Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Satheesh, Paloor"'
Publikováno v:
BMC Medical Imaging, Vol 23, Iss 1, Pp 1-11 (2023)
Abstract Background In recent years, there has been a growing trend towards utilizing Artificial Intelligence (AI) and machine learning techniques in medical imaging, including for the purpose of automating quality assurance. In this research, we aim
Externí odkaz:
https://doaj.org/article/2959192712a64ac4bf3e0b276f0ebb37
Autor:
S.A. Yoganathan, Souha Aouadi, Sharib Ahmed, Satheesh Paloor, Tarraf Torfeh, Noora Al-Hammadi, Rabih Hammoud
Publikováno v:
Physics and Imaging in Radiation Oncology, Vol 28, Iss , Pp 100512- (2023)
Background and purpose: Accurate CT numbers in Cone Beam CT (CBCT) are crucial for precise dose calculations in adaptive radiotherapy (ART). This study aimed to generate synthetic CT (sCT) from CBCT using deep learning (DL) models in head and neck (H
Externí odkaz:
https://doaj.org/article/43a6bbc9dc2f4ee6bcacd00729afb8b3
Autor:
S A, Yoganathan, Siji Nojin, Paul, Satheesh, Paloor, Tarraf, Torfeh, Suparna Halsnad, Chandramouli, Rabih, Hammoud, Noora, Al-Hammadi
Publikováno v:
Medical Physics. 49:1571-1584
Magnetic resonance (MR) imaging is the gold standard in image-guided brachytherapy (IGBT) due to its superior soft-tissue contrast for target and organs-at-risk (OARs) delineation. Accurate and fast segmentation of MR images are very important for hi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8f6ea7058c12ee90954ac650816e7ce6
https://doi.org/10.1002/mp.15506
https://doi.org/10.1002/mp.15506
Autor:
S A Yoganathan, Satheesh Paloor, Tarraf Torfeh, Souha Aouadi, Rabih Hammoud, Noora Al-Hammadi
Publikováno v:
Biomedical physicsengineering express. 8(6)
Real-time tracking of a target volume is a promising solution for reducing the planning margins and both dosimetric and geometric uncertainties in the treatment of thoracic and upper-abdomen cancers. Respiratory motion prediction is an integral part
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::81a5f74a67efc5483aa088b1ec0c13aa
https://pubmed.ncbi.nlm.nih.gov/36130525
https://pubmed.ncbi.nlm.nih.gov/36130525
Autor:
Souha Aouadi, Tarraf Torfeh, Yoganathan Arunachalam, Satheesh Paloor, Mohamed Riyas, Rabih Hammoud, Noora Al-Hammadi
Publikováno v:
Biomedical Physics & Engineering Express. 9:035020
Purpose. To determine glioma grading by applying radiomic analysis or deep convolutional neural networks (DCNN) and to benchmark both approaches on broader validation sets. Methods. Seven public datasets were considered: (1) low-grade glioma or high-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bfdf4414a1cc24f556eb1e004c2fb320
https://doi.org/10.1088/2057-1976/acc33a
https://doi.org/10.1088/2057-1976/acc33a