Dataset Information

Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification.

ABSTRACT: BACKGROUND:Phase contrast (PC) cardiovascular magnetic resonance (CMR) is widely employed for flow quantification, but analysis typically requires time consuming manual segmentation which can require human correction. Advances in machine learning have markedly improved automated processing, but have yet to be applied to PC-CMR. This study tested a novel machine learning model for fully automated analysis of PC-CMR aortic flow. METHODS:A machine learning model was designed to track aortic valve borders based on neural network approaches. The model was trained in a derivation cohort encompassing 150 patients who underwent clinical PC-CMR then compared to manual and commercially-available automated segmentation in a prospective validation cohort. Further validation testing was performed in an external cohort acquired from a different site/CMR vendor. RESULTS:Among 190 coronary artery disease patients prospectively undergoing CMR on commercial scanners (84% 1.5T, 16% 3T), machine learning segmentation was uniformly successful, requiring no human intervention: Segmentation time was

SUBMITTER: Bratt A

PROVIDER: S-EPMC6322266 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification.

Bratt Alex A Kim Jiwon J Pollie Meridith M Beecy Ashley N AN Tehrani Nathan H NH Codella Noel N Perez-Johnston Rocio R Palumbo Maria Chiara MC Alakbarli Javid J Colizza Wayne W Drexler Ian R IR Azevedo Clerio F CF Kim Raymond J RJ Devereux Richard B RB Weinsaft Jonathan W JW

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance 20190107 1

<h4>Background</h4>Phase contrast (PC) cardiovascular magnetic resonance (CMR) is widely employed for flow quantification, but analysis typically requires time consuming manual segmentation which can require human correction. Advances in machine learning have markedly improved automated processing, but have yet to be applied to PC-CMR. This study tested a novel machine learning model for fully automated analysis of PC-CMR aortic flow.<h4>Methods</h4>A machine learning model was designed to track ...[more]

PMID: 30612574

Similar Datasets

Project description:PurposeTo generate fully automated and fast 4D-flow MRI-based 3D segmentations of the aorta using deep learning for reproducible quantification of aortic flow, peak velocity, and dimensions.MethodsA total of 1018 subjects with aortic 4D-flow MRI (528 with bicuspid aortic valve, 376 with tricuspid aortic valve and aortic dilation, 114 healthy controls) comprised the data set. A convolutional neural network was trained to generate 3D aortic segmentations from 4D-flow data. Manual segmentations served as the ground truth (N = 499 training, N = 101 validation, N = 418 testing). Dice scores, Hausdorff distance, and average symmetrical surface distance were calculated to assess performance. Aortic flow, peak velocity, and lumen dimensions were quantified at the ascending, arch, and descending aorta and compared using Bland-Altman analysis. Interobserver variability of manual analysis was assessed on a subset of 40.ResultsConvolutional neural network segmentation required 0.438 ± 0.355 seconds versus 630 ± 254 seconds for manual analysis and demonstrated excellent performance with a median Dice score of 0.951 (0.930-0.966), Hausdorff distance of 2.80 (2.13-4.35), and average symmetrical surface distance of 0.176 (0.119-0.290). Excellent agreement was found for flow, peak velocity, and dimensions with low bias and limits of agreement less than 10% difference versus manual analysis. For aortic volume, limits of agreement were moderate within 16.3%. Interobserver variability (median Dice score: 0.950; Hausdorff distance: 2.45; and average symmetrical surface distance: 0.145) and convolutional neural network-based analysis (median Dice score: 0.953-0.959; Hausdorff distance: 2.24-2.91; and average symmetrical surface distance: 0.145-1.98 to observers) demonstrated similar reproducibility.ConclusionsDeep learning enabled fast and automated 3D aortic segmentation from 4D-flow MRI, demonstrating its potential for efficient clinical workflows. Future studies should investigate its utility for other vasculature and multivendor applications.

