FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2149491421> ?p ?o ?g. }

• W2149491421 endingPage "1502" @default.
• W2149491421 startingPage "1491" @default.
• W2149491421 abstract "A new gold standard data set for validation of 2D/3D registration based on a porcine cadaver head with attached fiducial markers was presented in the first part of this article. The advantage of this new phantom is the large amount of soft tissue, which simulates realistic conditions for registration. This article tests the performance of intensity- and gradient-based algorithms for 2D/3D registration using the new phantom data set.Intensity-based methods with four merit functions, namely, cross correlation, rank correlation, correlation ratio, and mutual information (MI), and two gradient-based algorithms, the backprojection gradient-based (BGB) registration method and the reconstruction gradient-based (RGB) registration method, were compared. Four volumes consisting of CBCT with two fields of view, 64 slice multidetector CT, and magnetic resonance-T1 weighted images were registered to a pair of kV x-ray images and a pair of MV images. A standardized evaluation methodology was employed. Targets were evenly spread over the volumes and 250 starting positions of the 3D volumes with initial displacements of up to 25 mm from the gold standard position were calculated. After the registration, the displacement from the gold standard was retrieved and the root mean square (RMS), mean, and standard deviation mean target registration errors (mTREs) over 250 registrations were derived. Additionally, the following merit properties were computed: Accuracy, capture range, number of minima, risk of nonconvergence, and distinctiveness of optimum for better comparison of the robustness of each merit.Among the merit functions used for the intensity-based method, MI reached the best accuracy with an RMS mTRE down to 1.30 mm. Furthermore, it was the only merit function that could accurately register the CT to the kV x rays with the presence of tissue deformation. As for the gradient-based methods, BGB and RGB methods achieved subvoxel accuracy (RMS mTRE down to 0.56 and 0.70 mm, respectively). Overall, gradient-based similarity measures were found to be substantially more accurate than intensity-based methods and could cope with soft tissue deformation and enabled also accurate registrations of the MR-T1 volume to the kV x-ray image.In this article, the authors demonstrate the usefulness of a new phantom image data set for the evaluation of 2D/3D registration methods, which featured soft tissue deformation. The author's evaluation shows that gradient-based methods are more accurate than intensity-based methods, especially when soft tissue deformation is present. However, the current nonoptimized implementations make them prohibitively slow for practical applications. On the other hand, the speed of the intensity-based method renders these more suitable for clinical use, while the accuracy is still competitive." @default.
• W2149491421 created "2016-06-24" @default.
• W2149491421 creator A5004056985 @default.
• W2149491421 creator A5007220929 @default.
• W2149491421 creator A5009163035 @default.
• W2149491421 creator A5017379508 @default.
• W2149491421 creator A5024434129 @default.
• W2149491421 creator A5025688738 @default.
• W2149491421 creator A5033613563 @default.
• W2149491421 creator A5047925902 @default.
• W2149491421 creator A5058111722 @default.
• W2149491421 creator A5068338800 @default.
• W2149491421 creator A5078964143 @default.
• W2149491421 date "2011-02-23" @default.
• W2149491421 modified "2023-10-09" @default.
• W2149491421 title "Validation for 2D/3D registration II: The comparison of intensity- and gradient-based merit functions using a new gold standard data set" @default.
• W2149491421 cites W1974369017 @default.
• W2149491421 cites W1982007452 @default.
• W2149491421 cites W1996672948 @default.
• W2149491421 cites W2000293349 @default.
• W2149491421 cites W2002874321 @default.
• W2149491421 cites W2012585457 @default.
• W2149491421 cites W2023995307 @default.
• W2149491421 cites W2028580836 @default.
• W2149491421 cites W2031247349 @default.
• W2149491421 cites W2034432063 @default.
• W2149491421 cites W2035539344 @default.
• W2149491421 cites W2050304790 @default.
• W2149491421 cites W2058004921 @default.
• W2149491421 cites W2062883940 @default.
• W2149491421 cites W2066993558 @default.
• W2149491421 cites W2067215519 @default.
• W2149491421 cites W2077593678 @default.
• W2149491421 cites W2085036530 @default.
• W2149491421 cites W2097435585 @default.
• W2149491421 cites W2097840056 @default.
• W2149491421 cites W2099752932 @default.
• W2149491421 cites W2104066816 @default.
• W2149491421 cites W2107050683 @default.
• W2149491421 cites W2122313934 @default.
• W2149491421 cites W2126440019 @default.
• W2149491421 cites W2131035649 @default.
• W2149491421 cites W2134042740 @default.
• W2149491421 cites W2136244984 @default.
• W2149491421 cites W2143272247 @default.
• W2149491421 cites W2147934296 @default.
• W2149491421 cites W2149289693 @default.
• W2149491421 cites W2158596786 @default.
• W2149491421 cites W2166484594 @default.
• W2149491421 cites W2214079758 @default.
• W2149491421 cites W4206495173 @default.
• W2149491421 cites W50240017 @default.
• W2149491421 doi "https://doi.org/10.1118/1.3553403" @default.
• W2149491421 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3089767" @default.
• W2149491421 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/21520861" @default.
• W2149491421 hasPublicationYear "2011" @default.
• W2149491421 type Work @default.
• W2149491421 sameAs 2149491421 @default.
• W2149491421 citedByCount "34" @default.
• W2149491421 countsByYear W21494914212012 @default.
• W2149491421 countsByYear W21494914212013 @default.
• W2149491421 countsByYear W21494914212014 @default.
• W2149491421 countsByYear W21494914212015 @default.
• W2149491421 countsByYear W21494914212016 @default.
• W2149491421 countsByYear W21494914212017 @default.
• W2149491421 countsByYear W21494914212018 @default.
• W2149491421 countsByYear W21494914212020 @default.
• W2149491421 countsByYear W21494914212021 @default.
• W2149491421 countsByYear W21494914212022 @default.
• W2149491421 crossrefType "journal-article" @default.
• W2149491421 hasAuthorship W2149491421A5004056985 @default.
• W2149491421 hasAuthorship W2149491421A5007220929 @default.
• W2149491421 hasAuthorship W2149491421A5009163035 @default.
• W2149491421 hasAuthorship W2149491421A5017379508 @default.
• W2149491421 hasAuthorship W2149491421A5024434129 @default.
• W2149491421 hasAuthorship W2149491421A5025688738 @default.
• W2149491421 hasAuthorship W2149491421A5033613563 @default.
• W2149491421 hasAuthorship W2149491421A5047925902 @default.
• W2149491421 hasAuthorship W2149491421A5058111722 @default.
• W2149491421 hasAuthorship W2149491421A5068338800 @default.
• W2149491421 hasAuthorship W2149491421A5078964143 @default.
• W2149491421 hasBestOaLocation W21494914212 @default.
• W2149491421 hasConcept C104293457 @default.
• W2149491421 hasConcept C104317684 @default.
• W2149491421 hasConcept C105795698 @default.
• W2149491421 hasConcept C115961682 @default.
• W2149491421 hasConcept C130277099 @default.
• W2149491421 hasConcept C152139883 @default.
• W2149491421 hasConcept C154945302 @default.
• W2149491421 hasConcept C166704113 @default.
• W2149491421 hasConcept C173974348 @default.
• W2149491421 hasConcept C185592680 @default.
• W2149491421 hasConcept C22679943 @default.
• W2149491421 hasConcept C2989005 @default.
• W2149491421 hasConcept C31601959 @default.
• W2149491421 hasConcept C31972630 @default.
• W2149491421 hasConcept C33923547 @default.
• W2149491421 hasConcept C40993552 @default.

• next