Dataset Information

Accuracy of subject-specific prediction of end-systolic time in MRI across a range of RR intervals.

ABSTRACT: BACKGROUND:Prediction of End-Systole time is of utmost importance for cardiac MRI to correctly associate acquired k-space lines during reconstruction of cine acquisitions. This prediction is usually based on the patient's heart rate using Weissler's formula, which was calibrated by linear regression within a population and cannot account for individual variability. OBJECTIVE:We propose an automatic method to build a personalized model that better predicts end-systole. METHODS:A phase contrast sequence was modified to acquire only central k-space line with 6.6ms temporal resolution, in a slice passing through the aorta during 128 heartbeats in 35 subjects. Segmentation of aorta and detection of end of systolic ejection was automatic. Duration of electromechanical systole duration as function of heart rate was determined for each subject separately. RESULTS:In comparison with the global models, the adapted cardiac model predicted significantly better both echocardiographic end-systolic reference (bias = 0ms vs 17ms, p<0.001) and MRI measurements (bias = 6.8ms vs 17ms). Favorable impact was shown on the cine reconstruction of the 5 subjects with the higher cardiac variability (p = 0.02). CONCLUSIONS:Personalization of cardiac model to the subject is feasible in MRI and reduces the error of prediction of systole.

SUBMITTER: Meyer C

PROVIDER: S-EPMC5466307 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Accuracy of subject-specific prediction of end-systolic time in MRI across a range of RR intervals.

Meyer Christophe C Felblinger Jacques J Vuissoz Pierre-André PA Bonnemains Laurent L

PloS one 20170609 6

<h4>Background</h4>Prediction of End-Systole time is of utmost importance for cardiac MRI to correctly associate acquired k-space lines during reconstruction of cine acquisitions. This prediction is usually based on the patient's heart rate using Weissler's formula, which was calibrated by linear regression within a population and cannot account for individual variability.<h4>Objective</h4>We propose an automatic method to build a personalized model that better predicts end-systole.<h4>Methods</ ...[more]

PMID: 28598980

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Project description:The left ventricular (LV) end-systolic (ES) pressure volume relationship (ESPVR) is the cornerstone of systolic LV function analysis. We describe a 2D real-time (RT) MRI-based method (RTPVR) with separate software tools for 1) semi-automatic level set-based shape prior method (LSSPM) of the LV, 2) generation of synchronized pressure area loops and 3) calculation of the ESPVR. We used the RTPVR method to measure ventricular geometry, ES pressure area relationship (ESPAR) and ESPVR during vena cava occlusion (VCO) in normal sheep. 14 adult sheep were anesthetized and underwent measurement of LV systolic function. Ten of the 14 sheep underwent RTMRI and eight of the 14 underwent measurement with conductance catheter; 4 had both RTMRI and conductance measurements. 2D cross sectional RTMRI were performed at apex, mid-ventricle and base levels during separate VCOs. The Dice similarity coefficient was used to compare LSSPM and manual image segmentation and thus determine LSSPM accuracy. LV cross-sectional area, major and minor axis length, axis ratio, major axis orientation angle and ESPAR were measured at each LV level. ESPVR was calculated with a trapezoidal rule. The Dice similarity coefficient between LSSPM and manual segmentation by two readers was 87.31±2.51% and 88.13±3.43%. All cross sections became more elliptical during VCO. The major axis orientation shifted during VCO but remained in the septo-lateral direction. LV chamber obliteration at the apical level occurred during VCO in 7 of 10 sheep that underwent RTMRI. ESPAR was non-linear at all levels. Finally, ESPVR was non-linear because of apical collapse. ESPVR measured by conductance catheter (EES,Index = 2.23±0.66 mmHg/ml/m2) and RT (EES,Index = 2.31±0.31 mmHg/ml/m2) was not significantly different. LSSPM segmentation of 2D RT MRI images is accurate and allows calculation of LV geometry, ESPAR and ESPVR during VCO. In the future, RTPVR will facilitate determination of regional systolic material parameters underlying ESPVR.

Dataset Information

Accuracy of subject-specific prediction of end-systolic time in MRI across a range of RR intervals.

Publications

Accuracy of subject-specific prediction of end-systolic time in MRI across a range of RR intervals.

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