Cardiac Cine MRI Research Articles

A Supervised Image Registration Approach for Late Gadolinium Enhanced MRI and Cine Cardiac MRI Using Convolutional Neural Networks.

Late gadolinium enhanced (LGE) cardiac magnetic resonance (CMR) imaging is the current gold standard for assessing myocardium viability for patients diagnosed with myocardial infarction, myocarditis or cardiomyopathy. This imaging method enables the identification and quantification of myocardial tissue regions that appear hyper-enhanced. However, the delineation of the myocardium is hampered by the reduced contrast between the myocardium and the left ventricle (LV) blood-pool due to the gadolinium-based contrast agent. The balanced-Steady State Free Precession (bSSFP) cine CMR imaging provides high resolution images with superior contrast between the myocardium and the LV blood-pool. Hence, the registration of the LGE CMR images and the bSSFP cine CMR images is a vital step for accurate localization and quantification of the compromised myocardial tissue. Here, we propose a Spatial Transformer Network (STN) inspired convolutional neural network (CNN) architecture to perform supervised registration of bSSFP cine CMR and LGE CMR images. We evaluate our proposed method on the 2019 Multi-Sequence Cardiac Magnetic Resonance Segmentation Challenge (MS-CMRSeg) dataset and use several evaluation metrics, including the center-to-center LV and right ventricle (RV) blood-pool distance, and the contour-to-contour blood-pool and myocardium distance between the LGE and bSSFP CMR images. Specifically, we showed that our registration method reduced the bSSFP to LGE LV blood-pool center distance from 3.28mm before registration to 2.27mm post registration and RV blood-pool center distance from 4.35mm before registration to 2.52mm post registration. We also show that the average surface distance (ASD) between bSSFP and LGE is reduced from 2.53mm to 2.09mm, 1.78mm to 1.40mm and 2.42mm to 1.73mm for LV blood-pool, LV myocardium and RV blood-pool, respectively.

Efficient Segmentation Pipeline Using Diffeomorphic Image Registration: A Validation Study.

Functional measurements of the left atrium (LA) in atrial fibrillation (AF) patients is limited to a single CINE slice midway through the LA. Nonetheless, a full 3D characterization of atrial functional measurements would provide more insights into LA function. But this improved modeling capacity comes at a price of requiring LA segmentation of each 3D time point,a time-consuming and expensive task that requires anatomy-specific expertise.We propose an efficient pipeline which requires ground truth segmentation of a single (or limited) CINE time point to accurately propagate it throughout the sequence. This method significantly saves human effort and enable better characterization of LA anatomy. From a gated cardiac CINE MRI sequence we select a single CINE time point with ground truth segmentation, and assuming cyclic motion, we register other images corresponding to all time points using diffeomorphic registration in ANTs. The diffeomorphic registration fields allow us to map a given anatomical shape (segmentation) to each CINE time point, facilitating the construction of a 4D shape model.

Respiratory motion-corrected, compressively sampled dynamic MR image reconstruction by exploiting multiple sparsity constraints and phase correlation-based data binning.

Cardiac magnetic resonance imaging (cMRI) is a standard method that is clinically used to evaluate the function of the human heart. Respiratory motion during a cMRI scan causes blurring artefacts in the reconstructed images. In conventional MRI, breath holding is used to avoid respiratory motion artefacts, which may be difficult for cardiac patients. This paper proposes a method in which phase correlation-based binning, followed by image registration-based sparsity along with spatio-temporal sparsity, is incorporated into the standard low rank + sparse (L+S) reconstruction for free-breathing cardiac cine MRI. The proposed method is validated on clinical data and simulated free-breathing cardiac cine data for different acceleration factors (AFs). The reconstructed images are analysed using visual assessment, artefact power (AP) and root-mean-square error (RMSE). The results of the proposed method are compared with the contemporary motion-corrected compressed sensing (MC-CS) method given in the literature. Our results show that the proposed method successfully reconstructs the motion-corrected images from respiratory motion-corrupted, compressively sampled cardiac cine MR data, e.g., there is 26% and 24% improvement in terms of AP and RMSE values, respectively, at AF = 4 and 20% and 16.04% improvement in terms of AP and RMSE values, respectively, at AF = 8 in the reconstruction results from the proposed method for the cardiac phantom cine data. The proposed method achieves significant improvement in the AP and RMSE values at different AFs for both the phantom and in vivo data.

