ArticleJournal of the American Medical Informatics Association : JAMIA
Multiscale segmentation of the skull in MR images for MRI-based attenuation correction of combined MR/PET.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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18 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- A systematic literature review: deep learning techniques for synthetic medical image generation and their applications in radiotherapy.Frontiers in radiology · 2024Pooled it
- 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
- A review on medical imaging synthesis using deep learning and its clinical applications.Journal of applied clinical medical physics · 2021Review
- MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation.Journal of digital imaging · 2020Review
- 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
- MRI-based synthetic CT generation using semantic random forest with iterative refinement.Physics in medicine and biology · 2019Article
- An Automatic Parameter Decision System of Bilateral Filtering with GPU-Based Acceleration for Brain MR Images.Journal of digital imaging · 2019Article
- MRI-based pseudo CT synthesis using anatomical signature and alternating random forest with iterative refinement model.Journal of medical imaging (Bellingham, Wash.) · 2018Article
- Magnetic resonance imaging-based pseudo computed tomography using anatomic signature and joint dictionary learning.Journal of medical imaging (Bellingham, Wash.) · 2018Article
- Pseudo CT Estimation from MRI Using Patch-based Random Forest.Proceedings of SPIE--the International Society for Optical Engineering · 2017Article
- A review of substitute CT generation for MRI-only radiation therapy.Radiation oncology (London, England) · 2017Review
- MR Imaging-Guided Attenuation Correction of PET Data in PET/MR Imaging.PET clinics · 2016Review
- Trends in biomedical informatics: automated topic analysis of JAMIA articles.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- Multi-contrast attenuation map synthesis for PET/MR scanners: assessment on FDG and Florbetapir PET tracers.European journal of nuclear medicine and molecular imaging · 2015Article
- A Minimum Spanning Forest Based Hyperspectral Image Classification Method for Cancerous Tissue Detection.Proceedings of SPIE--the International Society for Optical Engineering · 2014Article
- A panel of clinical and neuropathological features of cerebrovascular disease through the novel neuroimaging methods.Dementia & neuropsychologiaArticle
- Biomedical imaging informatics in the era of precision medicine: progress, challenges, and opportunities.Journal of the American Medical Informatics Association : JAMIAArticle
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
BACKGROUND AND
objectiveCombined magnetic resonance/positron emission tomography (MR/PET) is a relatively new, hybrid imaging modality. MR-based attenuation correction often requires segmentation of the bone on MR images. In this study, we present an automatic segmentation method for the skull on MR images for attenuation correction in brain MR/PET applications. MATERIALS AND
methodsOur method transforms T1-weighted MR images to the Radon domain and then detects the features of the skull image. In the Radon domain we use a bilateral filter to construct a multiscale image series. For the repeated convolution we increase the spatial smoothing in each scale and make the width of the spatial and range Gaussian function doubled in each scale. Two filters with different kernels along the vertical direction are applied along the scales from the coarse to fine levels. The results from a coarse scale give a mask for the next fine scale and supervise the segmentation in the next fine scale. The use of the multiscale bilateral filtering scheme is to improve the robustness of the method for noise MR images. After combining the two filtered sinograms, the reciprocal binary sinogram of the skull is obtained for the reconstruction of the skull image.
resultsThis method has been tested with brain phantom data, simulated brain data, and real MRI data. For real MRI data the Dice overlap ratios are 92.2%±1.9% between our segmentation and manual segmentation.
conclusionsThe multiscale segmentation method is robust and accurate and can be used for MRI-based attenuation correction in combined MR/PET.
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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.