O. da Costa-Luis
Publikováno v:
IEEE Transactions on Radiation and Plasma Medical Sciences. 5:1-25
This article reviews the use of a subdiscipline of artificial intelligence (AI), deep learning , for the reconstruction of images in positron emission tomography (PET). Deep learning can be used either directly or as a component of conventional recon
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ddbad6b2e444fb5c8877459de60b6da3
https://doi.org/10.1109/trpms.2020.3014786
https://doi.org/10.1109/trpms.2020.3014786
Autor:
Colm J. McGinnity, Claudia Prieto, Enrico De Vita, Alexander Hammers, Abolfazl Mehranian, Andrew J. Reader, Radhouene Neji
Publikováno v:
Magnetic Resonance in Medicine
Purpose A model-based reconstruction framework is proposed for motion-corrected and high-resolution anatomically assisted (MOCHA) reconstruction of arterial spin labeling (ASL) data. In this framework, all low-resolution ASL control-label pairs are u
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a0da7a1626615d2be3063a6c3684f2b9
http://europepmc.org/articles/PMC8614125
http://europepmc.org/articles/PMC8614125
Autor:
Scott David Wollenweber, Daniel R. McGowan, Matthew D. Walker, Robert Johnsen, Abolfazl Mehranian, Patrick Fielding, Kuan-Hao Su, Floris Jansen, Kevin M. Bradley, Fotis A. Kotasidis
Purpose To enhance the image quality of oncology [18F]-FDG PET scans acquired in shorter times and reconstructed by faster algorithms using deep neural networks. Methods List-mode data from 277 [18F]-FDG PET/CT scans, from six centres using GE Discov
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d4045b29745262252a01c8351eff3482
https://ora.ox.ac.uk/objects/uuid:01304f26-38a5-42c8-ba1c-6ed4097014bb
https://ora.ox.ac.uk/objects/uuid:01304f26-38a5-42c8-ba1c-6ed4097014bb
Autor:
Andrew J. Reader, Casper O. da Costa-Luis, Alexander Hammers, Abolfazl Mehranian, Martin A. Belzunce, Sam Ellis, James Bland, Colm J. McGinnity
Publikováno v:
Medical Physics
Purpose Numerous image reconstruction methodologies for positron emission tomography (PET) have been developed that incorporate magnetic resonance (MR) imaging structural information, producing reconstructed images with improved suppression of noise
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6d64ddbe1d4c5dfe1a8ae9751cd9544b
https://doi.org/10.1002/mp.13812
https://doi.org/10.1002/mp.13812
Autor:
Abolfazl, Mehranian, Scott D, Wollenweber, Matthew D, Walker, Kevin M, Bradley, Patrick A, Fielding, Kuan-Hao, Su, Robert, Johnsen, Fotis, Kotasidis, Floris P, Jansen, Daniel R, McGowan
Publikováno v:
European Journal of Nuclear Medicine and Molecular Imaging
Purpose To enhance the image quality of oncology [18F]-FDG PET scans acquired in shorter times and reconstructed by faster algorithms using deep neural networks. Methods List-mode data from 277 [18F]-FDG PET/CT scans, from six centres using GE Discov
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::6faa60d0b45ac777164cc8982a5a745a
https://pubmed.ncbi.nlm.nih.gov/34318350
https://pubmed.ncbi.nlm.nih.gov/34318350
Publikováno v:
2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).
This work aims to evaluate the impact of lowering PET dose on image quality and diagnostic confidence of PET images reconstructed by filtered back-projection (FBP), standard maximum-likelihood expectation-maximization (MLEM) and advanced MR-assisted
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::230e141cab9a4829d5766d432955a1db
https://doi.org/10.1109/nss/mic42101.2019.9059655
https://doi.org/10.1109/nss/mic42101.2019.9059655
Publikováno v:
2019 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC).
Neurological positron emission tomography (PET) provides a vital diagnostic and patient management tool for treating a wide range of conditions, such as oncological and neurodegenerative diseases. One particular research direction currently being exp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f025d63a9861d72ea46c6d21851b386a
https://doi.org/10.1109/nss/mic42101.2019.9059968
https://doi.org/10.1109/nss/mic42101.2019.9059968
Publikováno v:
Medical Physics. 43:1588-1597
Purpose: Metal artifact reduction (MAR) produces images with improved quality potentially leading to confident and reliable clinical diagnosis and therapy planning. In this work, the authors evaluate the performance of five MAR techniques for the ass
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::97aa2e851adfaf43e71e1c7965c05121
https://doi.org/10.1118/1.4942810
https://doi.org/10.1118/1.4942810