Fusion of 3D real-time echocardiography and cine MRI using a saliency analysis.

This paper presents a novel 3D multimodal registration strategy to fuse 3D real-time echocardiography images with cardiac cine MRI images. This alignment is performed in a saliency space, which is designed to maximize similarity between the two imaging modalities. This fusion improves the quality of the available information. The method performs in two steps: temporal and spatial registrations. A temporal alignment is firstly achieved by nonlinearly matching pairs of correspondences between the two modalities using a dynamic time warping. A temporal registration is then carried out by applying nonrigid transformations in a common saliency space where normalized cross correlation between temporal pairs of salient volumes is maximized. The alignment performance was evaluated with a set of 18 subjects, 3 with cardiomyopathies and 15 healthy, by computing the Dice score and Hausdorff distance with respect to manual delineations of the left ventricle cavity in both modalities. A Dice score and Hausdorff distance of [Formula: see text] and [Formula: see text], respectively, were obtained. In addition, the deformation field was estimated by quantifying its foldings, obtaining a 98% of regularity in the deformation field. The 3D multimodal registration strategy presented is performed in a saliency space. Unlike state-of-the-art methods, the presented one takes advantage of the temporal information of the heart to construct this common space, ending up with two well-aligned modalities and regular deformation fields. This preliminary study was evaluated on heterogeneous data composed of two different datasets, healthy and pathological cases, showing similar performances in both cases. Future work will focus on testing the presented strategy in a larger dataset with a balanced number of classes.

A distance map regularized CNN for cardiac cine MR image segmentation.

Cardiac image segmentation is a critical process for generating personalized models of the heart and for quantifying cardiac performance parameters. Fully automatic segmentation of the left ventricle (LV), the right ventricle (RV), and the myocardium from cardiac cine MR images is challenging due to variability of the normal and abnormal anatomy, as well as the imaging protocols. This study proposes a multi-task learning (MTL)-based regularization of a convolutional neural network (CNN) to obtain accurate segmenation of the cardiac structures from cine MR images. We train a CNN network to perform the main task of semantic segmentation, along with the simultaneous, auxiliary task of pixel-wise distance map regression. The network also predicts uncertainties associated with both tasks, such that their losses are weighted by the inverse of their corresponding uncertainties. As a result, during training, the task featuring a higher uncertainty is weighted less and vice versa. The proposed distance map regularizer is a decoder network added to the bottleneck layer of an existing CNN architecture, facilitating the network to learn robust global features. The regularizer block is removed after training, so that the original number of network parameters does not change. The trained network outputs per-pixel segmentation when a new patient cine MR image is provided as an input. We show that the proposed regularization method improves both binary and multi-class segmentation performance over the corresponding state-of-the-art CNN architectures. The evaluation was conducted on two publicly available cardiac cine MRI datasets, yielding average Dice coefficients of 0.84±0.03 and 0.91±0.04. We also demonstrate improved generalization performance of the distance map regularized network on cross-dataset segmentation, showing as much as 42% improvement in myocardium Dice coefficient from 0.56±0.28 to 0.80±0.14. We have presented a method for accurate segmentation of cardiac structures from cine MR images. Our experiments verify that the proposed method exceeds the segmentation performance of three existing state-of-the-art methods. Furthermore, several cardiac indices that often serve as diagnostic biomarkers, specifically blood pool volume, myocardial mass, and ejection fraction, computed using our method are better correlated with the indices computed from the reference, ground truth segmentation. Hence, the proposed method has the potential to become a non-invasive screening and diagnostic tool for the clinical assessment of various cardiac conditions, as well as a reliable aid for generating patient specific models of the cardiac anatomy for therapy planning, simulation, and guidance.

Progress of Right Ventricle Segmentation from Short-Axis Images Acquired with Cardiac Cine MRI

Progress of Right Ventricle Segmentation from Short-Axis Images Acquired with Cardiac Cine MRI

Spatio-Temporal Deep Learning-Based Undersampling Artefact Reduction for 2D Radial Cine MRI With Limited Training Data.

