ArticleMedical dosimetry : official journal of the American Association of Medical Dosimetrists
Dose evaluation of MRI-based synthetic CT generated using a machine learning method for prostate cancer radiotherapy.
Article in Medical dosimetry : official journal of the American Association of Medical Dosimetrists. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 40 citations in OpenAlex.
- Clinical validation of MR imaging time reduction for substitute/synthetic CT generation for prostate MRI-only treatment planning.Physical and engineering sciences in medicine · 2023Trial
- Using multimodal PET+MR data as conditional generative adversarial network inputs improves pseudo-CT and attenuation correction estimates for brain PET/MR.American journal of nuclear medicine and molecular imaging · 2026Article
- Knowledge-based deep residual U-Net (DRU) for synthetic CT generation using a single MR volume for frameless radiosurgery.Journal of applied clinical medical physics · 2026Article
- Prostate MRI: bridging radiology and radiation oncology for precision radiation therapy.European radiology · 2025Article
- Innovative approaches in precision radiation oncology: advanced imaging technologies and challenges which shape the future of radiation therapy.Frontiers in medicine · 2025Review
- Artificial general intelligence for radiation oncology.Meta-radiology · 2023Article
- Machine Learning in Prostate MRI for Prostate Cancer: Current Status and Future Opportunities.Diagnostics (Basel, Switzerland) · 2022Review
- MRI classification using semantic random forest with auto-context model.Quantitative imaging in medicine and surgery · 2021Article
- Synthetic CT generation from weakly paired MR images using cycle-consistent GAN for MR-guided radiotherapy.Biomedical engineering letters · 2021Article
- 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 and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives.Diagnostics (Basel, Switzerland) · 2021Review
- Machine learning in quantitative PET: A review of attenuation correction and low-count image reconstruction methods.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) · 2020Review
- CT prostate segmentation based on synthetic MRI-aided deep attention fully convolution network.Medical physics · 2020Article
- Machine learning applications in prostate cancer magnetic resonance imaging.European radiology experimental · 2019Review
- MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks.Medical physics · 2019Article
- MRI-based treatment planning for proton radiotherapy: dosimetric validation of a deep learning-based liver synthetic CT generation method.Physics in medicine and biology · 2019Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
Magnetic resonance imaging (MRI)-only radiotherapy treatment planning is attractive since MRI provides superior soft tissue contrast over computed tomographies (CTs), without the ionizing radiation exposure. However, it requires the generation of a synthetic CT (SCT) from MRIs for patient setup and dose calculation. In this study, we aim to investigate the accuracy of dose calculation in prostate cancer radiotherapy using SCTs generated from MRIs using our learning-based method. We retrospectively investigated a total of 17 treatment plans from 10 patients, each having both planning CTs (pCT) and MRIs acquired before treatment. The SCT was registered to the pCT for generating SCT-based treatment plans. The original pCT-based plans served as ground truth. Clinically-relevant dose volume histogram (DVH) metrics were extracted from both ground truth and SCT-based plans for comparison and evaluation. Gamma analysis was performed for the comparison of absorbed dose distributions between SCT- and pCT-based plans of each patient. Gamma analysis of dose distribution on pCT and SCT within 1%/1 mm at 10% dose threshold showed greater than 99% pass rate. The average differences in DVH metrics for planning target volumes (PTVs) were less than 1%, and similar metrics for organs at risk (OAR) were not statistically different. The SCT images created from MR images using our proposed machine learning method are accurate for dose calculation in prostate cancer radiation treatment planning. This study also demonstrates the great potential for MRI to completely replace CT scans in the process of simulation and treatment planning. However, MR images are needed to further analyze geometric distortion effects. Digitally reconstructed radiograph (DRR) can be generated within our method, and their accuracy in patient setup needs further analysis.
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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.