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The Society for Cardiovascular Magnetic Resonance (SCMR) last published its comprehensive expert panel report of clinical indications for CMR in 2004. This new Consensus Panel report brings those indications up to date for 2020 and includes the very substantial increase in scanning techniques, clinical applicability and adoption of CMR worldwide. We have used a nearly identical grading system for indications as in 2004 to ensure comparability with the previous report but have added the presence of randomized controlled trials as evidence for level 1 indications. In addition to the text, tables of the consensus indication levels are included for rapid assimilation and illustrative figures of some key techniques are provided.

With mounting data on its accuracy and prognostic value, cardiovascular magnetic resonance (CMR) is becoming an increasingly important diagnostic tool with growing utility in clinical routine. Given its versatility and wide range of quantitative parameters, however, agreement on specific standards for the interpretation and post-processing of CMR studies is required to ensure consistent quality and reproducibility of CMR reports. This document addresses this need by providing consensus recommendations developed by the Task Force for Post Processing of the Society for Cardiovascular MR (SCMR). The aim of the task force is to recommend requirements and standards for image interpretation and post processing enabling qualitative and quantitative evaluation of CMR images. Furthermore, pitfalls of CMR image analysis are discussed where appropriate.

Background Left atrial (LA) size is related to cardiovascular morbidity and mortality. Cardiovascular magnetic resonance (CMR) provides high quality images of the left atrium with high temporal resolution steady state free precession (SSFP) cine sequences. We used SSFP cines to define normal ranges for LA volumes and dimensions relative to gender, age and body surface area (BSA), and examine the relative value of 2D atrial imaging techniques in patients. For definition of normal ranges of LA volume we studied 120 healthy subjects after careful exclusion of cardiovascular abnormality (60 men, 60 women; 20 subjects per age decile from 20 to 80 years). Data were generated from 3-dimensional modeling, including tracking of the atrioventricular ring motion and time-volume curves analysis. For definition of the best 2D images-derived predictors of LA enlargement, we studied 120 patients (60 men, 60 women; age range 20 to 80 years) with a clinical indication for CMR. Results In the healthy subjects, age was associated with LA 4-chamber transverse and 3-chamber anteroposterior diameters, but not with LA volume. Gender was an independent predictor of most absolute LA dimensions and volume, but following normalization to BSA, some associations became non-significant. CMR normal ranges were modeled and are tabled for clinical use with normalization, where appropriate, for BSA and gender and display of parameter variation with age. The best 2D predictors of LA volume were the 2-chamber area and 3-chamber area (both r = 0.90, p < 0.001). Conclusions These CMR data show that LA dimensions and volume in healthy, individuals vary significantly by BSA, with lesser effects of age and gender.

Intravoxel incoherent motion (IVIM) imaging can provide information on cardiac microstructure and microvascular perfusion from a single examination. However, the spin echo-based approaches typically used for cardiac IVIM suffer from low sensitivity to changes in perfusion. The aim of this work was to develop a stimulated-echo (STEAM)-based method for IVIM and diffusion tensor cardiovascular magnetic resonance to simultaneously provide biomarkers of microstructure and perfusion in vivo in the human heart. Here we introduce a novel STEAM-IVIM sequence incorporating phase cycling to obtain true non-diffusion weighted images (b = 0 s/mm2). STEAM-IVIM imaging was performed at 20 b-values (0 to 1000 s/mm2) to enable accurate estimation of the IVIM parameters, and with six diffusion encoding directions to enable reconstruction of the diffusion tensor. 20 healthy subjects (8 female, median age 31 years) were imaged on a clinical 3T system with STEAM-IVIM. A simulation study was performed to investigate the optimal fitting algorithms for the IVIM parameters, which was subsequently used to create pixel-wise IVIM parameter maps for the in vivo acquisitions. Good image quality across the myocardium was obtained for all b-values. Mean(±SD) IVIM parameter estimates were: diffusivity D = 0.83 ± 0.07 × 10-3 mm2/s, perfusion coefficient D* = 19.08 ± 6.48 × 10-3 mm2/s, perfusion fraction f = 19.72 ± 4.11%, and mean diffusion tensor parameters were: mean diffusivity = 0.88 ± 0.06 × 10-3 mm2/s, fractional anisotropy = 0.45 ± 0.04, absolute E2 angle = 55.29 ± 6.38º, helix angle gradient = -0.68 ± 0.18º/%. Phase-cycled STEAM-IVIM enables fitting of cardiac diffusion tensor and perfusion parameters in healthy subjects and shows promise for the simultaneous detection of microstructural aberration and perfusion abnormalities in the presence of cardiac disease without the need for exogenous contrast agents. [Display omitted]