In this work we reduce undersampling artefacts in two-dimensional (2D) golden-angle radial cine cardiac MRI by applying a modified version of the U-net. The network is trained on 2D spatio-temporal slices which are previously extracted from the image sequences. We compare our approach to two 2D and a 3D deep learning-based post processing methods, three iterative reconstruction methods and two recently proposed methods for dynamic cardiac MRI based on 2D and 3D cascaded networks. Our method outperforms the 2D spatially trained U-net and the 2D spatio-temporal U-net. Compared to the 3D spatio-temporal U-net, our method delivers comparable results, but requiring shorter training times and less training data. Compared to the compressed sensing-based methods kt-FOCUSS and a total variation regularized reconstruction approach, our method improves image quality with respect to all reported metrics. Further, it achieves competitive results when compared to the iterative reconstruction method based on adaptive regularization with dictionary learning and total variation and when compared to the methods based on cascaded networks, while only requiring a small fraction of the computational and training time. A persistent homology analysis demonstrates that the data manifold of the spatio-temporal domain has a lower complexity than the one of the spatial domain and therefore, the learning of a projection-like mapping is facilitated. Even when trained on only one single subject without data-augmentation, our approach yields results which are similar to the ones obtained on a large training dataset. This makes the method particularly suitable for training a network on limited training data. Finally, in contrast to the spatial 2D U-net, our proposed method is shown to be naturally robust with respect to image rotation in image space and almost achieves rotation-equivariance where neither data-augmentation nor a particular network design are required.

Dynamic cardiac MRI reconstruction using motion aligned locally low rank tensor (MALLRT)

Various sparse transform models have been explored for compressed sensing-based dynamic cardiac MRI reconstruction from vastly under-sampled k-space data. Recently emerged low rank tensor model using Tucker decomposition could be viewed as a special form of sparse model, where the core tensor, which is obtained using high-order singular value decomposition, is sparse in the sense that only a few elements have dominantly large magnitude. However, local details tend to be over-smoothed when the entire image is conventionally modeled as a global tensor. Moreover, low rankness is sensitive to motion as spatiotemporal correlation is corrupted by spatial misalignment between temporal frames. To overcome these limitations, this paper presents a novel motion aligned locally low rank tensor (MALLRT) model for dynamic MRI reconstruction. In MALLRT, low rank constraint is enforced on image patch-based local tensors, which correspond to overlapping blocks extracted from the reconstructed high-dimensional image after group-wise inter-frame motion registration. For solving the proposed model, this paper presents an efficient optimization algorithm by using variable splitting and alternating direction method of multipliers (ADMM). MALLRT demonstrated promising performance as validated on one cardiac perfusion MRI dataset and two cardiac cine MRI datasets using retrospective under-sampling with various acceleration factors, as well as one prospectively under-sampled cardiac perfusion MRI dataset. Compared to four state-of-the-art methods, MALLRT achieved substantially better image reconstruction quality in terms of both signal to error ratio (SER) and structural similarity index (SSIM) metrics, and visual perception in preserving spatial details and capturing temporal variations.

Functional LGE Imaging: Cardiac Phase-Resolved Assessment of Focal Fibrosis.

Cardiac Magnetic Resonance Imaging (CMR) is a central tool for diagnosis of various ischemic and non-ischemic cardiomyopathies. CMR protocols commonly comprise assessment of functional properties using cardiac phase-resolved CINE MRI and characterization of myocardial viability using late gadolinium enhancement (LGE) imaging. Conventional LGE imaging requires inversion recovery preparation with a specific inversion time to null the healthy myocardium, which restricts the acquisition to a single cardiac phase. In turn, this necessitates separate scans for cardiac function and viability. In this work, we develop a new method for functional LGE imaging in a single breath-hold using a three-step approach: 1) ECG-triggered multi-contrast data is acquired for each cardiac phase, 2) semi-quantitative relaxation maps are generated, 3) LGE imaging contrast is synthesized based on the semi-quantitative maps. The proposed functional LGE method is evaluated in four healthy subject and 20 patients at 1.5T and 3T. Thorough suppression of the healthy myocardium, as well as 40-80ms temporal resolution are achieved, with no visually apparent temporal blurring at tissue interfaces. Functional LGE in patients with focal scar demonstrates robust hyperenhancement in the scar area throughout all cardiac phases, allowing for visual assessment of scar motility. The proposed technique bears the potential to simplify and speedup common cardiac imaging protocols, while enabling improved data fusion of functional and viability information for improved evaluation of CMR.

