ArticleJournal of medical imaging (Bellingham, Wash.)2018
MRI-based pseudo CT synthesis using anatomical signature and alternating random forest with iterative refinement model.
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 17 papers.
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Who cites it
17 citing papers in PubMed.
- Synthetic CT imaging for pelvic and abdominal MR-only radiotherapy: Clinical validation on a 1.5T MR-Linac.Journal of applied clinical medical physics · 2026Article
- MRI-only based material mass density and relative stopping power estimation via deep learning for proton therapy: a preliminary study.Scientific reports · 2024Article
- Advancing medical imaging with language models: featuring a spotlight on ChatGPT.Physics in medicine and biology · 2024Review
- A review of PET attenuation correction methods for PET-MR.EJNMMI physics · 2023Review
- Evaluation of synthetically generated computed tomography for use in transcranial focused ultrasound procedures.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Unsupervised-learning-based method for chest MRI-CT transformation using structure constrained unsupervised generative attention networks.Scientific reports · 2022Article
- 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
- 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
- Deep learning-based image quality improvement for low-dose computed tomography simulation in radiation therapy.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks.Medical physics · 2019Article
- MRI-based treatment planning for proton radiotherapy: dosimetric validation of a deep learning-based liver synthetic CT generation method.Physics in medicine and biology · 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 Proton Treatment Planning for Base of Skull Tumors.International journal of particle therapy · 2019Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
We develop a learning-based method to generate patient-specific pseudo computed tomography (CT) from routinely acquired magnetic resonance imaging (MRI) for potential MRI-based radiotherapy treatment planning. The proposed pseudo CT (PCT) synthesis method consists of a training stage and a synthesizing stage. During the training stage, patch-based features are extracted from MRIs. Using a feature selection, the most informative features are identified as an anatomical signature to train a sequence of alternating random forests based on an iterative refinement model. During the synthesizing stage, we feed the anatomical signatures extracted from an MRI into the sequence of well-trained forests for a PCT synthesis. Our PCT was compared with original CT (ground truth) to quantitatively assess the synthesis accuracy. The mean absolute error, peak signal-to-noise ratio, and normalized cross-correlation indices 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.