ArticleMedical physics2015
Generation of brain pseudo-CTs using an undersampled, single-acquisition UTE-mDixon pulse sequence and unsupervised clustering.
Article in Medical physics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Comprehensive evaluation of a deep learning-based synthetic CT model for MR-only radiotherapy across multiple anatomical sites.Physics and imaging in radiation oncology · 2026Article
- Validating clinical feasibility of MRCAT and deep learning-based synthetic CT images for cervical cancer patient.Journal of applied clinical medical physics · 2025Article
- Advancements in synthetic CT generation from MRI: A review of techniques, and trends in radiation therapy planning.Journal of applied clinical medical physics · 2024Review
- Adversarial robustness improvement for X-ray bone segmentation using synthetic data created from computed tomography scans.Scientific reports · 2024Article
- Clinical feasibility of deep learning-based synthetic CT images from T2-weighted MR images for cervical cancer patients compared to MRCAT.Scientific reports · 2024Article
- Fuzzy C-means clustering algorithm applied in computed tomography images of patients with intracranial hemorrhage.Frontiers in neuroinformatics · 2024Article
- A review of PET attenuation correction methods for PET-MR.EJNMMI physics · 2023Review
- Abdominopelvic MR to CT registration using a synthetic CT intermediate.Journal of applied clinical medical physics · 2022Article
- MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation.Journal of digital imaging · 2020Review
- MR-based PET attenuation correction using a combined ultrashort echo time/multi-echo Dixon acquisition.Medical physics · 2020Article
- Generation of Pseudo-CT using High-Degree Polynomial Regression on Dual-Contrast Pelvic MRI Data.Scientific reports · 2020Article
- mDixon-Based Synthetic CT Generation for PET Attenuation Correction on Abdomen and Pelvis Jointly Using Transfer Fuzzy Clustering and Active Learning-Based Classification.IEEE transactions on medical imaging · 2020Article
- Deep learning-based MR-to-CT synthesis: The influence of varying gradient echo-based MR images as input channels.Magnetic resonance in medicine · 2020Article
- Novel adversarial semantic structure deep learning for MRI-guided attenuation correction in brain PET/MRI.European journal of nuclear medicine and molecular imaging · 2019Article
- UTE-mDixon-based thorax synthetic CT generation.Medical physics · 2019Article
- Feasibility of generating synthetic CT from T1-weighted MRI using a linear mixed-effects regression model.Biomedical physics & engineering express · 2019Article
- A Novel Distributed Multitask Fuzzy Clustering Algorithm for Automatic MR Brain Image Segmentation.Journal of medical systems · 2019Article
- Neuromodulation with single-element transcranial focused ultrasound in human thalamus.Human brain mapping · 2018Article
- Multiatlas approach with local registration goodness weighting for MRI-based electron density mapping of head and neck anatomy.Medical physics · 2017Article
- Robust Estimation of Electron Density From Anatomic Magnetic Resonance Imaging of the Brain Using a Unifying Multi-Atlas Approach.International journal of radiation oncology, biology, physics · 2017Article
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12 authors.
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Abstract
purposeMR-based pseudo-CT has an important role in MR-based radiation therapy planning and PET attenuation correction. The purpose of this study is to establish a clinically feasible approach, including image acquisition, correction, and CT formation, for pseudo-CT generation of the brain using a single-acquisition, undersampled ultrashort echo time (UTE)-mDixon pulse sequence.
methodsNine patients were recruited for this study. For each patient, a 190-s, undersampled, single acquisition UTE-mDixon sequence of the brain was acquired (TE = 0.1, 1.5, and 2.8 ms). A novel method of retrospective trajectory correction of the free induction decay (FID) signal was performed based on point-spread functions of three external MR markers. Two-point Dixon images were reconstructed using the first and second echo data (TE = 1.5 and 2.8 ms). R2(∗) images (1/T2(∗)) were then estimated and were used to provide bone information. Three image features, i.e., Dixon-fat, Dixon-water, and R2(∗), were used for unsupervised clustering. Five tissue clusters, i.e., air, brain, fat, fluid, and bone, were estimated using the fuzzy c-means (FCM) algorithm. A two-step, automatic tissue-assignment approach was proposed and designed according to the prior information of the given feature space. Pseudo-CTs were generated by a voxelwise linear combination of the membership functions of the FCM. A low-dose CT was acquired for each patient and was used as the gold standard for comparison.
resultsThe contrast and sharpness of the FID images were improved after trajectory correction was applied. The mean of the estimated trajectory delay was 0.774 μs (max: 1.350 μs; min: 0.180 μs). The FCM-estimated centroids of different tissue types showed a distinguishable pattern for different tissues, and significant differences were found between the centroid locations of different tissue types. Pseudo-CT can provide additional skull detail and has low bias and absolute error of estimated CT numbers of voxels (-22 ± 29 HU and 130 ± 16 HU) when compared to low-dose CT.
conclusionsThe MR features generated by the proposed acquisition, correction, and processing methods may provide representative clustering information and could thus be used for clinical pseudo-CT generation.
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