Developments in deep learning based corrections of cone beam computed tomography to enable dose calculations for adaptive radiotherapy

[1] Ahmad MA, 2018, ACM-BCB'18: PROCEEDINGS OF THE 2018 ACM INTERNATIONAL CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY, AND HEALTH INFORMATICS, P559, DOI [10.1145/3233547.3233667, 10.1109/ICHI.2018.00095]

[2] Efficient scatter distribution estimation and correction in CBCT using concurrent Monte Carlo fitting [J]. MEDICAL PHYSICS, 2015, 42 (01) : 54 - 68

[3] On-line dose-guidance to account for inter-fractional motion during proton therapy [J]. PHYSICS & IMAGING IN RADIATION ONCOLOGY, 2019, 9 : 7 - 13

[4] A feasibility study on an automated method to generate patient-specific dose distributions for radiotherapy using deep learning [J]. MEDICAL PHYSICS, 2019, 46 (01) : 56 - 64

[5] 3D radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-net deep learning architecture [J]. PHYSICS IN MEDICINE AND BIOLOGY, 2019, 64 (06)

[6] ScatterNet: A convolutional neural network for cone-beam CT intensity correction [J]. MEDICAL PHYSICS, 2018, 45 (11) : 4916 - 4926

[7] Cardio-pulmonary substructure segmentation of radiotherapy computed tomography images using convolutional neural networks for clinical outcomes analysis [J]. PHYSICS & IMAGING IN RADIATION ONCOLOGY, 2020, 14 : 61 - 66

[8] Cone beam computed tomography-based monitoring and management of target and organ motion during external beam radiotherapy in cervical cancer [J]. PHYSICS & IMAGING IN RADIATION ONCOLOGY, 2019, 9 : 14 - 20

[9] Clinical implementation of AI technologies will require interpretable AI models [J]. MEDICAL PHYSICS, 2020, 47 (01) : 1 - 4

[10] Cone beam CT based dose calculation in the thorax region [J]. PHYSICS & IMAGING IN RADIATION ONCOLOGY, 2018, 7 : 45 - 50

[1] Ahmad MA, 2018, ACM-BCB'18: PROCEEDINGS OF THE 2018 ACM INTERNATIONAL CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY, AND HEALTH INFORMATICS, P559, DOI [10.1145/3233547.3233667, 10.1109/ICHI.2018.00095]

[2] Efficient scatter distribution estimation and correction in CBCT using concurrent Monte Carlo fitting [J]. MEDICAL PHYSICS, 2015, 42 (01) : 54 - 68

[3] On-line dose-guidance to account for inter-fractional motion during proton therapy [J]. PHYSICS & IMAGING IN RADIATION ONCOLOGY, 2019, 9 : 7 - 13

[4] A feasibility study on an automated method to generate patient-specific dose distributions for radiotherapy using deep learning [J]. MEDICAL PHYSICS, 2019, 46 (01) : 56 - 64

[5] 3D radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-net deep learning architecture [J]. PHYSICS IN MEDICINE AND BIOLOGY, 2019, 64 (06)

[6] ScatterNet: A convolutional neural network for cone-beam CT intensity correction [J]. MEDICAL PHYSICS, 2018, 45 (11) : 4916 - 4926

[7] Cardio-pulmonary substructure segmentation of radiotherapy computed tomography images using convolutional neural networks for clinical outcomes analysis [J]. PHYSICS & IMAGING IN RADIATION ONCOLOGY, 2020, 14 : 61 - 66

[8] Cone beam computed tomography-based monitoring and management of target and organ motion during external beam radiotherapy in cervical cancer [J]. PHYSICS & IMAGING IN RADIATION ONCOLOGY, 2019, 9 : 14 - 20

[9] Clinical implementation of AI technologies will require interpretable AI models [J]. MEDICAL PHYSICS, 2020, 47 (01) : 1 - 4

[10] Cone beam CT based dose calculation in the thorax region [J]. PHYSICS & IMAGING IN RADIATION ONCOLOGY, 2018, 7 : 45 - 50