GC-L8.2

DUAL-DOMAIN NEURAL NETWORKS FOR CLINICAL AND LOW-DOSE CBCT RECONSTRUCTION

Xuzhi Zhao, Xi Liu, Xinyi Wang, Rui Yang, Beijing Jiaotong University, China; Yi Du, Peking University Cancer Hospital & Institute, China; Yahui Peng, Beijing Jiaotong University, China
Session:
GC-L8: Advancing the frontiers of deep learning for low-dose 3D cone-beam CT reconstruction Lecture
Track:
Grand Challenges
Location:
Room 209B
Presentation Time:
Thu, 18 Apr, 16:50 - 17:10 (UTC +9)
Session Co-Chairs:
Ander Biguri, University of Cambridge, UK and Andreas Hauptmann, University of Oulu
Presentation
Discussion
Resources
No resources available.
Session GC-L8
GC-L8.1: ADVANCING THE FRONTIERS OF DEEP LEARNING FOR LOW-DOSE 3D CONE-BEAM COMPUTED TOMOGRAPHY (CT) RECONSTRUCTION
Ander Biguri, University of Cambridge, United Kingdom of Great Britain and Northern Ireland; Subhadip Mukherjee, IIT Kharagpur, India
GC-L8.2: DUAL-DOMAIN NEURAL NETWORKS FOR CLINICAL AND LOW-DOSE CBCT RECONSTRUCTION
Xuzhi Zhao, Xi Liu, Xinyi Wang, Rui Yang, Beijing Jiaotong University, China; Yi Du, Peking University Cancer Hospital & Institute, China; Yahui Peng, Beijing Jiaotong University, China
GC-L8.3: MONAI FOR DEEP-LEARNING BASED CBCT RECONSTRUCTION
Mikael Brudfors, NVIDIA, United Kingdom of Great Britain and Northern Ireland; Mark Graham, King's College London, United Kingdom of Great Britain and Northern Ireland; Hyungon Ryu, Oliver Kutter, NVIDIA, Korea, Republic of
GC-L8.4: A MULTI-FILTER AND MULTI-SCALE U-NET FOR CONE-BEAM COMPUTED TOMOGRAPHY WITH HARDWARE CONSTRAINTS
Andreas Hauptmann, University of Oulu; University College London, Finland; Mustafa Al-Rubaye, University of Oulu, Finland; Miika T. Nieminen, Mikael A.K. Brix, University of Oulu; Oulu University Hospital, Finland
GC-L8.5: LOW DOSE CBCT DENOISING USING A 3D U-NET
Austin Yunker, Argonne National Laboratory, United States of America; John Roeske, Loyola University Chicago, United States of America; Rajkumar Kettimuthu, Argonne National Laboratory, United States of America
GC-L8.6: 3D CBCT CHALLENGE 2024: IMPROVED CONE BEAM CT RECONSTRUCTION USING SWINIR-BASED SINOGRAM AND IMAGE ENHANCEMENT
Sasidhar Alavala, Subrahmanyam Gorthi, Indian Institute of Technology Tirupati, India
Contacts