Free-breathing fetal cardiac MRI with doppler ultrasound gating, compressed sensing, and motion compensation.

BackgroundFetal cardiovascular MRI complements ultrasound to assess fetal cardiovascular pathophysiology.PurposeTo develop a free‐breathing method for retrospective fetal cine MRI using Doppler ultrasound (DUS) cardiac gating and tiny golden angle radial sampling (tyGRASP) for accelerated acquisition capable of detecting fetal movements for motion compensation.Study TypeFeasibility study.SubjectsNine volunteers (gestational week 34–40). Short‐axis and four‐chamber views were acquired during maternal free‐breathing and breath‐hold.Field Strength/Sequence1.5T cine balanced steady‐state free precession.AssessmentA self‐gated reconstruction method was improved for clinical application by using 1) retrospective DUS gating, and 2) motion detection and rejection/correction algorithms for compensating for fetal motion. The free‐breathing reconstructions were qualitatively and quantitatively assessed, and DUS‐gating was compared with self‐gating in breath‐hold reconstructions. A scoring of 1–4 for overall image quality, cardiac, and extracardiac diagnostic quality was used.Statistical TestsFriedman's test was used to assess differences in qualitative scoring between observers. A Wilcoxon matched‐pairs signed rank test was used to assess differences between breath‐hold and free‐breathing acquisitions and between observers' quantitative measurements.ResultsIn all cases, 111 free‐breathing and 145 breath‐hold acquisitions, the automatically calculated DUS‐based cardiac gating signal provided reconstructions of diagnostic quality (median score 4, range 1–4). Free‐breathing did not affect the DUS‐based cardiac gated retrospective radial reconstruction with respect to image or diagnostic quality (all P > 0.06). Motion detection with rejection/correction in k‐space produced high‐quality free‐breathing DUS‐based reconstructions [median 3, range (2–4)], whereas free‐breathing self‐gated methods failed in 80 out of 88 cases to produce a stable gating signal.Data ConclusionFree‐breathing fetal cine cardiac MRI based on DUS gating and tyGRASP with motion compensation yields diagnostic images. This simplifies acquisition for the pregnant woman and thus could help increase fetal cardiac MRI acceptance in the clinic. Level of Evidence: 2 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:260–272.

Free-Breathing & Ungated Cardiac MRI Using Iterative SToRM (i-SToRM).

We introduce a local manifold regularization approach to recover dynamic MRI data from highly undersampled measurements. The proposed scheme relies on the manifold structure of local image patches at the same spatial location in a free-breathing cardiac MRI dataset; this approach is a generalization of the SmooThness Regularization on Manifolds (SToRM) scheme that exploits the global manifold structure of images in the dataset. Since the manifold structure of the patches varies depending on the spatial location and is often considerably simpler than the global one, this approach significantly reduces the data demand, facilitating the recovery from shorter scans. Since the navigator-based estimation of manifold structure pursued in SToRM is not feasible in this setting, a reformulation of SToRM is introduced. Specifically, the regularization term of the cost function involves the sum of robust distances between images sub-patches in the dataset. The optimization algorithm alternates between updating the images and estimating the manifold structure of the image patches. The utility of the proposed scheme is demonstrated in the context of in-vivo prospective free-breathing cardiac CINE MRI imaging with multichannel acquisitions and simulated phantoms. The new framework facilitates a reduction in scan time, as compared to the SToRM strategy.

