ArticleJournal of applied clinical medical physics2021
Deep learning-based synthetic CT generation for MR-only radiotherapy of prostate cancer patients with 0.35T MRI linear accelerator.
Article in Journal of applied clinical medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy.Frontiers in radiology · 2024Pooled it
- 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
- A comprehensive comparative study of generative adversarial network architectures for synthetic computed tomography generation in the abdomen.Medical physics · 2025Article
- Synthetic CT generation from CBCT using deep learning for adaptive radiotherapy in prostate cancer.Frontiers in radiology · 2025Article
- Dosimetric evaluations using cycle-consistent generative adversarial network synthetic CT for MR-guided adaptive radiation therapy.Frontiers in oncology · 2025Article
- Machine Learning for Medical Image Translation: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Using RegGAN to generate synthetic CT images from CBCT images acquired with different linear accelerators.BMC cancer · 2023Article
- Robot-assisted screw fixation in a cadaver utilizing magnetic resonance imaging-based synthetic computed tomography: toward radiation-free spine surgery. Illustrative case.Journal of neurosurgery. Case lessons · 2023Article
- Article
- Evaluation of MRI-only based online adaptive radiotherapy of abdominal region on MR-linac.Journal of applied clinical medical physics · 2023Article
- A multi-centric evaluation of self-learning GAN based pseudo-CT generation software for low field pelvic magnetic resonance imaging.Frontiers in oncology · 2023Article
- Synthetic computed tomography for low-field magnetic resonance-guided radiotherapy in the abdomen.Physics and imaging in radiation oncology · 2022Article
- A Deep Learning Framework for Segmenting Brain Tumors Using MRI and Synthetically Generated CT Images.Sensors (Basel, Switzerland) · 2022Article
- Deep learning-based synthetic CT generation for MR-only radiotherapy of prostate cancer patients with 0.35T MRI linear accelerator.Journal of applied clinical medical physics · 2021Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
purposeTo develop a deep learning model to generate synthetic CT for MR-only radiotherapy of prostate cancer patients treated with 0.35 T MRI linear accelerator. MATERIALS AND
methodsA U-NET convolutional neural network was developed to translate 0.35 T TRUFI MRI into electron density map using a novel cost function equalizing the contribution of various tissue types including fat, muscle, bone, and background air in training. The impact of training time, dataset size, image standardization, and data augmentation approaches was also quantified. Mean absolute error (MAE) between synthetic and planning CTs was calculated to measure the goodness of the model.
resultsWith 20 patients in training, our U-NET model has the potential to generate synthetic CT with a MAE of about 29.68 ± 4.41, 16.34 ± 2.67, 23.36 ± 2.85, and 105.90 ± 22.80 HU over the entire body, fat, muscle, and bone tissues, respectively. As expected, we found that the number of patients used for training and MAE are nonlinearly correlated. Data augmentation and our proposed loss function were effective to improve MAE by ~9% and ~18% in bony voxels, respectively. Increasing the training time and image standardization did not improve the accuracy of the model.
conclusionA U-NET model has been developed and tested numerically to generate synthetic CT from 0.35T TRUFI MRI for MR-only radiotherapy of prostate cancer patients. Dosimetric evaluation using a large and independent dataset warrants the validity of the proposed model and the actual number of patients needed for the safe usage of the model in routine clinical workflow.
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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 OpenQuestion graph.