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Background: Assessment of regional myocardial function at native pixel-level resolution can play a crucial role in recognizing the early signs of the decline in regional myocardial function. Extensive data processing in existing techniques limits the effective resolution and accuracy of the generated strain maps. The purpose of this study is to compute myocardial principal strain maps ε p1 and ε p2 from tagged MRI (tMRI) at the native image resolution using deep-learning local patch convolutional neural network (CNN) models (DeepStrain)., Methods: For network training, validation, and testing, realistic tMRI datasets were generated and consisted of 53,606 cine images simulating the heart, the liver, blood pool, and backgrounds, including ranges of shapes, positions, motion patterns, noise, and strain. In addition, 102 in-vivo image datasets from three healthy subjects, and three Pulmonary Arterial Hypertension patients, were acquired and used to assess the network's in-vivo performance. Four convolutional neural networks were trained for mapping input tagging patterns to corresponding ground-truth principal strains using different cost functions. Strain maps using harmonic phase analysis (HARP) were obtained with various spectral filtering settings for comparison. CNN and HARP strain maps were compared at the pixel level versus the ground-truth and versus the least-loss in-vivo maps using Pearson correlation coefficients (R) and the median error and Inter-Quartile Range (IQR) histograms., Results: CNN-based local patch DeepStrain maps at a phantom resolution of 1.1mm × 1.1 mm and in-vivo resolution of 2.1mm × 1.6 mm were artifact-free with multiple fold improvement with ε p1 ground-truth median error of 0.009(0.007) vs. 0.32(0.385) using HARP and ε p2 ground-truth error of 0.016(0.021) vs. 0.181(0.08) using HARP. CNN-based strain maps showed substantially higher agreement with the ground-truth maps with correlation coefficients R > 0.91 for ε p1 and ε p2 compared to R < 0.21 and R < 0.82 for HARP-generated maps, respectively., Conclusion: CNN-generated Eulerian strain mapping permits artifact-free visualization of myocardial function at the native image resolution., (Copyright © 2021. Published by Elsevier Ltd.)

Regional soft tissue mechanical strain offers crucial insights into tissue's mechanical function and vital indicators for different related disorders. Tagging magnetic resonance imaging (tMRI) has been the standard method for assessing the mechanical characteristics of organs such as the heart, the liver, and the brain. However, constructing accurate artifact-free pixelwise strain maps at the native resolution of the tagged images has for decades been a challenging unsolved task. In this work, we developed an end-to-end deep-learning framework for pixel-to-pixel mapping of the two-dimensional Eulerian principal strains [Formula: see text] and [Formula: see text] directly from 1-1 spatial modulation of magnetization (SPAMM) tMRI at native image resolution using convolutional neural network (CNN). Four different deep learning conditional generative adversarial network (cGAN) approaches were examined. Validations were performed using Monte Carlo computational model simulations, and in-vivo datasets, and compared to the harmonic phase (HARP) method, a conventional and validated method for tMRI analysis, with six different filter settings. Principal strain maps of Monte Carlo tMRI simulations with various anatomical, functional, and imaging parameters demonstrate artifact-free solid agreements with the corresponding ground-truth maps. Correlations with the ground-truth strain maps were R = 0.90 and 0.92 for the best-proposed cGAN approach compared to R = 0.12 and 0.73 for the best HARP method for [Formula: see text] and [Formula: see text], respectively. The proposed cGAN approach's error was substantially lower than the error in the best HARP method at all strain ranges. In-vivo results are presented for both healthy subjects and patients with cardiac conditions (Pulmonary Hypertension). Strain maps, obtained directly from their corresponding tagged MR images, depict for the first time anatomical, functional, and temporal details at pixelwise native high resolution with unprecedented clarity. This work demonstrates the feasibility of using the deep learning cGAN for direct myocardial and liver Eulerian strain mapping from tMRI at native image resolution with minimal artifacts., (© 2021. The Author(s).)

Background and Aims: Autosomal dominant hyper-IgE (AD-HIES) is a primary immunodeficiency caused by mutations in STAT3. Elevated levels of IgE, an ineffective immune response, connective tissue abnormalities, and coronary arterial dilation and tortuosity characterize AD-HIES. To date, coronary artery evaluation in AD-HIES patients has been limited to lumenography measurements. Direct in vivo coronary vessel wall (VW) imaging may allow for better interrogation of coronary vessel abnormalities. The goal of this prospective study was to evaluate the coronary VW of AD-HIES patients using Magnetic Resonance Imaging (MRI) and histology. VW image findings were compared in healthy subjects and subjects with coronary atherosclerotic disease (CAD)., Methods: A total of 28 subjects (10 with AD-HIES, 8 healthy, 10 with CAD) were studied by coronary VW MRI imaging. Additionally, a post-mortem coronary artery from one VW imaged AD-HIES patient was examined., Results: Coronary VW in AD-HIES was thicker than in healthy controls but not significantly different from VW thickness in CAD subjects. AD-HIES coronaries showed increased VW area compared to healthy controls and CAD subjects. On histology, the AD-HIES coronary artery had findings consistent with atherosclerotic plaque, but had minimal luminal narrowing, deficient adventitia thickening and absence of both internal and external elastic laminae., Conclusions: This is the first study to demonstrate subclinical coronary atherosclerosis in AD-HIES patients on VW imaging by MRI. Histologic evaluation confirmed the presence of atherosclerosis with lack of supportive adventitial thickening and elastic components. These findings suggest mechanisms for coronary dilation in AD-HIES and thereby help direct clinical management., (Published by Elsevier B.V.)

