Matches in SemOpenAlex for { <https://semopenalex.org/work/W3188116148> ?p ?o ?g. }
- W3188116148 endingPage "47" @default.
- W3188116148 startingPage "37" @default.
- W3188116148 abstract "Compressed sensing is commonly concerned with optimizing the image quality after a partial undersampling of the measurable k-space to accelerate MRI. In this article, we propose to change the focus from the quality of the reconstructed image to the quality of the downstream image analysis outcome. Specifically, we propose to optimize the patterns according to how well a sought-after pathology could be detected or localized in the reconstructed images. We find the optimal undersampling patterns in k-space that maximize target value functions of interest in commonplace medical vision problems (reconstruction, segmentation, and classification) and propose a new iterative gradient sampling routine universally suitable for these tasks. We validate the proposed MRI acceleration paradigm on three classical medical datasets, demonstrating a noticeable improvement of the target metrics at the high acceleration factors (for the segmentation problem at ×16 acceleration, we report up to 12% improvement in Dice score over the other undersampling patterns)." @default.
- W3188116148 created "2021-08-16" @default.
- W3188116148 creator A5031517308 @default.
- W3188116148 creator A5048793164 @default.
- W3188116148 creator A5070529920 @default.
- W3188116148 date "2023-11-01" @default.
- W3188116148 modified "2023-10-16" @default.
- W3188116148 title "Optimal MRI undersampling patterns for ultimate benefit of medical vision tasks" @default.
- W3188116148 cites W1641498739 @default.
- W3188116148 cites W1754483689 @default.
- W3188116148 cites W2014547837 @default.
- W3188116148 cites W2042965174 @default.
- W3188116148 cites W2101675075 @default.
- W3188116148 cites W2104434712 @default.
- W3188116148 cites W2111388536 @default.
- W3188116148 cites W2145096794 @default.
- W3188116148 cites W2151380359 @default.
- W3188116148 cites W2300951415 @default.
- W3188116148 cites W2315470063 @default.
- W3188116148 cites W2416236246 @default.
- W3188116148 cites W2462438481 @default.
- W3188116148 cites W2562637781 @default.
- W3188116148 cites W2611467245 @default.
- W3188116148 cites W2751069891 @default.
- W3188116148 cites W2765982206 @default.
- W3188116148 cites W2795380527 @default.
- W3188116148 cites W2804047627 @default.
- W3188116148 cites W2907696891 @default.
- W3188116148 cites W2910002198 @default.
- W3188116148 cites W2944761405 @default.
- W3188116148 cites W2963682501 @default.
- W3188116148 cites W2999511788 @default.
- W3188116148 cites W3015259956 @default.
- W3188116148 cites W3018639952 @default.
- W3188116148 cites W3046934520 @default.
- W3188116148 cites W3078928940 @default.
- W3188116148 cites W3080851077 @default.
- W3188116148 cites W3080893417 @default.
- W3188116148 cites W3097194025 @default.
- W3188116148 cites W3100730608 @default.
- W3188116148 cites W3124596402 @default.
- W3188116148 cites W4233764193 @default.
- W3188116148 cites W4249760698 @default.
- W3188116148 cites W4319459215 @default.
- W3188116148 doi "https://doi.org/10.1016/j.mri.2023.06.020" @default.
- W3188116148 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37423471" @default.
- W3188116148 hasPublicationYear "2023" @default.
- W3188116148 type Work @default.
- W3188116148 sameAs 3188116148 @default.
- W3188116148 citedByCount "0" @default.
- W3188116148 crossrefType "journal-article" @default.
- W3188116148 hasAuthorship W3188116148A5031517308 @default.
- W3188116148 hasAuthorship W3188116148A5048793164 @default.
- W3188116148 hasAuthorship W3188116148A5070529920 @default.
- W3188116148 hasBestOaLocation W31881161482 @default.
- W3188116148 hasConcept C106131492 @default.
- W3188116148 hasConcept C115961682 @default.
- W3188116148 hasConcept C117896860 @default.
- W3188116148 hasConcept C120665830 @default.
- W3188116148 hasConcept C121332964 @default.
- W3188116148 hasConcept C124504099 @default.
- W3188116148 hasConcept C124851039 @default.
- W3188116148 hasConcept C136536468 @default.
- W3188116148 hasConcept C140779682 @default.
- W3188116148 hasConcept C153180895 @default.
- W3188116148 hasConcept C154945302 @default.
- W3188116148 hasConcept C192209626 @default.
- W3188116148 hasConcept C31601959 @default.
- W3188116148 hasConcept C31972630 @default.
- W3188116148 hasConcept C41008148 @default.
- W3188116148 hasConcept C55020928 @default.
- W3188116148 hasConcept C74650414 @default.
- W3188116148 hasConcept C89600930 @default.
- W3188116148 hasConceptScore W3188116148C106131492 @default.
- W3188116148 hasConceptScore W3188116148C115961682 @default.
- W3188116148 hasConceptScore W3188116148C117896860 @default.
- W3188116148 hasConceptScore W3188116148C120665830 @default.
- W3188116148 hasConceptScore W3188116148C121332964 @default.
- W3188116148 hasConceptScore W3188116148C124504099 @default.
- W3188116148 hasConceptScore W3188116148C124851039 @default.
- W3188116148 hasConceptScore W3188116148C136536468 @default.
- W3188116148 hasConceptScore W3188116148C140779682 @default.
- W3188116148 hasConceptScore W3188116148C153180895 @default.
- W3188116148 hasConceptScore W3188116148C154945302 @default.
- W3188116148 hasConceptScore W3188116148C192209626 @default.
- W3188116148 hasConceptScore W3188116148C31601959 @default.
- W3188116148 hasConceptScore W3188116148C31972630 @default.
- W3188116148 hasConceptScore W3188116148C41008148 @default.
- W3188116148 hasConceptScore W3188116148C55020928 @default.
- W3188116148 hasConceptScore W3188116148C74650414 @default.
- W3188116148 hasConceptScore W3188116148C89600930 @default.
- W3188116148 hasLocation W31881161481 @default.
- W3188116148 hasLocation W31881161482 @default.
- W3188116148 hasLocation W31881161483 @default.
- W3188116148 hasOpenAccess W3188116148 @default.
- W3188116148 hasPrimaryLocation W31881161481 @default.
- W3188116148 hasRelatedWork W1669643531 @default.
- W3188116148 hasRelatedWork W1979954835 @default.