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Background: Cardiac MRI has become the gold‐standard imaging technique for assessing cardiovascular morphology and function. In spite of this, its slow data acquisition process presents imaging challenges due to the motion from heartbeats, respiration, and blood flow. In recent studies, deep learning (DL) algorithms have shown promising results for the task of image reconstruction. However, there have been instances where they have introduced artifacts that may be misinterpreted as pathologies or may obscure the detection of pathologies. Therefore, it is important to obtain a metric, such as the uncertainty of the network output, that identifies such artifacts. However, this can be quite challenging for large‐scale image reconstruction problems such as dynamic multi‐coil non‐Cartesian MRI. Purpose: To efficiently quantify uncertainties of a physics‐informed DL‐based image reconstruction method for a large‐scale accelerated 2D multi‐coil dynamic radial MRI reconstruction problem, and demonstrate the benefits of physics‐informed DL over model‐agnostic DL in reducing uncertainties while at the same time improving image quality. Methods: We extended a recently proposed physics‐informed 2D U‐Net that learns spatio‐temporal slices (named XT‐YT U‐Net), and employed it for the task of uncertainty quantification (UQ) by using Monte Carlo dropout and a Gaussian negative log‐likelihood loss function. Our data comprised 2D dynamic MR images acquired with a radial balanced steady‐state free precession sequence. The XT‐YT U‐Net, which allows for training with a limited amount of data, was trained and validated on a dataset of 15 healthy volunteers, and further tested on data from four patients. An extensive comparison between physics‐informed and model‐agnostic neural networks (NNs) concerning the obtained image quality and uncertainty estimates was performed. Further, we employed calibration plots to assess the quality of the UQ. Results: The inclusion of the MR‐physics model of data acquisition as a building block in the NN architecture led to higher image quality (NRMSE: −33±8.2%$-33 \pm 8.2 \%$, PSNR: 6.3±1.3%$6.3 \pm 1.3 \%$, and SSIM: 1.9±0.96%$1.9 \pm 0.96 \%$), lower uncertainties (−46±8.7%$-46 \pm 8.7 \%$), and, based on the calibration plots, an improved UQ compared to its model‐agnostic counterpart. Furthermore, the UQ information can be used to differentiate between anatomical structures (e.g., coronary arteries, ventricle boundaries) and artifacts. Conclusions: Using an XT‐YT U‐Net, we were able to quantify uncertainties of a physics‐informed NN for a high‐dimensional and computationally demanding 2D multi‐coil dynamic MR imaging problem. In addition to improving the image quality, embedding the acquisition model in the network architecture decreased the reconstruction uncertainties as well as quantitatively improved the UQ. The UQ provides additional information to assess the performance of different network approaches. [ABSTRACT FROM AUTHOR]

Background: Unrolled neural networks (NNs) have been extensively applied to different image reconstruction problems across all imaging modalities. A key component of the latter is that they allow for physics‐informed learning of the regularization method, which is parametrized by the NN. However, due to the lack of understanding of deep NNs from a theoretical point of view, unrolled NNs are still black‐boxes when the regularizers are given by deep NNs, for example, U‐Nets. Purpose: Dictionarylearning (DL) is a well‐established regularization method, which is based on learning a transform to sparsely approximate the signals of interest. Typically, DL‐based image reconstruction either employs a dictionary, which was pretrained on a set of patches which were extracted from ground‐truth images or a dictionary which is jointly trained during the reconstruction. However, in both cases, the used DL‐algorithms are not designed to take into account the reconstruction problem or the underlying physical model, which describes the imaging process. In this work, we propose a DL‐algorithm based on unrolled NNs to overcome these limitations. Methods: We construct an unrolled NN, which corresponds to an unrolled DL‐based reconstruction algorithm and train the unrolled NN to optimize its weights, that is, the atoms of the dictionary, by back‐propagation in a supervised manner. Further, we propose a new way to employ a 2D dictionary in the spatio‐temporal domain. We tested and evaluated the method on an accelerated cardiac cine MR image reconstruction problem using 216/36/36 dynamic images for training, validation, and testing and compared it to two well‐known state‐of‐the‐art approaches for cardiac cine MRI based on deep iterative CNNs. Further, we analyze the obtained dictionaries in terms of dictionary‐coherence and structure of the atoms. Last, we compare the reported methods in terms of stability by applying them to an entirely different dataset consisting of 49 different test images. Results: The investigated physics‐informed DL‐approach yields significantly more accurate reconstructions compared to the DL‐method, which uses dictionaries obtained by decoupled pretraining, thereby providing an improvement of up to 4.90 dB in terms of PSNR and 5% in terms of SSIM. Further, the proposed spatio‐temporal 2D dictionary outperforms the 1D and 3D dictionaries by preventing smoothing of image details while still accurately removing undersampling artifacts and noise resulting in an increase of up to 1.10 dB in terms of PSNR and 4% in terms of SSIM. Although being surpassed by the CNNs on the first dataset, the proposed NNs‐based DL method is more stable compared to the latter approach and yields comparable results on the second dataset. Last, it has the advantage of being entirely interpretable in each component. Conclusions: The presented physics‐informed NN can be used as training algorithm for a classical and interpretable data‐driven regularization method based on a learned dictionary, which can then not only be linked to the considered data but also to the reconstruction method that the NN defines. [ABSTRACT FROM AUTHOR]

