ArticleJournal of medical imaging (Bellingham, Wash.)2018
Magnetic resonance imaging-based pseudo computed tomography using anatomic signature and joint dictionary learning.
Article in Journal of medical imaging (Bellingham, Wash.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Deep Learning for Medical Image-Based Cancer Diagnosis.Cancers · 2023Review
- Learning-Based Stopping Power Mapping on Dual-Energy CT for Proton Radiation Therapy.International journal of particle therapy · 2021Article
- Data-driven dose calculation algorithm based on deep U-Net.Physics in medicine and biology · 2020Article
- Multimodal MRI synthesis using unified generative adversarial networks.Medical physics · 2020Article
- Machine learning in quantitative PET: A review of attenuation correction and low-count image reconstruction methods.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) · 2020Review
- Deep learning-based attenuation correction in the absence of structural information for whole-body positron emission tomography imaging.Physics in medicine and biology · 2020Article
- Whole-body PET estimation from low count statistics using cycle-consistent generative adversarial networks.Physics in medicine and biology · 2019Article
- Evaluation of a deep learning-based pelvic synthetic CT generation technique for MRI-based prostate proton treatment planning.Physics in medicine and biology · 2019Article
- MRI-based treatment planning for liver stereotactic body radiotherapy: validation of a deep learning-based synthetic CT generation method.The British journal of radiology · 2019Article
- MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks.Medical physics · 2019Article
- MRI-based synthetic CT generation using semantic random forest with iterative refinement.Physics in medicine and biology · 2019Article
- Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation.Medical physics · 2019Article
- Learning-based CBCT correction using alternating random forest based on auto-context model.Medical physics · 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
- Dose evaluation of MRI-based synthetic CT generated using a machine learning method for prostate cancer radiotherapy.Medical dosimetry : official journal of the American Association of Medical DosimetristsArticle
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Magnetic resonance imaging (MRI) provides a number of advantages over computed tomography (CT) for radiation therapy treatment planning; however, MRI lacks the key electron density information necessary for accurate dose calculation. We propose a dictionary-learning-based method to derive electron density information from MRIs. Specifically, we first partition a given MR image into a set of patches, for which we used a joint dictionary learning method to directly predict a CT patch as a structured output. Then a feature selection method is used to ensure prediction robustness. Finally, we combine all the predicted CT patches to obtain the final prediction for the given MR image. This prediction technique was validated for a clinical application using 14 patients with brain MR and CT images. The peak signal-to-noise ratio (PSNR), mean absolute error (MAE), normalized cross-correlation (NCC) indices and similarity index (SI) for air, soft-tissue and bone region were used to quantify the prediction accuracy. The mean ± std of PSNR, MAE, and NCC were:
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Registered trials
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 Socratic graph.