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Real-time free-breathing cardiac imaging with self-calibrated through-time radial GRAPPA.

ABSTRACT: Real-time free-breathing cardiac imaging with highly undersampled radial trajectories has previously been successfully demonstrated using calibrated radial generalized autocalibrating partially parallel acquisition (rGRAPPA). A self-calibrated approach for rGRAPPA is proposed that removes the need for the calibration prescan.To investigate the effect of various parameters on image quality, a comprehensive imaging study on one normal swine was performed. Root mean squared errors (RMSEs) were computed with respect to gold standard acquisitions, and several acquisition/reconstruction strategies were compared. Additionally, the method was tested on 13 human subjects, and RMSEs relative to standard through-time radial GRAPPA were computed.Real-time images with high spatiotemporal resolution were obtained. Image quality was comparable to calibrated through-time rGRAPPA with endocardial and epicardial borders clearly delineated. In the swine, the average RMSE between self-calibrated and gold-standard calibrated images was 5.18?±?0.84%. In normal human subjects, the average RMSE between self-calibrated and calibrated through-time rGRAPPA was 3.79?±?0.64%. For lower accelerations rates (R?=?6-8) image quality was similar to comparable calibrated scans though RMSE increased for higher degrees of undersampling (R?=?12-16).Highly accelerated real-time imaging with undersampled radial trajectories without additional calibration data is feasible. Image quality was acceptable for real-time cardiac MRI applications demanding high speed. Magn Reson Med 77:250-264, 2017. © 2016 Wiley Periodicals, Inc.

SUBMITTER: Sayin O

PROVIDER: S-EPMC5920649 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Real-time free-breathing cardiac imaging with self-calibrated through-time radial GRAPPA.

Sayin Ozan O Saybasili Haris H Zviman M Muz MM Griswold Mark M Halperin Henry H Seiberlich Nicole N Herzka Daniel A DA

Magnetic resonance in medicine 20160310 1

<h4>Purpose</h4>Real-time free-breathing cardiac imaging with highly undersampled radial trajectories has previously been successfully demonstrated using calibrated radial generalized autocalibrating partially parallel acquisition (rGRAPPA). A self-calibrated approach for rGRAPPA is proposed that removes the need for the calibration prescan.<h4>Methods</h4>To investigate the effect of various parameters on image quality, a comprehensive imaging study on one normal swine was performed. Root mean ...[more]

PMID: 26969611

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Project description:In recent years, a variety of deep learning networks for cardiac MRI (CMR) segmentation have been developed and analyzed. However, nearly all of them are focused on cine CMR under breathold. In this work, accuracy of deep learning methods is assessed for volumetric analysis (via segmentation) of the left ventricle in real-time free-breathing CMR at rest and under exercise stress. Data from healthy volunteers (n = 15) for cine and real-time free-breathing CMR at rest and under exercise stress were analyzed retrospectively. Exercise stress was performed using an ergometer in the supine position. Segmentations of two deep learning methods, a commercially available technique (comDL) and an openly available network (nnU-Net), were compared to a reference model created via the manual correction of segmentations obtained with comDL. Segmentations of left ventricular endocardium (LV), left ventricular myocardium (MYO), and right ventricle (RV) are compared for both end-systolic and end-diastolic phases and analyzed with Dice's coefficient. The volumetric analysis includes the cardiac function parameters LV end-diastolic volume (EDV), LV end-systolic volume (ESV), and LV ejection fraction (EF), evaluated with respect to both absolute and relative differences. For cine CMR, nnU-Net and comDL achieve Dice's coefficients above 0.95 for LV and 0.9 for MYO, and RV. For real-time CMR, the accuracy of nnU-Net exceeds that of comDL overall. For real-time CMR at rest, nnU-Net achieves Dice's coefficients of 0.94 for LV, 0.89 for MYO, and 0.90 for RV and the mean absolute differences between nnU-Net and the reference are 2.9 mL for EDV, 3.5 mL for ESV, and 2.6% for EF. For real-time CMR under exercise stress, nnU-Net achieves Dice's coefficients of 0.92 for LV, 0.85 for MYO, and 0.83 for RV and the mean absolute differences between nnU-Net and reference are 11.4 mL for EDV, 2.9 mL for ESV, and 3.6% for EF. Deep learning methods designed or trained for cine CMR segmentation can perform well on real-time CMR. For real-time free-breathing CMR at rest, the performance of deep learning methods is comparable to inter-observer variability in cine CMR and is usable for fully automatic segmentation. For real-time CMR under exercise stress, the performance of nnU-Net could promise a higher degree of automation in the future.

Project description:BackgroundFree-breathing cardiac cine magnetic resonance imaging (MRI) comparable to the traditional breath-hold 2D segmented cine imaging (SegBH) is clinically required for cardiac function and strain analysis. This study is to assess the feasibility and accuracy of a free-breathing cardiac cine technique (RTCSCineMoCo) combined with highly accelerated real-time acquisition, compressed sensing, and fully automated non-rigid motion correction for left ventricular (LV) function and strain analysis, using SegBH as the reference and comparing with free-breathing single-shot real-time compressed sensing cine imaging (RTCSCine) without motion correction.MethodsA total of 67 patients scheduled for clinical cardiac MRI were included. Cine images were acquired using three techniques (SegBH, RTCSCineMoCo, RTCSCine) consecutively at 3.0 T. LV functional parameters, including ejection fraction (EF), end-diastolic volume (EDV), end-systolic volume (ESV), stroke volumes (SV), and LV mass (LVM) were measured and compared. Strain parameters including global radial (GRS), circumferential (GCS), and longitudinal (GLS) strain as well as corresponding time to peak strain (TPS) were computed by magnetic resonance (MR) feature tracking and compared. Subgroup analyses were performed according to heart rate (HR), left ventricular ejection fraction (LVEF), and etiology.ResultsAll quantitative parameters of LV function and strain measured by RTCSCineMoCo (r≥0.766) and RTCSCine (r≥0.712) showed strong correlations with SegBH (all P<0.001). LV functional parameters were not statistically different between RTCSCineMoCo and SegBH (all P>0.05), but an overestimation of LV end-systolic volume (LVESV) and underestimation of LVEF and LVM were observed using RTCSCine (all P<0.001). GRS, GCS, and GLS by RTCSCineMoCo and RTCSCine were significantly different than those by SegBH (all P<0.05). All TPS values by RTCSCineMoCo showed no significant differences (all P>0.05) compared with SegBH, but TPS in longitudinal directions (TPSL) by RTCSCine was significantly different (P=0.011). There were no significant differences for GRS or GCS between RTCSCineMoCo and SegBH in patients with HR <70 bpm or LVEF <50%. GRS by RTCSCineMoCo showed similar results compared to SegBH in patients with pulmonary hypertension.ConclusionsRTCSCineMoCo is a promising method for robust free-breathing cardiac cine imaging, yielding more precise quantitative analytic results for LV function compared with RTCSCine. RTCSCineMoCo mildly underestimated GRS, GCS, and GLS, but showed smaller bias compared to RTCSCine in LV strain analysis.

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Real-time free-breathing cardiac imaging with self-calibrated through-time radial GRAPPA.

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Real-time free-breathing cardiac imaging with self-calibrated through-time radial GRAPPA.

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