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Cardiac magnetic resonance imaging is crucial for diagnosing cardiovascular diseases, but lengthy postprocessing and manual segmentation can lead to observer bias. Deep learning (DL) has been proposed for automated cardiac segmentation; however, its effectiveness is limited by the slice range selection from base to apex. In this study, we integrated an automated slice range classification step to identify basal to apical short-axis slices before DL-based segmentation. We employed publicly available Multi-Disease, Multi-View & Multi-Center Right Ventricular Segmentation in Cardiac MRI data set with short-axis cine data from 160 training, 40 validation, and 160 testing cases. Three classification and seven segmentation DL models were studied. The top-performing segmentation model was assessed with and without the classification model. Model validation to compare automated and manual segmentation was performed using Dice score and Hausdorff distance and clinical indices (correlation score and Bland-Altman plots). The combined classification (CBAM-integrated 2D-CNN) and segmentation model (2D-UNet with dilated convolution block) demonstrated superior performance, achieving Dice scores of 0.952 for left ventricle (LV), 0.933 for right ventricle (RV), and 0.875 for myocardium, compared to the stand-alone segmentation model (0.949 for LV, 0.925 for RV, and 0.867 for myocardium). Combined classification and segmentation model showed high correlation (0.92-0.99) with manual segmentation for biventricular volumes, ejection fraction, and myocardial mass. The mean absolute difference (2.8-8.3 mL) for clinical parameters between automated and manual segmentation was within the interobserver variability range, indicating comparable performance to manual annotation. Integrating an initial automated slice range classification step into the segmentation process improves the performance of DL-based cardiac chamber segmentation. [ABSTRACT FROM AUTHOR]

The main focus of this work is to introduce a single free-breathing and ungated imaging protocol to jointly estimate cardiac function and myocardial T 1 maps. We reconstruct a time series of images corresponding to k-space data from a free-breathing and ungated inversion recovery gradient echo sequence using a manifold algorithm. We model each image in the time series as a non-linear function of three variables: cardiac and respiratory phases and inversion time. The non-linear function is realized using a convolutional neural networks (CNN) generator, while the CNN parameters, as well as the phase information, are estimated from the measured k-t space data. We use a dense conditional auto-encoder to estimate the cardiac and respiratory phases from the central multi-channel k-space samples acquired at each frame. The latent vectors of the auto-encoder are constrained to be bandlimited functions with appropriate frequency bands, which enables the disentanglement of the latent vectors into cardiac and respiratory phases, even when the data are acquired with intermittent inversion pulses. Once the phases are estimated, we pose the image recovery as the learning of the parameters of the CNN generator from the measured k-t space data. The learned CNN generator is used to generate synthetic data on demand by feeding it with appropriate latent vectors. The proposed approach capitalizes on the synergies between cine MRI and T 1 mapping to reduce the scan time and improve patient comfort. The framework also enables the generation of synthetic breath-held cine movies with different inversion contrasts, which improves the visualization of the myocardium. In addition, the approach also enables the estimation of the T 1 maps with specific phases, which is challenging with breath-held approaches. [ABSTRACT FROM AUTHOR]

Bilinear models such as low-rank and dictionary methods, which decompose dynamic data to spatial and temporal factor matrices are powerful and memory-efficient tools for the recovery of dynamic MRI data. Current bilinear methods rely on sparsity and energy compaction priors on the factor matrices to regularize the recovery. Motivated by deep image prior, we introduce a novel bilinear model, whose factor matrices are generated using convolutional neural networks (CNNs). The CNN parameters, and equivalently the factors, are learned from the undersampled data of the specific subject. Unlike current unrolled deep learning methods that require the storage of all the time frames in the dataset, the proposed approach only requires the storage of the factors or compressed representation; this approach allows the direct use of this scheme to large-scale dynamic applications, including free breathing cardiac MRI considered in this work. To reduce the run time and to improve performance, we initialize the CNN parameters using existing factor methods. We use sparsity regularization of the network parameters to minimize the overfitting of the network to measurement noise. Our experiments on free-breathing and ungated cardiac cine data acquired using a navigated golden-angle gradient-echo radial sequence show the ability of our method to provide reduced spatial blurring as compared to classical bilinear methods as well as a recent unsupervised deep-learning approach. [ABSTRACT FROM AUTHOR]