Space-time variant weighted regularization in compressed sensing cardiac cine MRI

PurposeTo analyze the impact on image quality and motion fidelity of a motion-weighted space-time variant regularization term in compressed sensing cardiac cine MRI. Methodsk-t SPARSE-SENSE with temporal total variation (tTV) is used as the base reconstruction algorithm. Motion in the dynamic image is estimated by means of a robust registration technique for non-rigid motion. The resulting deformation fields are used to leverage the regularization term. The results are compared with standard k-t SPARSE-SENSE with tTV regularization as well as with an improved version of this algorithm that makes use of tTV and temporal Fast Fourier Transform regularization in x-f domain. ResultsThe proposed method with space-time variant regularization provides higher motion fidelity and image quality than the two previously reported methods. Difference images between undersampled reconstruction and fully sampled reference images show less systematic errors with the proposed approach. ConclusionsUsage of a space-time variant regularization offers reconstructions with better image quality than the state of the art approaches used for comparison.

A deep learning framework for unsupervised affine and deformable image registration.

Image registration, the process of aligning two or more images, is the core technique of many (semi-)automatic medical image analysis tasks. Recent studies have shown that deep learning methods, notably convolutional neural networks (ConvNets), can be used for image registration. Thus far training of ConvNets for registration was supervised using predefined example registrations. However, obtaining example registrations is not trivial. To circumvent the need for predefined examples, and thereby to increase convenience of training ConvNets for image registration, we propose the Deep Learning Image Registration (DLIR) framework for unsupervised affine and deformable image registration. In the DLIR framework ConvNets are trained for image registration by exploiting image similarity analogous to conventional intensity-based image registration. After a ConvNet has been trained with the DLIR framework, it can be used to register pairs of unseen images in one shot. We propose flexible ConvNets designs for affine image registration and for deformable image registration. By stacking multiple of these ConvNets into a larger architecture, we are able to perform coarse-to-fine image registration. We show for registration of cardiac cine MRI and registration of chest CT that performance of the DLIR framework is comparable to conventional image registration while being several orders of magnitude faster.

Circumferential Strain Predicts Major Adverse Cardiovascular Events Following an Acute ST-Segment-Elevation Myocardial Infarction.

Purpose To investigate the prognostic value of circumferential left ventricular (LV) strain measured by using cardiac MRI for prediction of major adverse cardiac events (MACE) following an acute ST-segment-elevation myocardial infarction (STEMI). Materials and Methods Participants with acute STEMI were prospectively enrolled from May 11, 2011, to November 22, 2012. Cardiac MRI was performed at 1.5 T during the index hospitalization. Displacement encoding with stimulated echoes (DENSE) and feature tracking of cine cardiac MRI was used to assess circumferential LV strain. MACE that occurred after discharge were independently assessed by cardiologists blinded to the baseline observations. Results A total of 259 participants (mean age, 58 years ± 11 [standard deviation]; 198 men [mean age, 58 years ± 11] and 61 women [mean age, 58 years ± 12]) underwent cardiac MRI 2.2 days ± 1.9 after STEMI. Average infarct size was 18% ± 13 of LV mass and circumferential strain was -13% ± 3 (DENSE method) and -24% ± 7 (feature- tracking method). Fifty-one percent (131 of 259 participants) had presence of microvascular obstruction. During a median follow-up period of 4 years, 8% (21 of 259) experienced MACE. Area under the curve (AUC) for DENSE was different from that of feature tracking (AUC, 0.76 vs 0.62; P = .03). AUC for DENSE was similar to that of initial infarct size (P = .06) and extent of microvascular obstruction (P = .08). DENSE-derived strain provided incremental prognostic benefit over infarct size for prediction of MACE (hazard ratio, 1.3; P < .01). Conclusion Circumferential strain has independent prognostic importance in study participants with acute ST-segment-elevation myocardial infarction. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Kramer in this issue.

