Dataset Information

Flow measurement at the aortic root - impact of location of through-plane phase contrast velocity mapping.

ABSTRACT: BACKGROUND:Cardiovascular magnetic resonance (CMR) is considered the gold standard of cardiac volumetric measurements. Flow in the aortic root is often measured at the sinotubular junction, even though placing the slice just above valve level may be more precise. It is unknown how much flow measurements vary at different levels in the aortic root and which level corresponds best to left ventricle volumetry. METHODS:All patients were older than 70 years presenting with at least one of the following diagnoses: diabetes, hypertension, prior stroke and/or heart failure. Patients with arrhythmias during CMR and aortic stenosis were excluded from the analyses. Stroke volumes were measured volumetrically (SVref) from steady-state free precision short axis images covering the entire left ventricle, excluding the papillary muscles and including the left ventricular outflow tract. Flow sequences (through-plane phase contrast velocity mapping) were obtained at valve level (SVV) and at the sinotubular junction (SVST). Firstly, SVV and SVST were compared to each other and secondly, after excluding patients with mitral regurgitations to ensure that stroke volumes measured volumetrically would theoretically be equal to flow measurements, SVV and SVST were compared to SVref. RESULTS:Initially, 152 patients were included. 22 were excluded because of arrhythmias during scans and 9 were excluded for aortic stenosis. Accordingly, data from 121 patients were analysed and of these 63 had visually evident mitral regurgitation on cine images. On average, stroke volumes measured with flow at the sinotubular junction was 13-16 % lower than when measured at valve level (70.0 mL ±13.8 vs. 81.8 mL ±15.5). This was in excess of the expected difference caused by the outflow to the coronary arteries. In the 58 patients with no valvulopathy, stroke volumes measured at valve level (79.0 mL ±12.4) was closest to the volumetric measurement (85.4 mL ±12.0) but still significantly lower (p?

SUBMITTER: Bertelsen L

PROVIDER: S-EPMC5013573 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Flow measurement at the aortic root - impact of location of through-plane phase contrast velocity mapping.

Bertelsen Litten L Svendsen Jesper Hastrup JH Køber Lars L Haugan Ketil K Højberg Søren S Thomsen Carsten C Vejlstrup Niels N

Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance 20160907 1

<h4>Background</h4>Cardiovascular magnetic resonance (CMR) is considered the gold standard of cardiac volumetric measurements. Flow in the aortic root is often measured at the sinotubular junction, even though placing the slice just above valve level may be more precise. It is unknown how much flow measurements vary at different levels in the aortic root and which level corresponds best to left ventricle volumetry.<h4>Methods</h4>All patients were older than 70 years presenting with at least one ...[more]

PMID: 27599727

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Project description:PurposeCardiovascular diseases remain the number one death cause worldwide. Preclinical 4D flow phase contrast magnetic resonance imaging can provide substantial insights in the analysis of aortic pathophysiologies in various animal models. These insights may allow a better understanding of pathophysiologies, therapy monitoring, and can possibly be translated to humans. This study provides a framework to acquire the velocity field within the aortic arch. It analyses important flow values at different locations within the aortic arch. Imaging parameters with high temporal and spatial resolution are provided, that still allow combining this time-consuming method with other necessary imaging-protocols.MethodsA new setup was established where a prospectively gated 4D phase contrast sequence is combined with a highly sensitive cryogenic coil on a preclinical magnetic resonance scanner. The sequence was redesigned to maintain a close to steady state condition of the longitudinal magnetization and hence to overcome steady state artifacts. Imaging parameters were optimized to provide high spatial and temporal resolution. Pathline visualizations were generated from the acquired velocity data in order to display complex flow patterns.ResultsOur setup allows data acquisition with at least two times the rate than that of previous publications based on Cartesian encoding, at an improved image quality. The "steady state" sequence reduces observed artifacts and provides uniform image intensity over the heart cycle. This made possible quantification of blood speed and wall shear stress (WSS) within the aorta and its branches. The highest velocities were observed in the ascending aorta with 137.5 ± 8 cm/s. Peak velocity values in the Brachiocephalic trunk were 57 ± 12 cm/s. Quantification showed that the peak flow occurs around 20 ms post R-wave in the ascending aorta. The highest mean axial wall shear stress was observed in the analysis plane between the left common carotid artery (LCCA) and the left subclavian artery. A stable image quality allows visualizing complex flow patterns by means of streamlines and for the first time, to the best of our knowledge, pathline visualizations from 4D flow MRI in mice.ConclusionThe described setup allows analyzing pathophysiologies in mouse models of cardiovascular diseases in the aorta and its branches with better image quality and higher spatial and temporal resolution than previous Cartesian publications. Pathlines provide an advanced analysis of complex flow patterns in the murine aorta. An imaging protocol is provided that offers the possibility to acquire the aortic arch at sufficiently high resolution in less than one hour. This allows the combination of the flow assessment with other multifunctional imaging protocols.

Project description: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 <?0.01?min/case (1.2?min for entire dataset); manual segmentation required 3.96?±?0.36?min/case (12.5?h for entire dataset). Correlations between machine learning and manual segmentation-derived flow approached unity (r?=?0.99, p?<?0.001). Machine learning yielded smaller absolute differences with manual segmentation than did commercial automation (1.85?±?1.80 vs. 3.33?±?3.18?mL, p?<?0.01): Nearly all (98%) of cases differed by ?5?mL between machine learning and manual methods. Among patients without advanced mitral regurgitation, machine learning correlated well (r?=?0.63, p?<?0.001) and yielded small differences with cine-CMR stroke volume (? 1.3?±?17.7?mL, p?=?0.36). Among advanced mitral regurgitation patients, machine learning yielded lower stroke volume than did volumetric cine-CMR (? 12.6?±?20.9?mL, p?=?0.005), further supporting validity of this method. Among the external validation cohort (n?=?80) acquired using a different CMR vendor, the algorithm yielded equivalently small differences (? 1.39?±?1.77?mL, p?=?0.4) and high correlations (r?=?0.99, p?<?0.001) with manual segmentation, including similar results in 20 patients with bicuspid or stenotic aortic valve pathology (? 1.71?±?2.25?mL, p?=?0.25). CONCLUSION:Fully automated machine learning PC-CMR segmentation performs robustly for aortic flow quantification - yielding rapid segmentation, small differences with manual segmentation, and identification of differential forward/left ventricular volumetric stroke volume in context of concomitant mitral regurgitation. Findings support use of machine learning for analysis of large scale CMR datasets.

Dataset Information

Flow measurement at the aortic root - impact of location of through-plane phase contrast velocity mapping.

Publications

Flow measurement at the aortic root - impact of location of through-plane phase contrast velocity mapping.

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