ArticlePhysics in medicine and biology2020
Abdominal synthetic CT generation from MR Dixon images using a U-net trained with 'semi-synthetic' CT data.
Article in Physics in medicine and biology, 2020. 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, 31 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
- Generative Models for Medical Image Creation and Translation: A Scoping Review.Sensors (Basel, Switzerland) · 2026Article
- Simulation-free workflow for lattice radiation therapy using deep learning predicted synthetic computed tomography: A feasibility study.Journal of applied clinical medical physics · 2025Article
- Feasibility study on the clinical application of CT-based synthetic brain T1-weighted MRI: comparison with conventional T1-weighted MRI.European radiology · 2024Article
- Artificial general intelligence for radiation oncology.Meta-radiology · 2023Article
- Machine Learning for Medical Image Translation: A Systematic Review.Bioengineering (Basel, Switzerland) · 2023Review
- Patient-specific quality assurance strategies for synthetic computed tomography in magnetic resonance-only radiotherapy of the abdomen.Physics and imaging in radiation oncology · 2023Article
- Automated deep learning auto-segmentation of air volumes for MRI-guided online adaptive radiation therapy of abdominal tumors.Physics in medicine and biology · 2023Article
- Article
- Synthetic CT in Carbon Ion Radiotherapy of the Abdominal Site.Bioengineering (Basel, Switzerland) · 2023Article
- Synthetic computed tomography for low-field magnetic resonance-guided radiotherapy in the abdomen.Physics and imaging in radiation oncology · 2022Article
- Dosimetric impact of geometric distortions in an MRI-only proton therapy workflow for lung, liver and pancreas.Zeitschrift fur medizinische Physik · 2022Article
- A Deep Learning Framework for Segmenting Brain Tumors Using MRI and Synthetically Generated CT Images.Sensors (Basel, Switzerland) · 2022Article
- Magnetic Resonance Imaging-Guided Adaptive Radiotherapy for Colorectal Liver Metastases.Cancers · 2021Review
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
Abstract
Magnetic resonance imaging (MRI) is gaining popularity in guiding radiation treatment for intrahepatic cancers due to its superior soft tissue contrast and potential of monitoring individual motion and liver function. This study investigates a deep learning-based method that generates synthetic CT volumes from T1-weighted MR Dixon images in support of MRI-based intrahepatic radiotherapy treatment planning. Training deep neutral networks for this purpose has been challenged by mismatches between CT and MR images due to motion and different organ filling status. This work proposes to resolve such challenge by generating 'semi-synthetic' CT images from rigidly aligned CT and MR image pairs. Contrasts within skeletal elements of the 'semi-synthetic' CT images were determined from CT images, while contrasts of soft tissue and air volumes were determined from voxel-wise intensity classification results on MR images. The resulting 'semi-synthetic' CT images were paired with their corresponding MR images and used to train a simple U-net model without adversarial components. MR and CT scans of 46 patients were investigated and the proposed method was evaluated for 31 patients with clinical radiotherapy plans, using 3-fold cross validation. The averaged mean absolute errors between synthetic CT and CT images across patients were 24.10 HU for liver, 28.62 HU for spleen, 47.05 HU for kidneys, 29.79 HU for spinal cord, 105.68 HU for lungs and 110.09 HU for vertebral bodies. VMAT and IMRT plans were optimized using CT-derived electron densities, and doses were recalculated using corresponding synthetic CT-derived density grids. Resulting dose differences to planning target volumes and various organs at risk were small, with the average difference less than 0.15 Gy for all dose metrics evaluated. The similarities in both image intensity and radiation dose distributions between CT and synthetic CT volumes demonstrate the accuracy of the method and its potential in supporting MRI-only radiotherapy treatment planning.
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