Cine Displacement Encoding with Stimulated Echoes (DENSE) facilitates the quantification of myocardial deformation, by encoding tissue displacements in the cardiovascular magnetic resonance (CMR) image phase, from which myocardial strain can be estimated with high accuracy and reproducibility. Current methods for analyzing DENSE images still heavily rely on user input, making this process time-consuming and subject to inter-observer variability. The present study sought to develop a spatio-temporal deep learning model for segmentation of the left-ventricular (LV) myocardium, as spatial networks often fail due to contrast-related properties of DENSE images. 2D + time nnU-Net-based models have been trained to segment the LV myocardium from DENSE magnitude data in short- and long-axis images. A dataset of 360 short-axis and 124 long-axis slices was used to train the networks, from a combination of healthy subjects and patients with various conditions (hypertrophic and dilated cardiomyopathy, myocardial infarction, myocarditis). Segmentation performance was evaluated using ground-truth manual labels, and a strain analysis using conventional methods was performed to assess strain agreement with manual segmentation. Additional validation was performed using an externally acquired dataset to compare the inter- and intra-scanner reproducibility with respect to conventional methods. Spatio-temporal models gave consistent segmentation performance throughout the cine sequence, while 2D architectures often failed to segment end-diastolic frames due to the limited blood-to-myocardium contrast. Our models achieved a DICE score of 0.83 ± 0.05 and a Hausdorff distance of 4.0 ± 1.1 mm for short-axis segmentation, and 0.82 ± 0.03 and 7.9 ± 3.9 mm respectively for long-axis segmentations. Strain measurements obtained from automatically estimated myocardial contours showed good to excellent agreement with manual pipelines, and remained within the limits of inter-user variability estimated in previous studies. Spatio-temporal deep learning shows increased robustness for the segmentation of cine DENSE images. It provides excellent agreement with manual segmentation for strain extraction. Deep learning will facilitate the analysis of DENSE data, bringing it one step closer to clinical routine.

In vivo cardiac diffusion tensor imaging (cDTI) is a promising Magnetic Resonance Imaging (MRI) technique for evaluating the microstructure of myocardial tissue in living hearts, providing insights into cardiac function and enabling the development of innovative therapeutic strategies. However, the integration of cDTI into routine clinical practice poses challenging due to the technical obstacles involved in the acquisition, such as low signal-to-noise ratio and prolonged scanning times. In this study, we investigated and implemented three different types of deep learning-based MRI reconstruction models for cDTI reconstruction. We evaluated the performance of these models based on the reconstruction quality assessment, the diffusion tensor parameter assessment as well as the computational cost assessment. Our results indicate that the models discussed in this study can be applied for clinical use at an acceleration factor (AF) of × 2 and × 4 , with the D5C5 model showing superior fidelity for reconstruction and the SwinMR model providing higher perceptual scores. There is no statistical difference from the reference for all diffusion tensor parameters at AF × 2 or most DT parameters at AF × 4 , and the quality of most diffusion tensor parameter maps is visually acceptable. SwinMR is recommended as the optimal approach for reconstruction at AF × 2 and AF × 4 . However, we believe that the models discussed in this study are not yet ready for clinical use at a higher AF. At AF × 8 , the performance of all models discussed remains limited, with only half of the diffusion tensor parameters being recovered to a level with no statistical difference from the reference. Some diffusion tensor parameter maps even provide wrong and misleading information.

Myocardial strain is a valuable biomarker for diagnosing and predicting cardiac conditions, offering additional prognostic information to traditional metrics such as ejection fraction. While cardiovascular magnetic resonance (CMR) methods, particularly cine displacement encoding with stimulated echoes (DENSE), are the gold standard for strain estimation, evaluation of regional strain estimation requires precise ground truth. This study introduces DENSE-SIM, an open-source simulation pipeline for generating realistic cine DENSE images with high-resolution known ground-truth strain, enabling evaluation of accuracy and precision in strain analysis pipelines. This pipeline is a modular tool designed for simulating cine DENSE images and evaluating strain estimation performance. It comprises four main modules: 1) anatomy generation, for creating end-diastolic cardiac shapes; 2) motion generation, to produce myocardial deformations over time and Lagrangian strain; 3) DENSE image generation, using Bloch equation simulations with realistic noise, spiral sampling, and phase cycling; and 4) strain evaluation. To illustrate the pipeline, a synthetic dataset of 180 short-axis slices was created and analyzed using the commonly used DENSEanalysis tool. The impact of the spatial regularization parameter (k) in DENSEanalysis was evaluated against the ground-truth pixel strain, to particularly assess the resulting bias and variance characteristics. Simulated strain profiles were generated with a myocardial signal-to-noise ratio (SNR) ranging from 3.9 to 17.7. For end-systolic radial strain, DENSEanalysis average signed error (ASE) in Green strain ranged from 0.04 ± 0.09 (true-calculated, mean ± std) for a typical regularization (k = 0.9), to −0.01 ± 0.21 at low regularization (k = 0.1). Circumferential strain ASE ranged from −0.00 ± 0.04 at k = 0.9 to −0.01 ± 0.10 at k = 0.1. This demonstrates that the circumferential strain closely matched the ground truth, while radial strain displayed more significant underestimations, particularly near the endocardium. A lower regularization parameter from 0.3 to 0.6 depending on the myocardial SNR would be more appropriate to estimate the radial strain, as a compromise between noise compensation and global strain accuracy. Generating realistic cine DENSE images with high-resolution ground-truth strain and myocardial segmentation enables accurate evaluation of strain analysis tools, while reproducing key in-vivo acquisition features, and will facilitate the future development of deep-learning models for myocardial strain analysis, enhancing clinical CMR workflows. [Display omitted]