Evaluation of a Semi-automatic Right Ventricle Segmentation Method on Short-Axis MR Images

The purpose of this study was to evaluate a semi-automatic right ventricle segmentation method on short-axis cardiac cine MR images which segment all right ventricle contours in a cardiac phase using one seed contour. Twenty-eight consecutive short-axis, four-chamber, and tricuspid valve view cardiac cine MRI examinations of healthy volunteers were used. Two independent observers performed the manual and automatic segmentations of the right ventricles. Analyses were based on the ventricular volume and ejection fraction of the right heart chamber. Reproducibility of the manual and semi-automatic segmentations was assessed using intra- and inter-observer variability. Validity of the semi-automatic segmentations was analyzed with reference to the manual segmentations. The inter- and intra-observer variability of manual segmentations were between 0.8 and 3.2%. The semi-automatic segmentations were highly correlated with the manual segmentations (R2 0.79–0.98), with median difference of 0.9–4.8% and of 3.3% for volume and ejection fraction parameters, respectively. In comparison to the manual segmentation, the semi-automatic segmentation produced contours with median dice metrics of 0.95 and 0.87 and median Hausdorff distance of 5.05 and 7.35 mm for contours at end-diastolic and end-systolic phases, respectively. The inter- and intra-observer variability of the semi-automatic segmentations were lower than observed in the manual segmentations. Both manual and semi-automatic segmentations performed better at the end-diastolic phase than at the end-systolic phase. The investigated semi-automatic segmentation method managed to produce a valid and reproducible alternative to manual right ventricle segmentation.

Fully automated segmentation of the left ventricle in cine cardiac MRI using neural network regression.

Left ventricle (LV) structure and functions are the primary assessment performed in most clinical cardiac MRI protocols. Fully automated LV segmentation might improve the efficiency and reproducibility of clinical assessment. To develop and validate a fully automated neural network regression-based algorithm for segmentation of the LV in cardiac MRI, with full coverage from apex to base across all cardiac phases, utilizing both short axis (SA) and long axis (LA) scans. Cross-sectional survey; diagnostic accuracy. In all, 200 subjects with coronary artery diseases and regional wall motion abnormalities from the public 2011 Left Ventricle Segmentation Challenge (LVSC) database; 1140 subjects with a mix of normal and abnormal cardiac functions from the public Kaggle Second Annual Data Science Bowl database. 1.5T, steady-state free precession. Reference standard data generated by experienced cardiac radiologists. Quantitative measurement and comparison via Jaccard and Dice index, modified Hausdorff distance (MHD), and blood volume. Paired t-tests compared to previous work. Tested against the LVSC database, we obtained 0.77 ± 0.11 (Jaccard index) and 1.33 ± 0.71 mm (MHD), both metrics demonstrating statistically significant improvement (P < 0.001) compared to previous work. Tested against the Kaggle database, the signed difference in evaluated blood volume was +7.2 ± 13.0 mL and -19.8 ± 18.8 mL for the end-systolic (ES) and end-diastolic (ED) phases, respectively, with a statistically significant improvement (P < 0.001) for the ED phase. A fully automated LV segmentation algorithm was developed and validated against a diverse set of cardiac cine MRI data sourced from multiple imaging centers and scanner types. The strong performance overall is suggestive of practical clinical utility. 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Nonrigid motion compensation in compressed sensing reconstruction of cardiac cine MRI

In this work, a robust nonrigid motion compensation approach, is applied to the compressed sensing reconstruction of dynamic cardiac cine MRI sequences. Respiratory and cardiac motion separation coupled with a registration algorithm is used to provide accurate reconstruction of dynamic cardiac images. The proposed scheme employs a variable splitting based optimization strategy to enable joint motion estimation along with reconstruction. We define the recovery as an energy minimization scheme utilizing an objective function that combines data consistency, spatial smoothness, and motion penalties. The validation of the proposed algorithm using numerical phantom and in-vivo cine MRI data demonstrates reconstruction of cardiac MRI data with less spatio-temporal blurring and motion artifacts from extensively under-sampled data. The proposed method is observed to provide improved reconstructions over state-of-the-art motion compensation schemes.

