Dataset Information

Reproducibility of Aorta Segmentation on 4D Flow MRI in Healthy Volunteers.

ABSTRACT:

Background

Hemodynamic 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.

Purpose

To determine the interexamination, interobserver reproducibility, and intraobserver repeatability of aortic lumen segmentation on 4D flow MRI.

Study type

Prospective and retrospective.

Population

A 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/sequence

3T; time-resolved three-directional and 3D velocity-encoded sequence (4D flow MRI).

Assessment

The 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 tests

To assess the reproducibility, the coefficient of variation (COV), Pearson correlation coefficient (r), and intraclass correlation coefficient (ICC) were calculated.

Results

The 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 conclusion

This study demonstrated no major reproducibility and repeatability limitations for 4D flow MRI aortic lumen segmentation.

Level of evidence

3 TECHNICAL EFFICACY STAGE: 2.

SUBMITTER: Juffermans JF

PROVIDER: S-EPMC7984392 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Publications

Reproducibility of Aorta Segmentation on 4D Flow MRI in Healthy Volunteers.

Juffermans Joe F JF Westenberg Jos J M JJM van den Boogaard Pieter J PJ Roest Arno A W AAW van Assen Hans C HC van der Palen Roel L F RLF Lamb Hildo J HJ

Journal of magnetic resonance imaging : JMRI 20201111 4

<h4>Background</h4>Hemodynamic 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 s ...[more]

PMID: 33179389

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Project description:PURPOSE:4D and 2D phase-contrast MRI (2D Flow MRI, 4D Flow MRI, respectively) are increasingly being used to noninvasively assess pulmonary hypertension (PH). The goals of this study were i) to evaluate whether established quantitative parameters in 2D Flow MRI associated with pulmonary hypertension can be assessed using 4D Flow MRI; ii) to compare results from 4D Flow MRI on a digital broadband 3T MR system with data from clinically established MRI-techniques as well as conservation of mass analysis and phantom correction and iii) to elaborate on the added value of secondary flow patterns in detecting PH. METHODS:11 patients with PH (4f, 63 ± 16y), 15 age-matched healthy volunteers (9f, 56 ± 11y), and 20 young healthy volunteers (13f, 23 ± 2y) were scanned on a 3T MR scanner (Philips Ingenia). Subjects were examined with a 4D Flow, a 2D Flow and a bSSFP sequence. For extrinsic comparison, quantitative parameters measured with 4D Flow MRI were compared to i) a static phantom, ii) 2D Flow acquisitions and iii) stroke volume derived from a bSSFP sequence. For intrinsic comparison conservation of mass-analysis was employed. Dedicated software was used to extract various flow, velocity, and anatomical parameters. Visualization of blood flow was performed to detect secondary flow patterns. RESULTS:Overall, there was good agreement between all techniques, 4D Flow results revealed a considerable spread. Data improved after phantom correction. Both 4D and 2D Flow MRI revealed concordant results to differentiate patients from healthy individuals, especially based on values derived from anatomical parameters. The visualization of a vortex, indicating the presence of PH was achieved in 9 /11 patients and 2/35 volunteers. DISCUSSION:This study confirms that quantitative parameters used for characterizing pulmonary hypertension can be gathered using 4D Flow MRI within clinically reasonable limits of agreement. Despite its unfavorable spatial and lesser temporal resolution and a non-neglible spread of results, the identification of diseased study participants was possible.

Project description:BackgroundComplex and reverse flow in the aorta has been implicated in aneurysm development and stroke via retrograde embolization.PurposeTo evaluate global and regional differences between standard 2D plane-based and volumetric voxel-based quantification of regional forward/reverse flow, and reverse flow fraction (RFF) in the aorta.Study typeRetrospective.SubjectsIn all, 35 subjects: 10 healthy controls (age: 57 ± 7 years, nine male), nine patients without aortic valve regurgitation (AR) (age: 63 ± 10 years, seven male), six patients with mild AR (age: 66 ± 6 years, five male), and 10 with moderate or severe AR (age: 60 ± 16 years, eight male).Field strength/sequence4D flow MRI (3T and 1.5T) was employed to acquire 3D blood flow velocities with entire thoracic aorta in all subjects.AssessmentData analysis included standard 2D plane-based quantification of forward/reverse flow, and RFF-plane. In addition, a new semiautomatic workflow based on 3D segmentation and extraction of an aorta centerline was developed for voxel-by-voxel visualization (forward/reverse flow and RFF-voxel maps) and quantification of regional voxel-by-voxel forward/reverse flow in the entire thoracic aorta.Statistical testsKruskal-Wallis tests were performed to test for differences between groups. A two-sample t-test or Wilcoxon rank sum test was used to compare voxel-based and plane-based results.ResultsSemiautomatic plane-based analysis showed excellent agreement with standard manual plane-based analysis for net flow and RFF-plane (RFF-plane: y = 0.99x-0.0, net flow: y = 1.00x-0.21, R > 0.99, P < 0.0001). Voxel-by-voxel maps demonstrated marked regional flow reversal in the ascending aorta in all patients and RFF-voxel was significantly increased (P < 0.001) compared to RFF-plane for all four groups, with the most pronounced differences for mild AR (18.0 ± 15.2% vs. 4.7 ± 5.4%). Voxel-based flow and RFF-voxel along the aorta showed areas with marked regional flow reversal (eg, vortex flow) compared to plane-based analysis.Data conclusionVoxel-based analysis demonstrated regional flow reversal that was not detected by plane-based analysis.Level of evidence3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:1276-1286.