Objective: This study optimizes use of 3-T magnetic resonance imaging (MRI) to delineate coronary venous anatomy and compares 3-T MRI with multidetector computed tomography (MDCT) measurements., Methods: The study population included 37 consecutive subjects (22 men, 19-71 years old). Whole-heart contrast-enhanced MRI images at 3 T were acquired using segmented k-space gradient echo with inversion recovery prepared technique. The MDCT images were obtained using nonionic iodinated contrast., Results: The coronary sinus and great cardiac, posterior interventricular, and anterior interventricular veins were visualized in 100% of cases by both MRI and MDCT. Detection of the posterior vein of the left ventricle and the left marginal vein by MRI was 97% and 81%, respectively. Bland-Altman plots showed agreement in ostial diameter measured by both modalities with correlation coefficients ranging from 0.5 to 0.76. Vein length and distances also agreed closely., Conclusions: Free-breathing whole-heart 3-dimensional MRI at 3 T provides high-spatial-resolution images and could offer an alternative imaging technique instead of MDCT scans.

Objectives: The purpose of this study was to (a) investigate the image quality of phase-sensitive dual-inversion recovery (PS-DIR) coronary wall imaging in healthy subjects and in subjects with known coronary artery disease (CAD) and to (b) investigate the utilization of PS-DIR at 3T in the assessment of coronary artery thickening in subjects with asymptomatic but variable degrees of CAD., Materials and Methods: A total of 37 subjects participated in this institutional review board-approved and HIPAA-compliant study. These included 21 subjects with known CAD as identified on multidetector computed tomography angiography (MDCT). Sixteen healthy subjects without known history of CAD were included. All subjects were scanned using free-breathing PS-DIR magnetic resonance imaging (MRI) for the assessment of coronary wall thickness at 3T. Lumen-tissue contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR) and quantitative vessel parameters including lumen area and wall thickness were measured. Statistical analyses were performed., Results: PS-DIR was successfully completed in 76% of patients and in 88% of the healthy subjects. Phase-sensitive signed-magnitude reconstruction, compared to modulus-magnitude images, significantly improved lumen-tissue CNR in healthy subjects (26.73±11.95 vs. 14.65±9.57, P<.001) and in patients (21.45±7.61 vs. 16.65±5.85, P<.001). There was no difference in image CNR and SNR between groups. In arterial segments free of plaques, coronary wall was thicker in patients in comparison to healthy subjects (1.74±0.27 mm vs. 1.17±0.14 mm, P<.001), without a change in lumen area (4.51±2.42 mm2 vs. 5.71±3.11mm2, P=.25)., Conclusions: This is the first study to demonstrate the feasibility of successfully obtaining vessel wall images at 3T using PS-DIR in asymptomatic patients with known variable degrees of CAD as detected by MDCT. This was achieved with a fixed subject-invariant planning of blood signal nulling. With that limitation alleviated, PS-DIR coronary wall MRI is capable of detecting arterial thickening and positive arterial remodeling at 3T in asymptomatic CAD., (Published by Elsevier Inc.)

Coronary bifurcations represent specific regions of the arterial tree that are susceptible to atherosclerotic lesions. While the effects of vessel compliance, curvature, pulsatile blood flow, and cardiac motion on coronary endothelial shear stress have been widely explored, the effects of myocardial contraction on arterial wall stress/strain (WS/S) and vessel stiffness distributions remain unclear. Local increase of vessel stiffness resulting from wall-strain stiffening phenomenon (a local process due to the nonlinear mechanical properties of the arterial wall) may be critical in the development of atherosclerotic lesions. Therefore, the aim of this study was to quantify WS/S and stiffness in coronary bifurcations and to investigate correlations with plaque sites. Anatomic coronary geometry and cardiac motion were generated based on both computed tomography and MRI examinations of eight patients with minimal coronary disease. Computational structural analyses using the finite element method were subsequently performed, and spatial luminal arterial wall stretch (LW(Stretch)) and stiffness (LW(Stiff)) distributions in the left main coronary bifurcations were calculated. Our results show that all plaque sites were concomitantly subject to high LW(Stretch) and high LW(Stiff), with mean amplitudes of 34.7 ± 1.6% and 442.4 ± 113.0 kPa, respectively. The mean LW(Stiff) amplitude was found slightly greater at the plaque sites on the left main coronary artery (mean value: 482.2 ± 88.1 kPa) compared with those computed on the left anterior descending and left circumflex coronary arteries (416.3 ± 61.5 and 428.7 ± 181.8 kPa, respectively). These findings suggest that local wall stiffness plays a role in the initiation of atherosclerotic lesions.

Cardiac magnetic resonance imaging (CMRI) at high magnetic field (3 Tesla) is rapidly evolving with many promising results. However, the challenges of field inhomogeneities and specific absorption rate limitations need to be addressed before reaping the benefits of high magnetic field for CMRI. This review focuses on the methods to overcome some of these challenges and the current and potential applications of this technology.