FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3136263382> ?p ?o ?g. }

• W3136263382 endingPage "105321" @default.
• W3136263382 startingPage "105321" @default.
• W3136263382 abstract "MRI is an inherently slow process, which leads to long scan time for high-resolution imaging. The speed of acquisition can be increased by ignoring parts of the data (undersampling). Consequently, this leads to the degradation of image quality, such as loss of resolution or introduction of image artefacts. This work aims to reconstruct highly undersampled Cartesian or radial MR acquisitions, with better resolution and with less to no artefact compared to conventional techniques like compressed sensing. In recent times, deep learning has emerged as a very important area of research and has shown immense potential in solving inverse problems, e.g. MR image reconstruction. In this paper, a deep learning based MR image reconstruction framework is proposed, which includes a modified regularised version of ResNet as the network backbone to remove artefacts from the undersampled image, followed by data consistency steps that fusions the network output with the data already available from undersampled k-space in order to further improve reconstruction quality. The performance of this framework for various undersampling patterns has also been tested, and it has been observed that the framework is robust to deal with various sampling patterns, even when mixed together while training, and results in very high quality reconstruction, in terms of high SSIM (highest being 0.990 ± 0.006 for acceleration factor of 3.5), while being compared with the fully sampled reconstruction. It has been shown that the proposed framework can successfully reconstruct even for an acceleration factor of 20 for Cartesian (0.968 ± 0.005) and 17 for radially (0.962 ± 0.012) sampled data. Furthermore, it has been shown that the framework preserves brain pathology during reconstruction while being trained on healthy subjects." @default.
• W3136263382 created "2021-03-29" @default.
• W3136263382 creator A5000099727 @default.
• W3136263382 creator A5007759025 @default.
• W3136263382 creator A5022172450 @default.
• W3136263382 creator A5055725293 @default.
• W3136263382 creator A5067703063 @default.
• W3136263382 creator A5075663234 @default.
• W3136263382 creator A5078134089 @default.
• W3136263382 date "2022-04-01" @default.
• W3136263382 modified "2023-10-05" @default.
• W3136263382 title "ReconResNet: Regularised residual learning for MR image reconstruction of Undersampled Cartesian and Radial data" @default.
• W3136263382 cites W1482297123 @default.
• W3136263382 cites W1641498739 @default.
• W3136263382 cites W1974670830 @default.
• W3136263382 cites W1984322424 @default.
• W3136263382 cites W2006096283 @default.
• W3136263382 cites W2070949043 @default.
• W3136263382 cites W2101675075 @default.
• W3136263382 cites W2103373759 @default.
• W3136263382 cites W2107906890 @default.
• W3136263382 cites W2111388536 @default.
• W3136263382 cites W2113030459 @default.
• W3136263382 cites W2116523778 @default.
• W3136263382 cites W2134051605 @default.
• W3136263382 cites W2134120396 @default.
• W3136263382 cites W2145020729 @default.
• W3136263382 cites W2150534249 @default.
• W3136263382 cites W2163133289 @default.
• W3136263382 cites W2508457857 @default.
• W3136263382 cites W2590877996 @default.
• W3136263382 cites W2604388535 @default.
• W3136263382 cites W2621235041 @default.
• W3136263382 cites W2737022104 @default.
• W3136263382 cites W2757208835 @default.
• W3136263382 cites W2757509933 @default.
• W3136263382 cites W2792765611 @default.
• W3136263382 cites W2889995282 @default.
• W3136263382 cites W2914959431 @default.
• W3136263382 cites W2941103766 @default.
• W3136263382 cites W2962734274 @default.
• W3136263382 cites W2977883299 @default.
• W3136263382 cites W3100220310 @default.
• W3136263382 cites W3105403262 @default.
• W3136263382 cites W3108906604 @default.
• W3136263382 cites W4249760698 @default.
• W3136263382 cites W4250955649 @default.
• W3136263382 cites W4252713891 @default.
• W3136263382 doi "https://doi.org/10.1016/j.compbiomed.2022.105321" @default.
• W3136263382 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35219188" @default.
• W3136263382 hasPublicationYear "2022" @default.
• W3136263382 type Work @default.
• W3136263382 sameAs 3136263382 @default.
• W3136263382 citedByCount "8" @default.
• W3136263382 countsByYear W31362633822022 @default.
• W3136263382 countsByYear W31362633822023 @default.
• W3136263382 crossrefType "journal-article" @default.
• W3136263382 hasAuthorship W3136263382A5000099727 @default.
• W3136263382 hasAuthorship W3136263382A5007759025 @default.
• W3136263382 hasAuthorship W3136263382A5022172450 @default.
• W3136263382 hasAuthorship W3136263382A5055725293 @default.
• W3136263382 hasAuthorship W3136263382A5067703063 @default.
• W3136263382 hasAuthorship W3136263382A5075663234 @default.
• W3136263382 hasAuthorship W3136263382A5078134089 @default.
• W3136263382 hasBestOaLocation W31362633822 @default.
• W3136263382 hasConcept C106131492 @default.
• W3136263382 hasConcept C108583219 @default.
• W3136263382 hasConcept C111919701 @default.
• W3136263382 hasConcept C11413529 @default.
• W3136263382 hasConcept C115961682 @default.
• W3136263382 hasConcept C124851039 @default.
• W3136263382 hasConcept C136536468 @default.
• W3136263382 hasConcept C140779682 @default.
• W3136263382 hasConcept C141379421 @default.
• W3136263382 hasConcept C154945302 @default.
• W3136263382 hasConcept C155512373 @default.
• W3136263382 hasConcept C16038011 @default.
• W3136263382 hasConcept C205372480 @default.
• W3136263382 hasConcept C2524010 @default.
• W3136263382 hasConcept C2779898584 @default.
• W3136263382 hasConcept C31972630 @default.
• W3136263382 hasConcept C33923547 @default.
• W3136263382 hasConcept C41008148 @default.
• W3136263382 hasConcept C55020928 @default.
• W3136263382 hasConcept C93361087 @default.
• W3136263382 hasConcept C98045186 @default.
• W3136263382 hasConceptScore W3136263382C106131492 @default.
• W3136263382 hasConceptScore W3136263382C108583219 @default.
• W3136263382 hasConceptScore W3136263382C111919701 @default.
• W3136263382 hasConceptScore W3136263382C11413529 @default.
• W3136263382 hasConceptScore W3136263382C115961682 @default.
• W3136263382 hasConceptScore W3136263382C124851039 @default.
• W3136263382 hasConceptScore W3136263382C136536468 @default.
• W3136263382 hasConceptScore W3136263382C140779682 @default.
• W3136263382 hasConceptScore W3136263382C141379421 @default.
• W3136263382 hasConceptScore W3136263382C154945302 @default.
• W3136263382 hasConceptScore W3136263382C155512373 @default.
• W3136263382 hasConceptScore W3136263382C16038011 @default.

• next