Cone‐beam CT‐derived relative stopping power map generation via deep learning for proton radiotherapy

2020 ◽  
Vol 47 (9) ◽  
pp. 4416-4427
Author(s):  
Joseph Harms ◽  
Yang Lei ◽  
Tonghe Wang ◽  
Mark McDonald ◽  
Beth Ghavidel ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cone Beam Ct ◽  
Stopping Power ◽  
Cone Beam ◽  
Proton Radiotherapy ◽  
Map Generation

Image quality improvement in cone-beam CT using deep learning

2019 ◽  
Author(s):  
Yang Lei ◽  
Tonghe Wang ◽  
Joseph Harms ◽  
Ghazal Shafai-Erfani ◽  
Xue Dong ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Deep Learning ◽  
Image Quality ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Image Quality Improvement

scholarly journals Self‐contained deep learning‐based boosting of 4D cone‐beam CT reconstruction

2020 ◽  
Vol 47 (11) ◽  
pp. 5619-5631
Author(s):  
Frederic Madesta ◽  
Thilo Sentker ◽  
Tobias Gauer ◽  
René Werner
Keyword(s):  
Deep Learning ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Ct Reconstruction

scholarly journals Cone-beam CT image quality improvement using Cycle-Deblur consistent adversarial networks (Cycle-Deblur GAN) for chest CT imaging in breast cancer patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hui-Ju Tien ◽  
Hsin-Chih Yang ◽  
Pei-Wei Shueng ◽  
Jyh-Cheng Chen
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Image Quality ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Breast Cancer Patients ◽  
Image Guided Radiotherapy ◽  
Image Quality Improvement ◽  
Image Pairs

AbstractCone-beam computed tomography (CBCT) integrated with a linear accelerator is widely used to increase the accuracy of radiotherapy and plays an important role in image-guided radiotherapy (IGRT). For comparison with fan-beam computed tomography (FBCT), the image quality of CBCT is indistinct due to X-ray scattering, noise, and artefacts. We proposed a deep learning model, “Cycle-Deblur GAN”, combined with CycleGAN and Deblur-GAN models to improve the image quality of chest CBCT images. The 8706 CBCT and FBCT image pairs were used for training, and 1150 image pairs were used for testing in deep learning. The generated CBCT images from the Cycle-Deblur GAN model demonstrated closer CT values to FBCT in the lung, breast, mediastinum, and sternum compared to the CycleGAN and RED-CNN models. The quantitative evaluations of MAE, PSNR, and SSIM for CBCT generated from the Cycle-Deblur GAN model demonstrated better results than the CycleGAN and RED-CNN models. The Cycle-Deblur GAN model improved image quality and CT-value accuracy and preserved structural details for chest CBCT images.

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