Project description:BackgroundNon-invasive assessment of myocardial ischaemia is a cornerstone of the diagnosis of coronary artery disease. Measurement of myocardial blood flow (MBF) using positron emission tomography (PET) is the current reference standard for non-invasive quantification of myocardial ischaemia. Dynamic myocardial perfusion cardiovascular magnetic resonance (CMR) offers an alternative to PET and a recently developed method with automated inline perfusion mapping has shown good correlation of MBF values between CMR and PET. This study assessed the repeatability of myocardial perfusion mapping by CMR in healthy subjects.MethodsForty-two healthy subjects were recruited and underwent adenosine stress and rest perfusion CMR on two visits. Scans were repeated with a minimum interval of 7 days. Intrastudy rest and stress MBF repeatability were assessed with a 15-min interval between acquisitions. Interstudy rest and stress MBF and myocardial perfusion reserve (MPR) were measured for global myocardium and regionally for coronary territories and slices.ResultsThere was no significant difference in intrastudy repeated global rest MBF (0.65 ± 0.13 ml/g/min vs 0.62 ± 0.12 ml/g/min, p = 0.24, repeatability coefficient (RC) =24%) or stress (2.89 ± 0.56 ml/g/min vs 2.83 ± 0.64 ml/g/min, p = 0.41, RC = 29%) MBF. No significant difference was seen in interstudy repeatability for global rest MBF (0.64 ± 0.13 ml/g/min vs 0.64 ± 0.15 ml/g/min, p = 0.80, RC = 32%), stress MBF (2.71 ± 0.61 ml/g/min vs 2.55 ± 0.57 ml/g/min, p = 0.12, RC = 33%) or MPR (4.24 ± 0.69 vs 3.73 ± 0.76, p = 0.25, RC = 36%). Regional repeatability was good for stress (RC = 30-37%) and rest MBF (RC = 32-36%) but poorer for MPR (RC = 35-43%). Within subject coefficient of variation was 8% for rest and 11% for stress within the same study, and 11% for rest and 12% for stress between studies.ConclusionsFully automated, inline, myocardial perfusion mapping by CMR shows good repeatability that is similar to the published PET literature. Both rest and stress MBF show better repeatability than MPR, particularly in regional analysis.

Project description:BackgroundCardiovascular magnetic resonance (CMR) represents the clinical gold standard for the assessment of biventricular morphology and function. Since manual post-processing is time-consuming and prone to observer variability, efforts have been directed towards automated volumetric quantification. In this study, we sought to validate the accuracy of a novel approach providing fully automated quantification of biventricular volumes and function in a "real-world" clinical setting.MethodsThree-hundred CMR examinations were randomly selected from the local data base. Fully automated quantification of left ventricular (LV) mass, LV and right ventricular (RV) end-diastolic and end-systolic volumes (EDV/ESV), stroke volume (SV) and ejection fraction (EF) were performed overnight using commercially available software (suiteHEART®, Neosoft, Pewaukee, Wisconsin, USA). Parameters were compared to manual assessments (QMass®, Medis Medical Imaging Systems, Leiden, Netherlands). Sub-group analyses were further performed according to image quality, scanner field strength, the presence of implanted aortic valves and repaired Tetralogy of Fallot (ToF).ResultsBiventricular automated segmentation was feasible in all 300 cases. Overall agreement between fully automated and manually derived LV parameters was good (LV-EF: intra-class correlation coefficient [ICC] 0.95; bias - 2.5% [SD 5.9%]), whilst RV agreement was lower (RV-EF: ICC 0.72; bias 5.8% [SD 9.6%]). Lowest agreement was observed in case of severely altered anatomy, e.g. marked RV dilation but normal LV dimensions in repaired ToF (LV parameters ICC 0.73-0.91; RV parameters ICC 0.41-0.94) and/or reduced image quality (LV parameters ICC 0.86-0.95; RV parameters ICC 0.56-0.91), which was more common on 3.0 T than on 1.5 T.ConclusionsFully automated assessments of biventricular morphology and function is robust and accurate in a clinical routine setting with good image quality and can be performed without any user interaction. However, in case of demanding anatomy (e.g. repaired ToF, severe LV hypertrophy) or reduced image quality, quality check and manual re-contouring are still required.

Dataset Information

Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification.

Publications

Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets