Dataset Information

Scan-rescan reproducibility of segmental aortic wall shear stress as assessed by phase-specific segmentation with 4D flow MRI in healthy volunteers.

ABSTRACT: OBJECTIVE:The aim was to investigate scan-rescan reproducibility and observer variability of segmental aortic 3D systolic wall shear stress (WSS) by phase-specific segmentation with 4D flow MRI in healthy volunteers. MATERIALS AND METHODS:Ten healthy volunteers (age 26.5?±?2.6 years) underwent aortic 4D flow MRI twice. Maximum 3D systolic WSS (WSSmax) and mean 3D systolic WSS (WSSmean) for five thoracic aortic segments over five systolic cardiac phases by phase-specific segmentations were calculated. Scan-rescan analysis and observer reproducibility analysis were performed. RESULTS:Scan-rescan data showed overall good reproducibility for WSSmean (coefficient of variation, COV 10-15%) with moderate-to-strong intraclass correlation coefficient (ICC 0.63-0.89). The variability in WSSmax was high (COV 16-31%) with moderate-to-good ICC (0.55-0.79) for different aortic segments. Intra- and interobserver reproducibility was good-to-excellent for regional aortic WSSmax (ICC???0.78; COV???17%) and strong-to-excellent for WSSmean (ICC???0.86; COV???11%). In general, ascending aortic segments showed more WSSmax/WSSmean variability compared to aortic arch or descending aortic segments for scan-rescan, intraobserver and interobserver comparison. CONCLUSIONS:Scan-rescan reproducibility was good for WSSmean and moderate for WSSmax for all thoracic aortic segments over multiple systolic phases in healthy volunteers. Intra/interobserver reproducibility for segmental WSS assessment was good-to-excellent. Variability of WSSmax is higher and should be taken into account in case of individual follow-up or in comparative rest-stress studies to avoid misinterpretation.

SUBMITTER: van der Palen RLF

PROVIDER: S-EPMC6132557 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Scan-rescan reproducibility of segmental aortic wall shear stress as assessed by phase-specific segmentation with 4D flow MRI in healthy volunteers.

van der Palen Roel L F RLF Roest Arno A W AAW van den Boogaard Pieter J PJ de Roos Albert A Blom Nico A NA Westenberg Jos J M JJM

Magma (New York, N.Y.) 20180526 5

<h4>Objective</h4>The aim was to investigate scan-rescan reproducibility and observer variability of segmental aortic 3D systolic wall shear stress (WSS) by phase-specific segmentation with 4D flow MRI in healthy volunteers.<h4>Materials and methods</h4>Ten healthy volunteers (age 26.5 ± 2.6 years) underwent aortic 4D flow MRI twice. Maximum 3D systolic WSS (WSSmax) and mean 3D systolic WSS (WSSmean) for five thoracic aortic segments over five systolic cardiac phases by phase-specific segmentati ...[more]

PMID: 29804208

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Project description:BackgroundHemodynamic aorta parameters can be derived from 4D flow MRI, but this requires lumen segmentation. In both commercially available and research 4D flow MRI software tools, lumen segmentation is mostly (semi-)automatically performed and subsequently manually improved by an observer. Since the segmentation variability, together with 4D flow MRI data and image processing algorithms, will contribute to the reproducibility of patient-specific flow properties, the observer's lumen segmentation reproducibility and repeatability needs to be assessed.PurposeTo determine the interexamination, interobserver reproducibility, and intraobserver repeatability of aortic lumen segmentation on 4D flow MRI.Study typeProspective and retrospective.PopulationA healthy volunteer cohort of 10 subjects who underwent 4D flow MRI twice. Also, a clinical cohort of six subjects who underwent 4D flow MRI once.Field strength/sequence3T; time-resolved three-directional and 3D velocity-encoded sequence (4D flow MRI).AssessmentThe thoracic aorta was segmented on the 4D flow MRI in five systolic phases. By positioning six planes perpendicular to a segmentation's centerline, the aorta was divided into five segments. The volume, surface area, centerline length, maximal diameter, and curvature radius were determined for each segment.Statistical testsTo assess the reproducibility, the coefficient of variation (COV), Pearson correlation coefficient (r), and intraclass correlation coefficient (ICC) were calculated.ResultsThe interexamination and interobserver reproducibility and intraobserver repeatability were comparable for each parameter. For both cohorts there was very good reproducibility and repeatability for volume, surface area, and centerline length (COV = 10-32%, r = 0.54-0.95 and ICC = 0.65-0.99), excellent reproducibility and repeatability for maximal diameter (COV = 3-11%, r = 0.94-0.99, ICC = 0.94-0.99), and good reproducibility and repeatability for curvature radius (COV = 25-62%, r = 0.73-0.95, ICC = 0.84-0.97).Data conclusionThis study demonstrated no major reproducibility and repeatability limitations for 4D flow MRI aortic lumen segmentation.Level of evidence3 TECHNICAL EFFICACY STAGE: 2.

Project description:PurposeTo determine the influence of preprocessing on the repeatability and redundancy of radiomics features extracted using a popular open-source radiomics software package in a scan-rescan glioblastoma MRI study.Materials and methodsIn this study, a secondary analysis of T2-weighted fluid-attenuated inversion recovery (FLAIR) and T1-weighted postcontrast images from 48 patients (mean age, 56 years [range, 22-77 years]) diagnosed with glioblastoma were included from two prospective studies (ClinicalTrials.gov NCT00662506 [2009-2011] and NCT00756106 [2008-2011]). All patients underwent two baseline scans 2-6 days apart using identical imaging protocols on 3-T MRI systems. No treatment occurred between scan and rescan, and tumors were essentially unchanged visually. Radiomic features were extracted by using PyRadiomics (https://pyradiomics.readthedocs.io/) under varying conditions, including normalization strategies and intensity quantization. Subsequently, intraclass correlation coefficients were determined between feature values of the scan and rescan.ResultsShape features showed a higher repeatability than intensity (adjusted P < .001) and texture features (adjusted P < .001) for both T2-weighted FLAIR and T1-weighted postcontrast images. Normalization improved the overlap between the region of interest intensity histograms of scan and rescan (adjusted P < .001 for both T2-weighted FLAIR and T1-weighted postcontrast images), except in scans where brain extraction fails. As such, normalization significantly improves the repeatability of intensity features from T2-weighted FLAIR scans (adjusted P = .003 [z score normalization] and adjusted P = .002 [histogram matching]). The use of a relative intensity binning strategy as opposed to default absolute intensity binning reduces correlation between gray-level co-occurrence matrix features after normalization.ConclusionBoth normalization and intensity quantization have an effect on the level of repeatability and redundancy of features, emphasizing the importance of both accurate reporting of methodology in radiomics articles and understanding the limitations of choices made in pipeline design. Supplemental material is available for this article. © RSNA, 2020See also the commentary by Tiwari and Verma in this issue.

Project description:ObjectivesThis study aims to measure the reproducibility of radiomic features in pancreatic parenchyma and ductal adenocarcinomas (PDAC) in patients who underwent consecutive contrast-enhanced computed tomography (CECT) scans.MethodsIn this IRB-approved and HIPAA-compliant retrospective study, 37 pairs of scans from 37 unique patients who underwent CECTs within a 2-week interval were included in the analysis of the reproducibility of features derived from pancreatic parenchyma, and a subset of 18 pairs of scans were further analyzed for the reproducibility of features derived from PDAC. In each patient, pancreatic parenchyma and pancreatic tumor (when present) were manually segmented by two radiologists independently. A total of 266 radiomic features were extracted from the pancreatic parenchyma and tumor region and also the volume and diameter of the tumor. The concordance correlation coefficient (CCC) was calculated to assess feature reproducibility for each patient in three scenarios: (1) different radiologists, same CECT; (2) same radiologist, different CECTs; and (3) different radiologists, different CECTs.ResultsAmong pancreatic parenchyma-derived features, using a threshold of CCC > 0.90, 58/266 (21.8%) and 48/266 (18.1%) features met the threshold for scenario 1, 14/266 (5.3%) and 15/266 (5.6%) for scenario 2, and 14/266 (5.3%) and 10/266 (3.8%) for scenario 3. Among pancreatic tumor-derived features, 11/268 (4.1%) and 17/268 (6.3%) features met the threshold for scenario 1, 1/268 (0.4%) and 5/268 (1.9%) features met the threshold for scenario 2, and no features for scenario 3 met the threshold, respectively.ConclusionsVariations between CECT scans affected radiomic feature reproducibility to a greater extent than variation in segmentation. A smaller number of pancreatic tumor-derived radiomic features were reproducible compared with pancreatic parenchyma-derived radiomic features under the same conditions.Key points• For pancreatic-derived radiomic features from contrast-enhanced CT (CECT), fewer than 25% are reproducible (with a threshold of CCC < 0.9) in a clinical heterogeneous dataset. • Variations between CECT scans affected the number of reproducible radiomic features to a greater extent than variations in radiologist segmentation. • A smaller number of pancreatic tumor-derived radiomic features were reproducible compared with pancreatic parenchyma-derived radiomic features under the same conditions.

Dataset Information

Scan-rescan reproducibility of segmental aortic wall shear stress as assessed by phase-specific segmentation with 4D flow MRI in healthy volunteers.

Publications

Scan-rescan reproducibility of segmental aortic wall shear stress as assessed by phase-specific segmentation with 4D flow MRI in healthy volunteers.

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