Isotropic 3D cardiac cine MRI allows efficient sparse segmentation strategies based on 3D surface reconstruction

Segmentation of cardiac cine MRI data is routinely used for the volumetric analysis of cardiac function. Conventionally, 2D contours are drawn on short-axis (SAX) image stacks with relatively thick slices (typically 8 mm). Here, an acquisition/reconstruction strategy is used for obtaining isotropic 3D cine datasets; reformatted slices are then used to optimize the manual segmentation workflow. Isotropic 3D cine datasets were obtained from multiple 2D cine stacks (acquired during free-breathing in SAX and long-axis (LAX) orientations) using nonrigid motion correction (cine-GRICS method) and super-resolution. Several manual segmentation strategies were then compared, including conventional SAX segmentation, LAX segmentation in three views only, and combinations of SAX and LAX slices. An implicit B-spline surface reconstruction algorithm is proposed to reconstruct the left ventricular cavity surface from the sparse set of 2D contours. All tested sparse segmentation strategies were in good agreement, with Dice scores above 0.9 despite using fewer slices (3-6 sparse slices instead of 8-10 contiguous SAX slices). When compared to independent phase-contrast flow measurements, stroke volumes computed from four or six sparse slices had slightly higher precision than conventional SAX segmentation (error standard deviation of 5.4 mL against 6.1 mL) at the cost of slightly lower accuracy (bias of -1.2 mL against 0.2 mL). Functional parameters also showed a trend to improved precision, including end-diastolic volumes, end-systolic volumes, and ejection fractions). The postprocessing workflow of 3D isotropic cardiac imaging strategies can be optimized using sparse segmentation and 3D surface reconstruction. Magn Reson Med 79:2665-2675, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Validation of highly accelerated real-time cardiac cine MRI with radial k-space sampling and compressed sensing in patients at 1.5T and 3T.

To validate an optimal 12-fold accelerated real-time cine MRI pulse sequence with radial k-space sampling and compressed sensing (CS) in patients at 1.5T and 3T. We used two strategies to reduce image artifacts arising from gradient delays and eddy currents in radial k-space sampling with balanced steady-state free precession readout. We validated this pulse sequence against a standard breath-hold cine sequence in two patient cohorts: a myocardial infarction (n = 16) group at 1.5T and chronic kidney disease group (n = 18) at 3T. Two readers independently performed visual analysis of 68 cine sets in four categories (myocardial definition, temporal fidelity, artifact, noise) on a 5-point Likert scale (1 = nondiagnostic, 2 = poor, 3 = adequate or moderate, 4 = good, 5 = excellent). Another reader calculated left ventricular (LV) functional parameters, including ejection fraction. Compared with standard cine, real-time cine produced nonsignificantly different visually assessed scores, except for the following categories: 1) temporal fidelity scores were significantly lower (P = 0.013) for real-time cine at both field strengths, 2) artifacts scores were significantly higher (P = 0.013) for real-time cine at both field strengths, and 3) noise scores were significantly (P = 0.013) higher for real-time cine at 1.5T. Standard and real-time cine pulse sequences produced LV functional parameters that were in good agreement (e.g., absolute mean difference in ejection fraction <4%). This study demonstrates that an optimal 12-fold, accelerated, real-time cine MRI pulse sequence using radial k-space sampling and CS produces good to excellent visual scores and relatively accurate LV functional parameters in patients at 1.5T and 3T. Magn Reson Med 79:2745-2751, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Chronological Change of Right Ventricle by Chronic Intermittent Hypoxia in Mice.

No studies have investigated sequential changes in the heart on magnetic resonance imaging (MRI), along with observation of functional lung phenotypes and genetics, over the duration of chronic intermittent hypoxia (CIH). We investigated chronological changes in heart and lung phenotypes after CIH using a mouse model to provide new insights into the pathophysiology of sleep apnea-induced cardiovascular disease. C57BL/6J adult male mice were randomized to 4 or 8 weeks of CIH. Cardiac cine-MRI images were analyzed to assess functional parameters of right ventricle (RV). Histopathological features of myocytes and pulmonary vessels, as well as genes involved in the endothelin (ET) system, were investigated. Function of the RV reduced significantly at 4 weeks and continuously decreased following another 4 weeks of CIH, although the rate of decrease was attenuated. Notably, persistence of reduced ejection fraction and end-systole RV wall thickness (WT) and increases in the ET system of the lungs and blood strongly implied the development of pulmonary hypertension after 8 weeks of CIH. RV dysfunction with reduced end-systole RV WT could be a late phenotype in long-standing CIH and possibly also in obstructive sleep apnea.