Introduction: Multiple cardiac magnetic resonance (CMR)‐derived indices of atrial fibrillation (AF) substrate have been shown in isolation to predict long‐term outcome following catheter ablation. Left atrial (LA) fibrosis, LA volume, LA ejection fraction (EF), left ventricular ejection fraction (LVEF), LA shape (sphericity) and pulmonary vein anatomy have all been shown to correlate with late AF recurrence. This study aimed to validate and assess the relative contribution of multiple indices in a long‐term single‐center study. Methods and Results: Eighty‐nine patients (53% paroxysmal AF, 73% male) underwent comprehensive CMR study before first‐time AF ablation (median follow‐up 726 days [IQR: 418‐1010 days]). The 3D late gadolinium‐enhanced acquisition (1.5T, 1.3 × 1.3 × 2 mm) was quantified for fibrosis; LA volume and sphericity were assessed on manual segmentation at atrial diastole; LAEF and LVEF were quantified on multislice cine imaging. AF recurred in 43 patients (48%) overall (31 at 1 year). In the recurrence group, LA fibrosis was higher (42% vs 29%; hazard ratio [HR]: 1.032; P = .002), left atrial ejection fraction (LAEF) lower (25% vs 34%; HR: 0.063; P = .016) and LVEF lower (57% vs 63%; HR: 0.011; P = .008). LA volume (135 vs 124 mL) and sphericity (0.819 vs 0.822) were similar. Multivariate Cox regression analysis was adjusted for age and sex (Model 1), additionally AF type (Model 2) and combined (Model 3). In Models 1 and 2, LA fibrosis, LAEF, and LVEF were independently associated with outcome, but only LA fibrosis was independent in Model 3 (HR: 1.021; P = .022). Conclusions: LAEF, LVEF, and LA fibrosis differed significantly in the AF recurrence cohort. However, on combined multivariate analysis only LA fibrosis remained independently associated with outcome. [ABSTRACT FROM AUTHOR]

Focal But Not Diffuse Fibrosis Burden Predicts CRT Remodeling Response Introduction Many heart failure patients with dyssynchrony do not reverse remodel (RR) in response to cardiac resynchronization therapy (CRT). The presence of focal and diffuse interstitial myocardial fibrosis may explain this high nonresponse rate. T1 mapping is a new cardiac magnetic resonance imaging (CMR) technique that overcomes the limitations of conventional contrast CMR and provides reliable quantitative assessment of diffuse myocardial fibrosis. The study tested the hypothesis that focal and diffuse fibrosis quantification would correlate with a lack of left ventricular (LV) RR to CRT. Methods and Results In a prospective study of 48 consecutive patients (27 ischemic cardiomyopathy, 21 dilated cardiomyopathy) LV scar burdens were quantified (scar core and gray zone using late gadolinium enhancement LGE CMR; interstitial fibrosis using T1 mapping) before CRT implant. LV RR was defined by a ≥ 15% reduction in LV end-systolic volume 6 months postimplant. Twenty-seven (56%) patients were responders with RR. Association between scar quantification and LV RR was assessed using the Poisson regression model. Univariate analysis showed that QRS duration/morphology, scar core, and gray zone volumes expressed as % of LV mass and extracellular volume index (ECV) (a measure of interstitial fibrosis from T1 mapping) to be significant predictors of LV RR. Multivariable-adjusted analyses demonstrated scar core quantification (≥13.7% LV mass) to be the only independent predictor of LV RR (prevalence ratio 0.40, P = 0.038). Conclusions Focal scar burden detected by LGE CMR is associated with a poor response to CRT. Diffuse interstitial fibrosis assessment by T1 mapping, however, is not independently predictive of CRT response. [ABSTRACT FROM AUTHOR]

