ArticlePhysics in medicine and biology2021
Synthetic dual-energy CT for MRI-only based proton therapy treatment planning using label-GAN.
Article in Physics in medicine and biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in MRI-guided radiation therapy: A systematic review.Journal of applied clinical medical physics · 2024Pooled it
- Deep learning-based generation of direct stopping power ratio maps for MR-only proton therapy of primary brain tumor patients.Physics and imaging in radiation oncology · 2026Article
- Generative Models for Medical Image Creation and Translation: A Scoping Review.Sensors (Basel, Switzerland) · 2026Article
- Dosimetric evaluation of magnetic resonance imaging based synthetic computed tomography for head and neck photon and proton therapy.Journal of applied clinical medical physics · 2025Article
- Evaluation of magnetic resonance imaging derived synthetic computed tomography for proton therapy planning in prostate cancer.Physics and imaging in radiation oncology · 2024Article
- MRI-only based material mass density and relative stopping power estimation via deep learning for proton therapy: a preliminary study.Scientific reports · 2024Article
- Enhancing Diagnostic Images to Improve the Performance of the Segment Anything Model in Medical Image Segmentation.Bioengineering (Basel, Switzerland) · 2024Article
- Deep learning-based synthetic dose-weighted LET map generation for intensity modulated proton therapy.Physics in medicine and biology · 2024Article
- A review of the clinical introduction of 4D particle therapy research concepts.Physics and imaging in radiation oncology · 2024Review
- Current and future developments of synthetic computed tomography generation for radiotherapy.Physics and imaging in radiation oncology · 2023Article
- Possibilities and challenges when using synthetic computed tomography in an adaptive carbon-ion treatment workflow.Zeitschrift fur medizinische Physik · 2023Article
- Article
- Image-to-image translation of label-free molecular vibrational images for a histopathological review using the UNet+/seg-cGAN model.Biomedical optics express · 2022Article
- Artificial Intelligence in Radiation Therapy.IEEE transactions on radiation and plasma medical sciences · 2022Article
- Management of Motion and Anatomical Variations in Charged Particle Therapy: Past, Present, and Into the Future.Frontiers in oncology · 2022Review
- 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
- Artificial Intelligence in Proton Therapy: What Works, What Does Not, and What Is Next.Cancer journal (Sudbury, Mass.)Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
MRI-only treatment planning is highly desirable in the current proton radiation therapy workflow due to its appealing advantages such as bypassing MR-CT co-registration, avoiding x-ray CT exposure dose and reduced medical cost. However, MRI alone cannot provide stopping power ratio (SPR) information for dose calculations. Given that dual energy CT (DECT) can estimate SPR with higher accuracy than conventional single energy CT, we propose a deep learning-based method in this study to generate synthetic DECT (sDECT) from MRI to calculate SPR. Since the contrast difference between high-energy and low-energy CT (LECT) is important, and in order to accurately model this difference, we propose a novel label generative adversarial network-based model which can not only discriminate the realism of sDECT but also differentiate high-energy CT (HECT) and LECT from DECT. A cohort of 57 head-and-neck cancer patients with DECT and MRI pairs were used to validate the performance of the proposed framework. The results of sDECT and its derived SPR maps were compared with clinical DECT and the corresponding SPR, respectively. The mean absolute error for synthetic LECT and HECT were 79.98 ± 18.11 HU and 80.15 ± 16.27 HU, respectively. The corresponding SPR maps generated from sDECT showed a normalized mean absolute error as 5.22% ± 1.23%. By comparing with the traditional Cycle GANs, our proposed method significantly improves the accuracy of sDECT. The results indicate that on our dataset, the sDECT image form MRI is close to planning DECT, and thus shows promising potential for generating SPR maps for proton therapy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.