Matches in SemOpenAlex for { <https://semopenalex.org/work/W2954687235> ?p ?o ?g. }
- W2954687235 endingPage "941" @default.
- W2954687235 startingPage "928" @default.
- W2954687235 abstract "Cortical bone porosity is a major determinant of bone strength. Despite the biomechanical importance of cortical bone porosity, the biological drivers of cortical porosity are unknown. The content of cortical pore space can indicate pore expansion mechanisms; both of the primary components of pore space, vessels and adipocytes, have been implicated in pore expansion. Dynamic contrast-enhanced MRI (DCE-MRI) is widely used in vessel detection in cardiovascular studies, but has not been applied to visualize vessels within cortical bone. In this study, we have developed a multimodal DCE-MRI and high resolution peripheral QCT (HR-pQCT) acquisition and image processing pipeline to detect vessel-filled cortical bone pores.For this in vivo human study, 19 volunteers (10 males and 9 females; mean age =63±5) were recruited. Both distal and ultra-distal regions of the non-dominant tibia were imaged by HR-pQCT (82 µm nominal resolution) for bone structure segmentation and by 3T DCE-MRI (Gadavist; 9 min scan time; temporal resolution =30 sec; voxel size 230×230×500 µm3) for vessel visualization. The DCE-MRI was registered to the HR-pQCT volume and the voxels within the MRI cortical bone region were extracted. Features of the DCE data were calculated and voxels were categorized by a 2-stage hierarchical kmeans clustering algorithm to determine which voxels represent vessels. Vessel volume fraction (volume ratio of vessels to cortical bone), vessel density (average vessel count per cortical bone volume), and average vessel volume (mean volume of vessels) were calculated to quantify the status of vessel-filled pores in cortical bone. To examine spatial resolution and perform validation, a virtual phantom with 5 channel sizes and an applied pseudo enhancement curve was processed through the proposed image processing pipeline. Overlap volume ratio and Dice coefficient was calculated to measure the similarity between the detected vessel map and ground truth.In the human study, mean vessel volume fraction was 2.2%±1.0%, mean vessel density was 0.68±0.27 vessel/mm3, and mean average vessel volume was 0.032±0.012 mm3/vessel. Signal intensity for detected vessel voxels increased during the scan, while signal for non-vessel voxels within pores did not enhance. In the validation phantom, channels with diameter 250 µm or greater were detected successfully, with volume ratio equal to 1 and Dice coefficient above 0.6. Both statistics decreased dramatically for channel sizes less than 250 µm.We have a developed a multi-modal image acquisition and processing pipeline that successfully detects vessels within cortical bone pores. The performance of this technique degrades for vessel diameters below the in-plane spatial resolution of the DCE-MRI acquisition. This approach can be applied to investigate the biological systems associated with cortical pore expansion." @default.
- W2954687235 created "2019-07-12" @default.
- W2954687235 creator A5003460542 @default.
- W2954687235 creator A5009406749 @default.
- W2954687235 creator A5035282947 @default.
- W2954687235 creator A5046201113 @default.
- W2954687235 creator A5049846775 @default.
- W2954687235 creator A5064765354 @default.
- W2954687235 creator A5085840133 @default.
- W2954687235 creator A5090133386 @default.
- W2954687235 creator A5090984935 @default.
- W2954687235 date "2019-06-01" @default.
- W2954687235 modified "2023-09-25" @default.
- W2954687235 title "Cortical bone vessel identification and quantification on contrast-enhanced MR images" @default.
- W2954687235 cites W1586031743 @default.
- W2954687235 cites W1605096437 @default.
- W2954687235 cites W1912212332 @default.
- W2954687235 cites W1963129880 @default.
- W2954687235 cites W1966244775 @default.
- W2954687235 cites W1966877738 @default.
- W2954687235 cites W1971915656 @default.
- W2954687235 cites W1972405727 @default.
- W2954687235 cites W1976863536 @default.
