Matches in SemOpenAlex for { <https://semopenalex.org/work/W2296001337> ?p ?o ?g. }
- W2296001337 endingPage "235" @default.
- W2296001337 startingPage "228" @default.
- W2296001337 abstract "The aims of this study were to optimize parameters for Nesterov algorithm (NESTA) in reconstruction of 3-dimensional time-of-flight (TOF) magnetic resonance angiography (MRA) at 3 T by performing an exhaustive search and to validate the performance of compressed sensing (CS) by applying it to data from cerebral aneurysms and evaluating diagnostic quality.Three-dimensional TOF-MRA was obtained using a 3 T MR system with a 32-channel head coil for both healthy volunteers and 10 patients (11 aneurysms). No undersampling was applied for imaging parameters, including parallel imaging or other partial Fourier sampling. In the first step, the experimental setup was for healthy subjects to optimize CS parameters of NESTA and the undersampling mask pattern, so 24,696 different reconstruction conditions were surveyed for sampling rates of 8.0X and 5.0X. Mean square error (MSE) was calculated for each image reconstructed with the undersampling pattern and CS parameter sets. Evaluation was by normalized MSE, edge sharpness for MRA reconstructed using fully sampled data (MRA-full), zero-filled MRA (ZF-MRA) with Poisson disk undersampling mask, and CS-MRA (5.0X and 8.0X) with iterations of 5, 10, 15, 20, 25, 30, 35, 40, 45, and 50. CS-MRA (5.0X and 8.0X) with 5, 10, and 50 iterations of the sampling pattern and CS parameter set with the lowest MSE were visually inspected by 2 neuroradiologists to check the diagnostic quality.The sampling pattern and CS parameter set with the lowest MSE were identical for both CS-MRA 5.0X and CS-MRA 8.0X. At the initial 5 to 15 iterations, MSE of both sampling rates greatly decreased from that of ZF-MRA. For subsequent iterations, the decrease in MSE was relatively small. For CS-MRA, sharpness greatly increased from that of ZF-MRA within the initial 5 to 15 iterations, followed by slight increases with further iterations. Two neuroradiologists graded most aneurysms as excellent, with the exception of 1 to 4 aneurysms recognized as good by 1 observer in CS-MRA (8.0X).Optimization of NESTA in the reconstruction of 3-dimensional TOF-MRA was conducted, and the parameters and undersampling mask with the lowest MSE were determined. Caliber measurement should be performed with CS (5.0X) with 25 or 30 iterations. Most cerebral aneurysms were sufficiently recognized using CS-MRA (5.0X) or CS-MRA (8.0X) with 10 iterations." @default.
- W2296001337 created "2016-06-24" @default.
- W2296001337 creator A5014391514 @default.
- W2296001337 creator A5039815602 @default.
- W2296001337 creator A5057969681 @default.
- W2296001337 creator A5060264972 @default.
- W2296001337 creator A5060847080 @default.
- W2296001337 creator A5065646924 @default.
- W2296001337 creator A5066655486 @default.
- W2296001337 creator A5079975386 @default.
- W2296001337 date "2016-04-01" @default.
- W2296001337 modified "2023-10-16" @default.
- W2296001337 title "Compressed Sensing 3-Dimensional Time-of-Flight Magnetic Resonance Angiography for Cerebral Aneurysms" @default.
- W2296001337 cites W1988821523 @default.
- W2296001337 cites W2007515996 @default.
- W2296001337 cites W2057536058 @default.
- W2296001337 cites W2084119090 @default.
- W2296001337 cites W2084673540 @default.
- W2296001337 cites W2100556411 @default.
- W2296001337 cites W2101675075 @default.
- W2296001337 cites W2116437043 @default.
- W2296001337 cites W2128744915 @default.
- W2296001337 cites W2130913244 @default.
- W2296001337 cites W2132122471 @default.
- W2296001337 cites W2133154528 @default.
- W2296001337 cites W2142615775 @default.
- W2296001337 cites W2151354228 @default.
- W2296001337 cites W2158147889 @default.
- W2296001337 cites W2163973643 @default.
- W2296001337 cites W2165614718 @default.
