FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2405268213> ?p ?o ?g. }

• W2405268213 endingPage "1641" @default.
• W2405268213 startingPage "1635" @default.
• W2405268213 abstract "Attenuation correction is important for PET reconstruction. In PET/MR, MR intensities are not directly related to attenuation coefficients that are needed in PET imaging. The attenuation coefficient map can be derived from CT images. Therefore, prediction of CT substitutes from MR images is desired for attenuation correction in PET/MR.This study presents a patch-based method for CT prediction from MR images, generating attenuation maps for PET reconstruction. Because no global relation exists between MR and CT intensities, we propose local diffeomorphic mapping (LDM) for CT prediction. In LDM, we assume that MR and CT patches are located on 2 nonlinear manifolds, and the mapping from the MR manifold to the CT manifold approximates a diffeomorphism under a local constraint. Locality is important in LDM and is constrained by the following techniques. The first is local dictionary construction, wherein, for each patch in the testing MR image, a local search window is used to extract patches from training MR/CT pairs to construct MR and CT dictionaries. The k-nearest neighbors and an outlier detection strategy are then used to constrain the locality in MR and CT dictionaries. Second is local linear representation, wherein, local anchor embedding is used to solve MR dictionary coefficients when representing the MR testing sample. Under these local constraints, dictionary coefficients are linearly transferred from the MR manifold to the CT manifold and used to combine CT training samples to generate CT predictions.Our dataset contains 13 healthy subjects, each with T1- and T2-weighted MR and CT brain images. This method provides CT predictions with a mean absolute error of 110.1 Hounsfield units, Pearson linear correlation of 0.82, peak signal-to-noise ratio of 24.81 dB, and Dice in bone regions of 0.84 as compared with real CTs. CT substitute-based PET reconstruction has a regression slope of 1.0084 and R2 of 0.9903 compared with real CT-based PET.In this method, no image segmentation or accurate registration is required. Our method demonstrates superior performance in CT prediction and PET reconstruction compared with competing methods." @default.
• W2405268213 created "2016-06-24" @default.
• W2405268213 creator A5006342956 @default.
• W2405268213 creator A5012139704 @default.
• W2405268213 creator A5026616191 @default.
• W2405268213 creator A5044127793 @default.
• W2405268213 creator A5070483964 @default.
• W2405268213 creator A5083537717 @default.
• W2405268213 creator A5084999538 @default.
• W2405268213 creator A5086110169 @default.
• W2405268213 creator A5089411715 @default.
• W2405268213 date "2016-05-26" @default.
• W2405268213 modified "2023-10-02" @default.
• W2405268213 title "Prediction of CT Substitutes from MR Images Based on Local Diffeomorphic Mapping for Brain PET Attenuation Correction" @default.
• W2405268213 cites W196093984 @default.
• W2405268213 cites W1965411365 @default.
• W2405268213 cites W1969750491 @default.
• W2405268213 cites W1970450077 @default.
• W2405268213 cites W1977556410 @default.
• W2405268213 cites W1984473052 @default.
• W2405268213 cites W1993120651 @default.
• W2405268213 cites W2010587020 @default.
• W2405268213 cites W2015897296 @default.
• W2405268213 cites W2021177063 @default.
• W2405268213 cites W2027922120 @default.
• W2405268213 cites W2035397698 @default.
• W2405268213 cites W2043626403 @default.
• W2405268213 cites W2080858163 @default.
• W2405268213 cites W2080951458 @default.
• W2405268213 cites W2087512379 @default.
• W2405268213 cites W2087844787 @default.
• W2405268213 cites W2100495482 @default.
• W2405268213 cites W2119862467 @default.
• W2405268213 cites W2136145485 @default.
• W2405268213 cites W2142082007 @default.
• W2405268213 cites W2144182447 @default.
• W2405268213 cites W2148726987 @default.
• W2405268213 cites W2154158661 @default.
• W2405268213 cites W2167157872 @default.
• W2405268213 cites W2223436381 @default.
• W2405268213 cites W4238530616 @default.
• W2405268213 cites W56917587 @default.
• W2405268213 cites W845003 @default.
• W2405268213 doi "https://doi.org/10.2967/jnumed.115.163121" @default.
• W2405268213 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/27230932" @default.
• W2405268213 hasPublicationYear "2016" @default.
• W2405268213 type Work @default.
• W2405268213 sameAs 2405268213 @default.
• W2405268213 citedByCount "15" @default.
• W2405268213 countsByYear W24052682132017 @default.
• W2405268213 countsByYear W24052682132018 @default.
• W2405268213 countsByYear W24052682132019 @default.
• W2405268213 countsByYear W24052682132020 @default.
• W2405268213 countsByYear W24052682132022 @default.
• W2405268213 crossrefType "journal-article" @default.
• W2405268213 hasAuthorship W2405268213A5006342956 @default.
• W2405268213 hasAuthorship W2405268213A5012139704 @default.
• W2405268213 hasAuthorship W2405268213A5026616191 @default.
• W2405268213 hasAuthorship W2405268213A5044127793 @default.
• W2405268213 hasAuthorship W2405268213A5070483964 @default.
• W2405268213 hasAuthorship W2405268213A5083537717 @default.
• W2405268213 hasAuthorship W2405268213A5084999538 @default.
• W2405268213 hasAuthorship W2405268213A5086110169 @default.
• W2405268213 hasAuthorship W2405268213A5089411715 @default.
• W2405268213 hasBestOaLocation W24052682131 @default.
• W2405268213 hasConcept C120665830 @default.
• W2405268213 hasConcept C121332964 @default.
• W2405268213 hasConcept C123688308 @default.
• W2405268213 hasConcept C127413603 @default.
• W2405268213 hasConcept C134306372 @default.
• W2405268213 hasConcept C153180895 @default.
• W2405268213 hasConcept C154945302 @default.
• W2405268213 hasConcept C184652730 @default.
• W2405268213 hasConcept C2989005 @default.
• W2405268213 hasConcept C33923547 @default.
• W2405268213 hasConcept C41008148 @default.
• W2405268213 hasConcept C47556283 @default.
• W2405268213 hasConcept C529865628 @default.
• W2405268213 hasConcept C54170458 @default.
• W2405268213 hasConcept C71924100 @default.
• W2405268213 hasConcept C78519656 @default.
• W2405268213 hasConceptScore W2405268213C120665830 @default.
• W2405268213 hasConceptScore W2405268213C121332964 @default.
• W2405268213 hasConceptScore W2405268213C123688308 @default.
• W2405268213 hasConceptScore W2405268213C127413603 @default.
• W2405268213 hasConceptScore W2405268213C134306372 @default.
• W2405268213 hasConceptScore W2405268213C153180895 @default.
• W2405268213 hasConceptScore W2405268213C154945302 @default.
• W2405268213 hasConceptScore W2405268213C184652730 @default.
• W2405268213 hasConceptScore W2405268213C2989005 @default.
• W2405268213 hasConceptScore W2405268213C33923547 @default.
• W2405268213 hasConceptScore W2405268213C41008148 @default.
• W2405268213 hasConceptScore W2405268213C47556283 @default.
• W2405268213 hasConceptScore W2405268213C529865628 @default.
• W2405268213 hasConceptScore W2405268213C54170458 @default.
• W2405268213 hasConceptScore W2405268213C71924100 @default.
• W2405268213 hasConceptScore W2405268213C78519656 @default.
• W2405268213 hasIssue "10" @default.

• next