Matches in SemOpenAlex for { <https://semopenalex.org/work/W2061620122> ?p ?o ?g. }
- W2061620122 endingPage "102305" @default.
- W2061620122 startingPage "102305" @default.
- W2061620122 abstract "Purpose: To develop an automated framework for accurate analysis of myocardial perfusion using first-pass magnetic resonance imaging. Methods: The proposed framework consists of four processing stages. First, in order to account for heart deformations due to respiratory motion and heart contraction, a two-step registration methodology is proposed, which has the ability to account for the global and local motions of the heart. The methodology involves an affine-based registration followed by a local B-splines alignment to maximize a new similarity function based on the first- and second-order normalized mutual information. Then the myocardium is segmented using a level-set function, its evolution being constrained by three features, namely, a weighted shape prior, a pixelwise mixed object/background image intensity distribution, and an energy of a second-order binary Markov–Gibbs random field spatial model. At the third stage, residual segmentation errors and imperfection of image alignment are reduced by employing a Laplace-based registration refinement step that provides accurate pixel-on-pixel matches on all segmented frames to generate accurate parametric perfusion maps. Finally, physiology is characterized by pixel-by-pixel mapping of empirical indexes (peak signal intensity, time-to-peak, initial upslope, and the average signal change of the slowly varying agent delivery phase), based on contrast agent dynamics. Results: The authors tested our framework on 24 perfusion data sets from 8 patients with ischemic damage who are undergoing a novel myoregeneration therapy. The performance of the processing steps of our framework is evaluated using both synthetic and in-vivo data. First, our registration methodology is evaluated using realistic synthetic phantoms and a distance-based error metric, and an improvement of registration is documented using the proposed similarity measure (P-value ≤10−4). Second, evaluation of our segmentation using the Dice similarity coefficient, documented an average of 0.910 ± 0.037 compared to two other segmentation methods that achieved average values of 0.862 ± 0.045 and 0.844 ± 0.047. Also, the receiver operating characteristic (ROC) analysis of our multifeature segmentation yielded an area under the ROC curve of 0.92, while segmentation based intensity alone showed low performance (an area of 0.69). Moreover, our framework indicated the ability, using empirical perfusion indexes, to reveal regional perfusion improvements with therapy and transmural perfusion differences across the myocardial wall. Conclusions: By quantitative and visual assessment, our framework documented the ability to characterize regional and transmural perfusion, thereby it augmenting the ability to assess follow-up treatment for patients undergoing myoregeneration therapy. This is afforded by our framework being able to handle both global and local deformations of the heart, segment accurately the myocardial wall, and provide accurate pixel-on-pixel matches of registered perfusion images." @default.
- W2061620122 created "2016-06-24" @default.
- W2061620122 creator A5003428654 @default.
- W2061620122 creator A5075057705 @default.
- W2061620122 creator A5078157026 @default.
- W2061620122 creator A5078282234 @default.
- W2061620122 date "2014-09-24" @default.
- W2061620122 modified "2023-09-24" @default.
- W2061620122 title "Fully automated framework for the analysis of myocardial first-pass perfusion MR images" @default.
- W2061620122 cites W1495971627 @default.
- W2061620122 cites W1535203294 @default.
- W2061620122 cites W1579109572 @default.
- W2061620122 cites W1965511886 @default.
- W2061620122 cites W1972829195 @default.
- W2061620122 cites W1985303086 @default.
- W2061620122 cites W1988029799 @default.
- W2061620122 cites W1988419754 @default.
- W2061620122 cites W1988462303 @default.
- W2061620122 cites W1990242906 @default.
- W2061620122 cites W1992452914 @default.
- W2061620122 cites W1993947467 @default.
- W2061620122 cites W1995561426 @default.
- W2061620122 cites W1996256871 @default.
- W2061620122 cites W1997643600 @default.
- W2061620122 cites W1997896203 @default.
- W2061620122 cites W2003863072 @default.
- W2061620122 cites W2015795623 @default.
- W2061620122 cites W2030920986 @default.
- W2061620122 cites W2031868184 @default.
- W2061620122 cites W2047815743 @default.
- W2061620122 cites W2059674017 @default.
- W2061620122 cites W2074124415 @default.
- W2061620122 cites W2075848331 @default.
- W2061620122 cites W2079139453 @default.
- W2061620122 cites W2092078486 @default.
- W2061620122 cites W2093167139 @default.
- W2061620122 cites W2099530880 @default.
- W2061620122 cites W2103404282 @default.
- W2061620122 cites W2103476848 @default.
- W2061620122 cites W2106750555 @default.
- W2061620122 cites W2107812457 @default.
- W2061620122 cites W2113524239 @default.
- W2061620122 cites W2114767146 @default.
- W2061620122 cites W2115384210 @default.
- W2061620122 cites W2116350918 @default.
- W2061620122 cites W2117057390 @default.
- W2061620122 cites W2123090765 @default.
- W2061620122 cites W2129655528 @default.
- W2061620122 cites W2129884887 @default.
- W2061620122 cites W2131466065 @default.
- W2061620122 cites W2131973318 @default.
- W2061620122 cites W2132401137 @default.
- W2061620122 cites W2132984191 @default.
- W2061620122 cites W2133782756 @default.
- W2061620122 cites W2147484997 @default.
- W2061620122 cites W2155373410 @default.
- W2061620122 cites W2158698691 @default.
- W2061620122 cites W2162378492 @default.
- W2061620122 cites W2163619454 @default.
- W2061620122 cites W2166227208 @default.
- W2061620122 cites W2166887721 @default.
- W2061620122 cites W2167369715 @default.
- W2061620122 cites W2169227941 @default.
- W2061620122 cites W2338675747 @default.
- W2061620122 doi "https://doi.org/10.1118/1.4893531" @default.
- W2061620122 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/25281975" @default.
- W2061620122 hasPublicationYear "2014" @default.
- W2061620122 type Work @default.
- W2061620122 sameAs 2061620122 @default.
- W2061620122 citedByCount "12" @default.
- W2061620122 countsByYear W20616201222014 @default.
- W2061620122 countsByYear W20616201222015 @default.
- W2061620122 countsByYear W20616201222016 @default.
- W2061620122 countsByYear W20616201222017 @default.
- W2061620122 countsByYear W20616201222018 @default.
- W2061620122 countsByYear W20616201222020 @default.
- W2061620122 countsByYear W20616201222021 @default.
- W2061620122 crossrefType "journal-article" @default.
- W2061620122 hasAuthorship W2061620122A5003428654 @default.
- W2061620122 hasAuthorship W2061620122A5075057705 @default.
- W2061620122 hasAuthorship W2061620122A5078157026 @default.
- W2061620122 hasAuthorship W2061620122A5078282234 @default.
- W2061620122 hasConcept C105795698 @default.
- W2061620122 hasConcept C11413529 @default.
- W2061620122 hasConcept C115961682 @default.
- W2061620122 hasConcept C117251300 @default.
- W2061620122 hasConcept C124504099 @default.
- W2061620122 hasConcept C152139883 @default.
- W2061620122 hasConcept C153180895 @default.
- W2061620122 hasConcept C154945302 @default.
- W2061620122 hasConcept C160633673 @default.
- W2061620122 hasConcept C166704113 @default.
- W2061620122 hasConcept C202444582 @default.
- W2061620122 hasConcept C2778045648 @default.
- W2061620122 hasConcept C31972630 @default.
- W2061620122 hasConcept C33923547 @default.
- W2061620122 hasConcept C41008148 @default.
- W2061620122 hasConcept C89600930 @default.