ArticleThe British journal of radiology2022
Learning-based synthetic dual energy CT imaging from single energy CT for stopping power ratio calculation in proton radiation therapy.
Article in The British journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Recent advances in applying machine learning to proton radiotherapy.Biomedical physics & engineering express · 2025Pooled it
- CT-based synthetic contrast-enhanced dual-energy CT generation using conditional denoising diffusion probabilistic model.Physics in medicine and biology · 2024Article
- The synthesis of high-energy CT images from low-energy CT images using an improved cycle generative adversarial network.Quantitative imaging in medicine and surgery · 2022Article
- Image synthesis of effective atomic number images using a deep convolutional neural network-based generative adversarial network.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation Oncology · 2022Article
- A review of deep learning based methods for medical image multi-organ segmentation.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2021Review
- Synthetic dual-energy CT for MRI-only based proton therapy treatment planning using label-GAN.Physics in medicine and biology · 2021Article
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10 authors.
Funding
Abstract
objectiveDual energy CT (DECT) has been shown to estimate stopping power ratio (SPR) map with a higher accuracy than conventional single energy CT (SECT) by obtaining the energy dependence of photon interactions. This work presents a learning-based method to synthesize DECT images from SECT image for proton radiotherapy.
methodsThe proposed method uses a residual attention generative adversarial network. Residual blocks with attention gates were used to force the model to focus on the difference between DECT images and SECT images. To evaluate the accuracy of the method, we retrospectively investigated 70 head-and-neck cancer patients whose DECT and SECT scans were acquired simultaneously. The model was trained to generate both a high and low energy DECT image based on a SECT image. The generated synthetic low and high DECT images were evaluated against the true DECT images using leave-one-out cross-validation. To evaluate our method in the context of a practical application, we generated SPR maps from synthetic DECT (sDECT) using a dual-energy based stoichiometric method and compared the SPR maps to those generated from DECT. A dosimetric comparison for dose obtained from DECT was performed against that derived from sDECT.
resultsThe mean of mean absolute error, peak signal-to-noise ratio and normalized cross-correlation for the synthetic high and low energy CT images was 36.9 HU, 29.3 dB, 0.96 and 35.8 HU, 29.2 dB, and 0.96, respectively. The corresponding SPR maps generated from synthetic DECT showed an average normalized mean square deviation of about 1% with reduced noise level and artifacts than those from original DECT. Dose-volume histogram (DVH) metrics for the clinical target volume agree within 1% between the DECT and sDECT calculated dose.
conclusionOur method synthesized accurate DECT images and showed a potential feasibility for proton SPR map generation. ADVANCES IN KNOWLEDGE: This study investigated a learning-based method to synthesize DECT images from SECT image for proton radiotherapy.
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