This paper presents a novel, magnetic resonance imaging (MRI)-compatible, force sensor suitable for cardiac catheterization procedures. The miniature, fiber-optic sensor is integrated with the tip of a catheter to allow the detection of interaction forces with the cardiac walls. The optical fiber light intensity is modulated when a force acting at the catheter tip deforms an elastic element, which, in turn, varies the distance between a reflector and the optical fiber. The tip sensor has an external diameter of 9 Fr (3 mm) and can be used during cardiac catheterization procedures. The sensor is able to measure forces in the range of 0–0.85 N, with relatively small hysteresis.  A nonlinear method for calibration is used and real-time MRI in vivo experiments are carried out, to prove the feasibility of this low-cost sensor, enabling the detection of catheter-tip contact forces under dynamic conditions. [ABSTRACT FROM PUBLISHER]

Failure rate for left ventricular (LV) lead implantation in cardiac resynchronization therapy (CRT) is up to 12%. The use of segmentation tools, advanced image registration software, and high-fidelity images from computerized tomography (CT) and cardiac magnetic resonance (CMR) of the coronary sinus (CS) can guide LV lead implantation. We evaluated the feasibility of advanced image registration onto live fluoroscopic images to allow successful LV lead placement. Twelve patients (11 male, 59 ± 16.8 years) undergoing CRT had three-dimensional (3D) whole-heart imaging (six CT, six CMR). Eight patients had at least one previously failed LV lead implant. Using segmentation software, anatomical models of the cardiac chambers, CS, and its branches were overlaid onto the live fluoroscopy using a prototype version of the Philips EP Navigator software to guide lead implantation. We achieved high-fidelity segmentations of cardiac chambers, coronary vein anatomy, and accurate registration between the 3D anatomical models and the live fluoroscopy in all 12 patients confirmed by balloon occlusion angiography. The CS was cannulated successfully in every patient and in 11, an LV lead was implanted successfully. (One patient had no acceptable lead values due to extensive myocardial scar.) Using overlaid 3D segmentations of the CS and cardiac chambers, it is feasible to guide CRT implantation in real time by fusing advanced imaging and fluoroscopy. This enabled successful CRT in a group of patients with previously failed implants. This technology has the potential to facilitate CRT and improve implant success. (PACE 2011; 34:226-234) [ABSTRACT FROM AUTHOR]

Abstract: Background: To compare the ventricular response to dobutamine stress between adult patients with chronic pulmonary regurgitation (PR) after repair of tetralogy of Fallot (r-TOF) and healthy volunteers using a staged dobutamine stress MR (DS-MR) protocol. Methods: Eighteen r-TOF patients (median age 31.9years, range 16.2–60.1) with severe PR and 10 healthy controls (median age 40.6years, range 23.9–51.8) completed staged DS-MR (baseline, 10 and 20μg/kg/min) with ventricular volumetry and pulmonary flow quantification. Comparative analysis involved 3-way ANOVA, t-test, regression analysis, and coefficient of variance. Results: All controls had significant increase of ejection fraction (EF) at each stress level for both ventricles (normal contractile reserve, all p<0.05). In r-TOF patients (RV-EDV 126±27ml/m2, RV-EF 55±7%, LV-EF 58±6%, PR-fraction 43±15%), low-dose DS-MR at 10μg/kg/min demonstrated normal biventricular contractile reserve as seen in volunteers. On increase from 10 to 20μg/kg/min a subgroup showed worsening ejection fraction (n=8, p<0.05), mainly due to lack of reduction or even increase of RV-ESV, while the remainder responded with further reduction of RV-ESV and RV-EDV (n=10, p<0.05) and a non-significant trend to increased EF. This different response could not be predicted at baseline. Conclusions: In r-TOF patients with chronic PR, DS-MR at 10μg/kg/min showed normal biventricular systolic response compared with controls. Increase to 20μg/kg/min provoked abnormal RV-ESV response in some r-TOF patients, suggesting presence of ventricular systolic dysfunction not evident at rest. [Copyright &y& Elsevier]