In-vivo diffusion tensor cardiovascular magnetic resonance (DT-CMR) is an emerging technique for microstructural tissue characterization in the myocardium. Most studies are performed at 3T, where higher signal-to-noise ratio (SNR) should benefit this signal-starved method. However, a few studies have suggested that DT-CMR is possible at 1.5T, where echo planar imaging artifacts may be less severe and 1.5T hardware is more widely available. We recruited 20 healthy volunteers and performed mid-ventricular short-axis DT-CMR at 1.5T and 3T. Acquisitions were performed at peak systole and end-diastole using both stimulated echo acquisition mode (STEAM) and motion-compensated spin-echo (MCSE) sequences at matched spatial resolutions. DT-CMR parameters were averaged over the left ventricle and compared between 1.5T and 3T sequences using both datasets with and without the blow reference data included. Eleven (1.5T) and 12 (3T) diastolic MCSE acquisitions were rejected as the helix angle (HA) demonstrated <50% normal appearance circumferentially or the acquisition was abandoned due to poor image quality; a maximum of one acquisition was rejected for other datasets. Subjective HA map quality was significantly better at 3T than 1.5T for STEAM (p < 0.05), but not for MCSE and other DT-CMR quality measures were consistent with improvements in STEAM at 3T over 1.5T. When blow data were excluded, no significant differences in mean diffusivity were observed between field strengths, but fractional anisotropy was significantly higher at 1.5T than 3T for STEAM systole (p < 0.05). Absolute second eigenvector orientation (E2A, sheetlet angle) was significantly higher at 1.5T than 3T for MCSE systole and STEAM diastole, but significantly lower for STEAM systole (all p < 0.05). Transmural HA distribution was less steep at 1.5T than 3T for STEAM diastole data (p < 0.05). SNR was higher at 3T than 1.5T for all acquisitions (p < 0.05). While 3T provides benefits in terms of SNR, both STEAM and MCSE can be performed at 1.5T. However, MCSE is unreliable in diastole at both field strengths and STEAM benefits from the improved SNR at 3T over 1.5T. Future clinical research studies may be able to leverage the wider availability of 1.5T CMR hardware where MCSE acquisitions are desirable. [Display omitted]

The coronary sinus reducer (CSR) is a novel percutaneous treatment for patients with refractory angina. Increasing evidence supports its clinical efficacy in patients with advanced epicardial coronary artery disease. However, its mechanism of action and its effects on myocardial perfusion remain undefined. Using quantitative stress perfusion cardiovascular magnetic resonance (CMR), this study assessed changes in myocardial perfusion in patients with refractory angina undergoing CSR implantation. This single-center retrospective observational cohort study included 16 patients. Rest and adenosine stress perfusion CMR was performed before and at median 5 months after CSR implantation. Perfusion images were acquired using a dual-sequence quantitative protocol with automated generation of myocardial blood flow (MBF; mL/min/g). In addition to visual assessment of ischemic segments, changes in absolute MBF across myocardial segments and between myocardial layers were analyzed. A high proportion of myocardial segments had visually adjudicated ischemia at baseline (208 out of 254: 81.9%), which significantly reduced after CSR implantation (175 out of 254: 68.9%; P = 0.001). There were no changes in global MBF or strain values. Changes in myocardial perfusion reserve (MPR) correlated with baseline MPR with more ischemic segments at baseline improving to a greater extent at follow-up. Similar patterns were observed in both the left and right coronary artery territories. Changes in endocardial/epicardial MBF ratio at stress were similarly dependent on baseline values. In patients with refractory angina undergoing CSR implantation, quantitative stress perfusion CMR demonstrated redistribution of myocardial perfusion across segments, from less ischemic to more ischemic myocardium, and across myocardial layers with greatest improvements in endocardial perfusion observed in the most ischemic myocardium. Further studies are needed to validate the different patterns of MBF redistribution that may occur after CSR implantation and correlate with clinical outcomes. [Display omitted]