A deep-learning method using single phantom to enhance megavoltage image quality for patient positioning in chest radiotherapy: a feasibility study
- 12 June 2023
- journal article
- research article
- Published by Springer Nature in Journal of the Korean Physical Society
- Vol. 83 (1) , 72-80
Abstract
No abstract availableKeywords
This publication has 12 references indexed in Scilit:
- MV CBCT-Based Synthetic CT Generation Using a Deep Learning Method for Rectal Cancer Adaptive RadiotherapyFrontiers in Oncology, 2021
- Improved contrast and noise of megavoltage computed tomography (MVCT) through cycle‐consistent generative machine learningMedical Physics, 2020
- A modality conversion approach to MV‐DRs and KV‐DRRs registration using information bottlenecked conditional generative adversarial networkMedical Physics, 2019
- A Megavoltage CT Image Enhancement Method for Image-Guided and Adaptive Helical TomoTherapyFrontiers in Oncology, 2019
- Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRIJournal of Magnetic Resonance Imaging, 2018
- Deep learning in medical imaging and radiation therapyMedical Physics, 2018
- Synthesising KV‐DRRs from MV‐DRs with fractal hourglass convolutional networkElectronics Letters, 2018
- Radiation oncology in the era of precision medicineNature Reviews Cancer, 2016
- U-Net: Convolutional Networks for Biomedical Image Segmentation2015
- Image quality assessment: from error visibility to structural similarityIEEE Transactions on Image Processing, 2004