- W2296001337 cites W2165621956 @default.
- W2296001337 cites W2167732364 @default.
- W2296001337 cites W2185602086 @default.
- W2296001337 cites W2333185816 @default.
- W2296001337 cites W3124114587 @default.
- W2296001337 doi "https://doi.org/10.1097/rli.0000000000000226" @default.
- W2296001337 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26606551" @default.
- W2296001337 hasPublicationYear "2016" @default.
- W2296001337 type Work @default.
- W2296001337 sameAs 2296001337 @default.
- W2296001337 citedByCount "42" @default.
- W2296001337 countsByYear W22960013372016 @default.
- W2296001337 countsByYear W22960013372017 @default.
- W2296001337 countsByYear W22960013372018 @default.
- W2296001337 countsByYear W22960013372019 @default.
- W2296001337 countsByYear W22960013372020 @default.
- W2296001337 countsByYear W22960013372021 @default.
- W2296001337 countsByYear W22960013372022 @default.
- W2296001337 countsByYear W22960013372023 @default.
- W2296001337 crossrefType "journal-article" @default.
- W2296001337 hasAuthorship W2296001337A5014391514 @default.
- W2296001337 hasAuthorship W2296001337A5039815602 @default.
- W2296001337 hasAuthorship W2296001337A5057969681 @default.
- W2296001337 hasAuthorship W2296001337A5060264972 @default.
- W2296001337 hasAuthorship W2296001337A5060847080 @default.
- W2296001337 hasAuthorship W2296001337A5065646924 @default.
- W2296001337 hasAuthorship W2296001337A5066655486 @default.
- W2296001337 hasAuthorship W2296001337A5079975386 @default.
- W2296001337 hasConcept C105795698 @default.
- W2296001337 hasConcept C106131492 @default.
- W2296001337 hasConcept C11413529 @default.
- W2296001337 hasConcept C121332964 @default.
- W2296001337 hasConcept C124851039 @default.
- W2296001337 hasConcept C126838900 @default.
- W2296001337 hasConcept C136536468 @default.
- W2296001337 hasConcept C139945424 @default.
- W2296001337 hasConcept C140779682 @default.
- W2296001337 hasConcept C143409427 @default.
- W2296001337 hasConcept C154945302 @default.
- W2296001337 hasConcept C2778212899 @default.
- W2296001337 hasConcept C2989005 @default.
- W2296001337 hasConcept C31972630 @default.
- W2296001337 hasConcept C33923547 @default.
- W2296001337 hasConcept C41008148 @default.
- W2296001337 hasConcept C62520636 @default.
- W2296001337 hasConcept C71907059 @default.
- W2296001337 hasConcept C71924100 @default.
- W2296001337 hasConceptScore W2296001337C105795698 @default.
- W2296001337 hasConceptScore W2296001337C106131492 @default.
- W2296001337 hasConceptScore W2296001337C11413529 @default.
- W2296001337 hasConceptScore W2296001337C121332964 @default.
- W2296001337 hasConceptScore W2296001337C124851039 @default.
- W2296001337 hasConceptScore W2296001337C126838900 @default.
- W2296001337 hasConceptScore W2296001337C136536468 @default.
- W2296001337 hasConceptScore W2296001337C139945424 @default.
- W2296001337 hasConceptScore W2296001337C140779682 @default.
- W2296001337 hasConceptScore W2296001337C143409427 @default.
- W2296001337 hasConceptScore W2296001337C154945302 @default.
- W2296001337 hasConceptScore W2296001337C2778212899 @default.
- W2296001337 hasConceptScore W2296001337C2989005 @default.
- W2296001337 hasConceptScore W2296001337C31972630 @default.
- W2296001337 hasConceptScore W2296001337C33923547 @default.
- W2296001337 hasConceptScore W2296001337C41008148 @default.
- W2296001337 hasConceptScore W2296001337C62520636 @default.
- W2296001337 hasConceptScore W2296001337C71907059 @default.
- W2296001337 hasConceptScore W2296001337C71924100 @default.
- W2296001337 hasIssue "4" @default.
- W2296001337 hasLocation W22960013371 @default.