ArticlePhysics in medicine and biology2019
MRI-based synthetic CT generation using semantic random forest with iterative refinement.
Article in Physics in medicine and biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic accuracy of MRI-based synthetic CT compared to conventional CT in spinal and sacroiliac disorders: a systematic review.Skeletal radiology · 2026Pooled it
- MR-based skull parametric modeling for blood-brain barrier opening in non-human primates.Scientific reports · 2026Article
- A proof-of-concept study of multitask learning for cranial synthetic CT generation across heterogeneous MRI field strengths.Medical physics · 2026Article
- Synthetic CT generation based on multi-sequence MR using CycleGAN for head and neck MRI-only planning.Biomedical engineering letters · 2024Article
- Advancements in synthetic CT generation from MRI: A review of techniques, and trends in radiation therapy planning.Journal of applied clinical medical physics · 2024Review
- Feasibility study on the clinical application of CT-based synthetic brain T1-weighted MRI: comparison with conventional T1-weighted MRI.European radiology · 2024Article
- ReeGAN: MRI image edge-preserving synthesis based on GANs trained with misaligned data.Medical & biological engineering & computing · 2024Article
- MRI-only based material mass density and relative stopping power estimation via deep learning for proton therapy: a preliminary study.Scientific reports · 2024Article
- Synthesis of magnetic resonance images from computed tomography data using convolutional neural network with contextual loss function.Quantitative imaging in medicine and surgery · 2022Article
- Imaging of Bone in the Head and Neck Region, is There More Than CT?Current radiology reports · 2022Review
- MRI classification using semantic random forest with auto-context model.Quantitative imaging in medicine and surgery · 2021Article
- Synthetic digital reconstructed radiographs for MR-only robotic stereotactic radiation therapy: A proof of concept.Computers in biology and medicine · 2021Article
- Knowledge-based radiation treatment planning: A data-driven method survey.Journal of applied clinical medical physics · 2021Review
- 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
- Synthetic dual-energy CT for MRI-only based proton therapy treatment planning using label-GAN.Physics in medicine and biology · 2021Article
- Artificial intelligence and machine learning for medical imaging: A technology review.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
- Learning-Based Stopping Power Mapping on Dual-Energy CT for Proton Radiation Therapy.International journal of particle therapy · 2021Article
- Comparison of deep learning synthesis of synthetic CTs using clinical MRI inputs.Physics in medicine and biology · 2020Article
- Multimodal MRI synthesis using unified generative adversarial networks.Medical physics · 2020Article
- Deep learning in medical image registration: a review.Physics in medicine and biology · 2020Review
Corrections and comments
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Authors and funding
11 authors.
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Abstract
Target delineation for radiation therapy treatment planning often benefits from magnetic resonance imaging (MRI) in addition to x-ray computed tomography (CT) due to MRI's superior soft tissue contrast. MRI-based treatment planning could reduce systematic MR-CT co-registration errors, medical cost, radiation exposure, and simplify clinical workflow. However, MRI-only based treatment planning is not widely used to date because treatment-planning systems rely on the electron density information provided by CTs to calculate dose. Additionally, air and bone regions are difficult to separategiven their similar intensities in MR imaging. The purpose of this work is to develop a learning-based method to generate patient-specific synthetic CT (sCT) from a routine anatomical MRI for use in MRI-only radiotherapy treatment planning. An auto-context model with patch-based anatomical features was integrated into a classification random forest to generate and improve semantic information. The semantic information along with anatomical features was then used to train a series of regression random forests based on the auto-context model. After training, the sCT of a new MRI can be generated by feeding anatomical features extracted from the MRI into the well-trained classification and regression random forests. The proposed algorithm was evaluated using 14 patient datasets withT1-weighted MR and corresponding CT images of the brain. The mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and normalized cross correlation (NCC) were 57.45 ± 8.45 HU, 28.33 ± 1.68 dB, and 0.97 ± 0.01. We also compared the difference between dose maps calculated on the sCT and those on the original CT, using the same plan parameters. The average DVH differences among all patients are less than 0.2 Gy for PTVs, and less than 0.02 Gy for OARs. The sCT generation by the proposed method allows for dose calculation based MR imaging alone, and may be a useful tool for MRI-based radiation treatment planning.
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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.