Corpus ID: 58014127

Cone-beam CT to Planning CT synthesis using generative adversarial networks

@article{Kida2019ConebeamCT,
  title={Cone-beam CT to Planning CT synthesis using generative adversarial networks},
  author={S. Kida and Shizuo Kaji and K. Nawa and Toshikazu Imae and T. Nakamoto and S. Ozaki and T. Ohta and Y. Nozawa and Keiichi Nakagawa},
  journal={ArXiv},
  year={2019},
  volume={abs/1901.05773}
}
Cone-beam computed tomography (CBCT) offers advantages over conventional fan-beam CT in that it requires a shorter time and less exposure to obtain images. [...] Key Result Our method enables more accurate adaptive radiation therapy, and opens up new applications for CBCT that hinge on high-quality images.Expand
Chest CBCT-based synthetic CT using cycle-consistent adversarial network with histogram matching
Image-guided radiation therapy (IGRT) is an important technological advancement that has significantly contributed to the accuracy of radiation oncology treatment plan delivery in the last decade.Expand
Fully automated dose prediction using generative adversarial networks in prostate cancer patients
TLDR
The CT-based dose prediction could reduce the time required for both the iterative optimization process and the structure contouring, allowing physicians and dosimetrists to focus their expertise on more challenging cases. Expand
Overview of image-to-image translation by use of deep neural networks: denoising, super-resolution, modality conversion, and reconstruction in medical imaging
  • S. Kaji, S. Kida
  • Physics, Engineering
  • Radiological Physics and Technology
  • 2019
Since the advent of deep convolutional neural networks (DNNs), computer vision has seen an extremely rapid progress that has led to huge advances in medical imaging. Every year, many new methods areExpand
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