We introduce a kernel low-rank algorithm to recover free-breathing and ungated dynamic MRI from spiral acquisitions without explicit k-space navigators. It is often challenging for low-rank methods to recover free-breathing and ungated images from undersampled measurements; extensive cardiac and respiratory motion often results in the Casorati matrix not being sufficiently low-rank. Therefore, we exploit the non-linear structure of the dynamic data, which gives the low-rank kernel matrix. Unlike prior work that rely on navigators to estimate the manifold structure, we propose a kernel low-rank matrix completion method to directly fill in the missing k-space data from variable density spiral acquisitions. We validate the proposed scheme using simulated data and in-vivo data. Our results show that the proposed scheme provides improved reconstructions compared to the classical methods such as low-rank and XD-GRASP. The comparison with breath-held cine data shows that the quantitative metrics agree, whereas the image quality is marginally lower. [ABSTRACT FROM AUTHOR]

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 $\textit {in-vivo}$ 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. [ABSTRACT FROM AUTHOR]

Purpose: To develop a continuous‐acquisition cardiac self‐gated spiral pulse sequence and a respiratory motion‐compensated reconstruction strategy for free‐breathing cine imaging. Methods: Cine data were acquired continuously on a 3T scanner for 8 seconds per slice without ECG gating or breath‐holding, using a golden‐angle gradient echo spiral pulse sequence. Cardiac motion information was extracted by applying principal component analysis on the gridded 8 × 8 k‐space center data. Respiratory motion was corrected by rigid registration on each heartbeat. Images were reconstructed using a low‐rank and sparse (L+S) technique. This strategy was evaluated in 37 healthy subjects and 8 subjects undergoing clinical cardiac MR studies. Image quality was scored (1–5 scale) in a blinded fashion by 2 experienced cardiologists. In 13 subjects with whole‐heart coverage, left ventricular ejection fraction (LVEF) from SPiral Acquisition with Respiratory correction and Cardiac Self‐gating (SPARCS) was compared to that from a standard ECG‐gated breath‐hold balanced steady‐state free precession (bSSFP) cine sequence. Results: The self‐gated signal was successfully extracted in all cases and demonstrated close agreement with the acquired ECG signal (mean bias, –0.22 ms). The mean image score across all subjects was 4.0 for reconstruction using the L+S model. There was good agreement between the LVEF derived from SPARCS and the gold‐standard bSSFP technique. Conclusion: SPARCS successfully images cardiac function without the need for ECG gating or breath‐holding. With an 8‐second data acquisition per slice, whole‐heart cine images with clinically acceptable spatial and temporal resolution and image quality can be acquired in <90 seconds of free‐breathing acquisition. [ABSTRACT FROM AUTHOR]

The role of reliable, non-invasive imaging-based recognition of pulmonary hypertension (PH) remains a diagnostic challenge. The aim of the current pilot radiomics study was to assess the diagnostic performance of cardiac MRI (cMRI)-based texture features to accurately predict PH. The study involved IRB-approved retrospective analysis of cMRIs from 72 patients (42 PH and 30 healthy controls) for the primary analysis. A subgroup analysis was performed including patients from the PH group with left ventricle ejection fraction ≥ 50%. Texture features were generated from mid-left ventricle myocardium using balanced steady-state free precession (bSSFP) cine short-axis imaging. Forty-five different combinations of classifier models and feature selection techniques were evaluated. Model performance was assessed using receiver operating characteristic curves. A multilayer perceptron model fitting using full feature sets was the best classifier model for both the primary analysis (AUC 0.862, accuracy 78%) and the subgroup analysis (AUC 0.918, accuracy 80%). Model performance demonstrated considerable variation between the models (AUC 0.523–0.918) based on the chosen model–feature selection combination. Cardiac MRI-based radiomics recognition of PH using texture features is feasible, even with preserved left ventricular ejection fractions. [ABSTRACT FROM AUTHOR]