ArticlePhysics in medicine and biology2017
Iterative framework for the joint segmentation and CT synthesis of MR images: application to MRI-only radiotherapy treatment planning.
Article in Physics in medicine and biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 45 citations in OpenAlex.
- Application of synthetic computed tomography based on a deep learning model in pelvic radiotherapy planning: A feasibility study.Journal of applied clinical medical physics · 2026Article
- Deep-Learning-Aided Evaluation of Spondylolysis Imaged with Ultrashort Echo Time Magnetic Resonance Imaging.Sensors (Basel, Switzerland) · 2023Article
- Compensation cycle consistent generative adversarial networks (Comp-GAN) for synthetic CT generation from MR scans with truncated anatomy.Medical physics · 2023Article
- Anomaly detection for the individual analysis of brain PET images.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Evaluation of a deep learning-based pelvic synthetic CT generation technique for MRI-based prostate proton treatment planning.Physics in medicine and biology · 2019Article
- Automatic Labeling of MR Brain Images Through the Hashing Retrieval Based Atlas Forest.Journal of medical systems · 2019Article
- Association of bone mineral density and fat fraction with magnetic susceptibility in inflamed trabecular bone.Magnetic resonance in medicine · 2019Article
- Synthetic CT Generation from MRI Using Generative Adversarial Networks for MR-Only Brain Radiotherapy Planning.Technology in cancer research & treatmentArticle
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Authors and funding
12 authors at 8 institutions in 3 countries.
Funding
Abstract
To tackle the problem of magnetic resonance imaging (MRI)-only radiotherapy treatment planning (RTP), we propose a multi-atlas information propagation scheme that jointly segments organs and generates pseudo x-ray computed tomography (CT) data from structural MR images (T1-weighted and T2-weighted). As the performance of the method strongly depends on the quality of the atlas database composed of multiple sets of aligned MR, CT and segmented images, we also propose a robust way of registering atlas MR and CT images, which combines structure-guided registration, and CT and MR image synthesis. We first evaluated the proposed framework in terms of segmentation and CT synthesis accuracy on 15 subjects with prostate cancer. The segmentations obtained with the proposed method were compared using the Dice score coefficient (DSC) to the manual segmentations. Mean DSCs of 0.73, 0.90, 0.77 and 0.90 were obtained for the prostate, bladder, rectum and femur heads, respectively. The mean absolute error (MAE) and the mean error (ME) were computed between the reference CTs (non-rigidly aligned to the MRs) and the pseudo CTs generated with the proposed method. The MAE was on average [Formula: see text] HU and the ME [Formula: see text] HU. We then performed a dosimetric evaluation by re-calculating plans on the pseudo CTs and comparing them to the plans optimised on the reference CTs. We compared the cumulative dose volume histograms (DVH) obtained for the pseudo CTs to the DVH obtained for the reference CTs in the planning target volume (PTV) located in the prostate, and in the organs at risk at different DVH points. We obtained average differences of [Formula: see text] in the PTV for [Formula: see text], and between [Formula: see text] and 0.05% in the PTV, bladder, rectum and femur heads for D
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