- W2954687235 cites W1981453135 @default.
- W2954687235 cites W1987696883 @default.
- W2954687235 cites W2003174799 @default.
- W2954687235 cites W2007902668 @default.
- W2954687235 cites W2014973716 @default.
- W2954687235 cites W2015079189 @default.
- W2954687235 cites W2015480546 @default.
- W2954687235 cites W2016237276 @default.
- W2954687235 cites W2024496124 @default.
- W2954687235 cites W2027469592 @default.
- W2954687235 cites W2040663403 @default.
- W2954687235 cites W2041287840 @default.
- W2954687235 cites W2057868965 @default.
- W2954687235 cites W2066428247 @default.
- W2954687235 cites W2078156116 @default.
- W2954687235 cites W2079053405 @default.
- W2954687235 cites W2080179323 @default.
- W2954687235 cites W2081735229 @default.
- W2954687235 cites W2086460111 @default.
- W2954687235 cites W2095272243 @default.
- W2954687235 cites W2103857226 @default.
- W2954687235 cites W2109392661 @default.
- W2954687235 cites W2119671639 @default.
- W2954687235 cites W2122305597 @default.
- W2954687235 cites W2130796365 @default.
- W2954687235 cites W2135758510 @default.
- W2954687235 cites W2139056805 @default.
- W2954687235 cites W2140727296 @default.
- W2954687235 cites W2158167845 @default.
- W2954687235 cites W2167751454 @default.
- W2954687235 cites W2170783994 @default.
- W2954687235 cites W2180783301 @default.
- W2954687235 cites W2346707941 @default.
- W2954687235 cites W2435961780 @default.
- W2954687235 cites W2474969214 @default.
- W2954687235 cites W2529535353 @default.
- W2954687235 cites W2554646502 @default.
- W2954687235 cites W2570076739 @default.
- W2954687235 cites W2582487533 @default.
- W2954687235 cites W2595498215 @default.
- W2954687235 cites W2736410558 @default.
- W2954687235 cites W2753979477 @default.
- W2954687235 cites W2780617250 @default.
- W2954687235 cites W2804323424 @default.
- W2954687235 cites W2980416293 @default.
- W2954687235 doi "https://doi.org/10.21037/qims.2019.05.23" @default.
- W2954687235 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6629562" @default.
- W2954687235 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31367547" @default.
- W2954687235 hasPublicationYear "2019" @default.
- W2954687235 type Work @default.
- W2954687235 sameAs 2954687235 @default.
- W2954687235 citedByCount "8" @default.
- W2954687235 countsByYear W29546872352017 @default.
- W2954687235 countsByYear W29546872352020 @default.
- W2954687235 countsByYear W29546872352021 @default.
- W2954687235 countsByYear W29546872352022 @default.
- W2954687235 countsByYear W29546872352023 @default.
- W2954687235 crossrefType "journal-article" @default.
- W2954687235 hasAuthorship W2954687235A5003460542 @default.
- W2954687235 hasAuthorship W2954687235A5009406749 @default.
- W2954687235 hasAuthorship W2954687235A5035282947 @default.
- W2954687235 hasAuthorship W2954687235A5046201113 @default.
- W2954687235 hasAuthorship W2954687235A5049846775 @default.
- W2954687235 hasAuthorship W2954687235A5064765354 @default.
- W2954687235 hasAuthorship W2954687235A5085840133 @default.
- W2954687235 hasAuthorship W2954687235A5090133386 @default.
- W2954687235 hasAuthorship W2954687235A5090984935 @default.
- W2954687235 hasBestOaLocation W29546872351 @default.
- W2954687235 hasConcept C105702510 @default.
- W2954687235 hasConcept C121332964 @default.
- W2954687235 hasConcept C126838900 @default.
- W2954687235 hasConcept C127313418 @default.
- W2954687235 hasConcept C136229726 @default.
- W2954687235 hasConcept C192562407 @default.
- W2954687235 hasConcept C20556612 @default.