FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2513595145> ?p ?o ?g. }

• W2513595145 endingPage "6552" @default.
• W2513595145 startingPage "6531" @default.
• W2513595145 abstract "Magnetic resonance imaging (MRI)-guided attenuation correction (AC) of positron emission tomography (PET) data and/or radiation therapy (RT) treatment planning is challenged by the lack of a direct link between MRI voxel intensities and electron density. Therefore, even if this is not a trivial task, a pseudo-computed tomography (CT) image must be predicted from MRI alone. In this work, we propose a two-step (segmentation and fusion) atlas-based algorithm focusing on bone tissue identification to create a pseudo-CT image from conventional MRI sequences and evaluate its performance against the conventional MRI segmentation technique and a recently proposed multi-atlas approach. The clinical studies consisted of pelvic CT, PET and MRI scans of 12 patients with loco-regionally advanced rectal disease. In the first step, bone segmentation of the target image is optimized through local weighted atlas voting. The obtained bone map is then used to assess the quality of deformed atlases to perform voxel-wise weighted atlas fusion. To evaluate the performance of the method, a leave-one-out cross-validation (LOOCV) scheme was devised to find optimal parameters for the model. Geometric evaluation of the produced pseudo-CT images and quantitative analysis of the accuracy of PET AC were performed. Moreover, a dosimetric evaluation of volumetric modulated arc therapy photon treatment plans calculated using the different pseudo-CT images was carried out and compared to those produced using CT images serving as references. The pseudo-CT images produced using the proposed method exhibit bone identification accuracy of 0.89 based on the Dice similarity metric compared to 0.75 achieved by the other atlas-based method. The superior bone extraction resulted in a mean standard uptake value bias of -1.5 ± 5.0% (mean ± SD) in bony structures compared to -19.9 ± 11.8% and -8.1 ± 8.2% achieved by MRI segmentation-based (water-only) and atlas-guided AC. Dosimetric evaluation using dose volume histograms and the average difference between minimum/maximum absorbed doses revealed a mean error of less than 1% for the both target volumes and organs at risk. Two-dimensional (2D) gamma analysis of the isocenter dose distributions at 1%/1 mm criterion revealed pass rates of 91.40 ± 7.56%, 96.00 ± 4.11% and 97.67 ± 3.6% for MRI segmentation, atlas-guided and the proposed methods, respectively. The proposed method generates accurate pseudo-CT images from conventional Dixon MRI sequences with improved bone extraction accuracy. The approach is promising for potential use in PET AC and MRI-only or hybrid PET/MRI-guided RT treatment planning." @default.
• W2513595145 created "2016-09-16" @default.
• W2513595145 creator A5006149096 @default.
• W2513595145 creator A5007891293 @default.
• W2513595145 creator A5039181443 @default.
• W2513595145 creator A5049449255 @default.
• W2513595145 creator A5088193611 @default.
• W2513595145 date "2016-08-15" @default.
• W2513595145 modified "2023-10-01" @default.
• W2513595145 title "Atlas-guided generation of pseudo-CT images for MRI-only and hybrid PET–MRI-guided radiotherapy treatment planning" @default.
• W2513595145 cites W1859655790 @default.
• W2513595145 cites W1884695109 @default.
• W2513595145 cites W1934517153 @default.
• W2513595145 cites W1967121735 @default.
• W2513595145 cites W1969750491 @default.
• W2513595145 cites W1971098086 @default.
• W2513595145 cites W1971355095 @default.
• W2513595145 cites W1974481022 @default.
• W2513595145 cites W1984473052 @default.
• W2513595145 cites W1987869189 @default.
• W2513595145 cites W2005508273 @default.
• W2513595145 cites W2015897296 @default.
• W2513595145 cites W2020195866 @default.
• W2513595145 cites W2021177063 @default.
• W2513595145 cites W2025172079 @default.
• W2513595145 cites W2027981446 @default.
• W2513595145 cites W2035397698 @default.
• W2513595145 cites W2045375756 @default.
• W2513595145 cites W2049130824 @default.
• W2513595145 cites W2051725640 @default.
• W2513595145 cites W2055935372 @default.
• W2513595145 cites W2056282952 @default.
• W2513595145 cites W2057016865 @default.
• W2513595145 cites W2060706952 @default.
• W2513595145 cites W2066125032 @default.
• W2513595145 cites W2066839705 @default.
• W2513595145 cites W2069988023 @default.
• W2513595145 cites W2074054457 @default.
• W2513595145 cites W2080858163 @default.
• W2513595145 cites W2082615917 @default.
• W2513595145 cites W2083586222 @default.
• W2513595145 cites W2084851806 @default.
• W2513595145 cites W2086284908 @default.
• W2513595145 cites W2100495482 @default.
• W2513595145 cites W2110966329 @default.
• W2513595145 cites W2117340355 @default.
• W2513595145 cites W2118758783 @default.
• W2513595145 cites W2122350519 @default.
• W2513595145 cites W2127107259 @default.
• W2513595145 cites W2127890285 @default.
• W2513595145 cites W2129804985 @default.
• W2513595145 cites W2133287637 @default.
• W2513595145 cites W2140866726 @default.
• W2513595145 cites W2142082007 @default.
• W2513595145 cites W2151899391 @default.
• W2513595145 cites W2157025461 @default.
• W2513595145 cites W2162246940 @default.
• W2513595145 cites W2167157872 @default.
• W2513595145 cites W2267700533 @default.
• W2513595145 cites W2276599903 @default.
• W2513595145 cites W2291593693 @default.
• W2513595145 cites W2321172432 @default.
• W2513595145 cites W2535751244 @default.
• W2513595145 cites W2541676425 @default.
• W2513595145 cites W4366956157 @default.
• W2513595145 doi "https://doi.org/10.1088/0031-9155/61/17/6531" @default.
• W2513595145 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/27524504" @default.
• W2513595145 hasPublicationYear "2016" @default.
• W2513595145 type Work @default.
• W2513595145 sameAs 2513595145 @default.
• W2513595145 citedByCount "80" @default.
• W2513595145 countsByYear W25135951452017 @default.
• W2513595145 countsByYear W25135951452018 @default.
• W2513595145 countsByYear W25135951452019 @default.
• W2513595145 countsByYear W25135951452020 @default.
• W2513595145 countsByYear W25135951452021 @default.
• W2513595145 countsByYear W25135951452022 @default.
• W2513595145 countsByYear W25135951452023 @default.
• W2513595145 crossrefType "journal-article" @default.
• W2513595145 hasAuthorship W2513595145A5006149096 @default.
• W2513595145 hasAuthorship W2513595145A5007891293 @default.
• W2513595145 hasAuthorship W2513595145A5039181443 @default.
• W2513595145 hasAuthorship W2513595145A5049449255 @default.
• W2513595145 hasAuthorship W2513595145A5088193611 @default.
• W2513595145 hasConcept C105702510 @default.
• W2513595145 hasConcept C115961682 @default.
• W2513595145 hasConcept C123688308 @default.
• W2513595145 hasConcept C126838900 @default.
• W2513595145 hasConcept C143409427 @default.
• W2513595145 hasConcept C154945302 @default.
• W2513595145 hasConcept C163716698 @default.
• W2513595145 hasConcept C201645570 @default.
• W2513595145 hasConcept C2775842073 @default.
• W2513595145 hasConcept C2776673561 @default.
• W2513595145 hasConcept C2989005 @default.
• W2513595145 hasConcept C41008148 @default.
• W2513595145 hasConcept C509974204 @default.
• W2513595145 hasConcept C54170458 @default.

• next