Project description:BackgroundSegmenting the whole heart over the cardiac cycle in 4D flow MRI is a challenging and time-consuming process, as there is considerable motion and limited contrast between blood and tissue.PurposeTo develop and evaluate a deep learning-based segmentation method to automatically segment the cardiac chambers and great thoracic vessels from 4D flow MRI.Study typeRetrospective.SubjectsA total of 205 subjects, including 40 healthy volunteers and 165 patients with a variety of cardiac disorders were included. Data were randomly divided into training (n = 144), validation (n = 20), and testing (n = 41) sets.Field strength/sequenceA 3 T/time-resolved velocity encoded 3D gradient echo sequence (4D flow MRI).AssessmentA 3D neural network based on the U-net architecture was trained to segment the four cardiac chambers, aorta, and pulmonary artery. The segmentations generated were compared to manually corrected atlas-based segmentations. End-diastolic (ED) and end-systolic (ES) volumes of the four cardiac chambers were calculated for both segmentations.Statistical testsDice score, Hausdorff distance, average surface distance, sensitivity, precision, and miss rate were used to measure segmentation accuracy. Bland-Altman analysis was used to evaluate agreement between volumetric parameters.ResultsThe following evaluation metrics were computed: mean Dice score (0.908 ± 0.023) (mean ± SD), Hausdorff distance (1.253 ± 0.293 mm), average surface distance (0.466 ± 0.136 mm), sensitivity (0.907 ± 0.032), precision (0.913 ± 0.028), and miss rate (0.093 ± 0.032). Bland-Altman analyses showed good agreement between volumetric parameters for all chambers. Limits of agreement as percentage of mean chamber volume (LoA%), left ventricular: 9.3%, 13.5%, left atrial: 12.4%, 16.9%, right ventricular: 9.9%, 15.6%, and right atrial: 18.7%, 14.4%; for ED and ES, respectively.Data conclusionThe addition of this technique to the 4D flow MRI assessment pipeline could expedite and improve the utility of this type of acquisition in the clinical setting.Evidence level4 TECHNICAL EFFICACY: Stage 1.

Project description:IntroductionPhase contrast MRI allows for the examination of complex hemodynamics in the heart and adjacent great vessels. Vortex flow patterns seem to play an important role in certain vascular pathologies. We propose two- and three-dimensional metrics for the objective quantification of aortic vortex blood flow in 4D phase contrast MRI.Materials and methodsFor two-dimensional vorticity assessment, a standardized set of 6 regions-of-interest (ROIs) was defined throughout the course of the aorta. For each ROI, a heatmap of time-resolved vorticity values [Formula: see text] was computed. Evolution of minimum, maximum, and average values as well as opposing rotational flow components were analyzed. For three-dimensional analysis, vortex core detection was implemented combining the predictor-corrector method with λ2 correction. Strength, elongation, and radial expansion of the detected vortex core were recorded over time. All methods were applied to 4D flow MRI datasets of 9 healthy subjects, 2 patients with mildly dilated aorta, and 1 patient with aortic aneurysm.ResultsVorticity quantification in the 6 standardized ROIs enabled the description of physiological vortex flow in the healthy aorta. Helical flow developed early in the ascending aorta (absolute vorticity = 166.4±86.4 s-1 at 12% of cardiac cycle) followed by maximum values in mid-systole in the aortic arch (240.1±45.2 s-1 at 16%). Strength, elongation, and radial expansion of 3D vortex cores escalated in early systole, reaching a peak in mid systole (strength = 241.2±30.7 s-1 at 17%, elongation = 65.1±34.6 mm at 18%, expansion = 80.1±48.8 mm2 at 20%), before all three parameters similarly decreased to overall low values in diastole. Flow patterns were considerably altered in patient data: Vortex flow developed late in mid/end-systole close to the aortic bulb and no physiological helix was found in the aortic arch.ConclusionsWe have introduced objective measures for quantification of vortical flow in 4D phase contrast MRI. Vortex blood flow in the thoracic aorta could be consistently described in all healthy volunteers. In patient data, pathologically altered vortex flow was observed.

Dataset Information

Reproducibility of Aorta Segmentation on 4D Flow MRI in Healthy Volunteers.

Background

Purpose

Study type

Population

Field strength/sequence

Assessment

Statistical tests

Results

Data conclusion

Level of evidence

Publications

Reproducibility of Aorta Segmentation on 4D Flow MRI in Healthy Volunteers.

Similar Datasets

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