ArticleProceedings of SPIE--the International Society for Optical Engineering2017
Pseudo CT Estimation from MRI Using Patch-based Random Forest.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.
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Who cites it
23 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy.Frontiers in radiology · 2024Pooled it
- Adoption of Hybrid MRI-Linac Systems for the Treatment of Brain Tumors: A Systematic Review of the Current Literature Regarding Clinical and Technical Features.Technology in cancer research & treatmentPooled it
- [Cross modal translation of magnetic resonance imaging and computed tomography images based on diffusion generative adversarial networks].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Innovative approaches in precision radiation oncology: advanced imaging technologies and challenges which shape the future of radiation therapy.Frontiers in medicine · 2025Review
- A review of PET attenuation correction methods for PET-MR.EJNMMI physics · 2023Review
- MRI classification using semantic random forest with auto-context model.Quantitative imaging in medicine and surgery · 2021Article
- Tissue outcome prediction in hyperacute ischemic stroke: Comparison of machine learning models.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 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
- A review on medical imaging synthesis using deep learning and its clinical applications.Journal of applied clinical medical physics · 2021Review
- Performance of deep learning synthetic CTs for MR-only brain radiation therapy.Journal of applied clinical medical physics · 2021Article
- MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation.Journal of digital imaging · 2020Review
- Male pelvic multi-organ segmentation aided by CBCT-based synthetic MRI.Physics in medicine and biology · 2020Article
- MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks.Medical physics · 2019Article
- Machine-learning based classification of glioblastoma using delta-radiomic features derived from dynamic susceptibility contrast enhanced magnetic resonance images: Introduction.Quantitative imaging in medicine and surgery · 2019Article
- MRI-based synthetic CT generation using semantic random forest with iterative refinement.Physics in medicine and biology · 2019Article
- MRI-based attenuation correction for brain PET/MRI based on anatomic signature and machine learning.Physics in medicine and biology · 2019Article
- MRI-based pseudo CT synthesis using anatomical signature and alternating random forest with iterative refinement model.Journal of medical imaging (Bellingham, Wash.) · 2018Article
- A Denoising Algorithm for CT Image Using Low-rank Sparse Coding.Proceedings of SPIE--the International Society for Optical Engineering · 2018Article
- High-resolution CT Image Retrieval Using Sparse Convolutional Neural Network.Proceedings of SPIE--the International Society for Optical Engineering · 2018Article
- Improving Image Quality of Cone-Beam CT Using Alternating Regression Forest.Proceedings of SPIE--the International Society for Optical Engineering · 2018Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Recently, MR simulators gain popularity because of unnecessary radiation exposure of CT simulators being used in radiation therapy planning. We propose a method for pseudo CT estimation from MR images based on a patch-based random forest. Patient-specific anatomical features are extracted from the aligned training images and adopted as signatures for each voxel. The most robust and informative features are identified using feature selection to train the random forest. The well-trained random forest is used to predict the pseudo CT of a new patient. This prediction technique was tested with human brain images and the prediction accuracy was assessed using the original CT images. Peak signal-to-noise ratio (PSNR) and feature similarity (FSIM) indexes were used to quantify the differences between the pseudo and original CT images. The experimental results showed the proposed method could accurately generate pseudo CT images from MR images. In summary, we have developed a new pseudo CT prediction method based on patch-based random forest, demonstrated its clinical feasibility, and validated its prediction accuracy. This pseudo CT prediction technique could be a useful tool for MRI-based radiation treatment planning and attenuation correction in a PET/MRI scanner.
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