To evaluate heart rate as a patient-related confounder in a commonly applied T2 balanced steady-state free precession (bSSFP) mapping sequence used for myocardial tissue characterization.Materials and Methods: This retrospective analysis included prospectively (from December 2013 to November 2021) acquired cardiac MRI (1.5 T) datasets with T2 bSSFP mapping from 69 healthy volunteers. Phantom studies and Bloch simulations were performed with heart rates of 60–130 beats per minute and different resting periods (three, six, or nine R-R intervals). Sequence parameters (repetition time, echo time, flip angle, echo train length) were matched across volunteer, phantom, and simulation measurements. Reference values covered clinically relevant T1 and T2 properties found in native myocardium (short, 1041 and 44 msec; medium, 1293 and 43 msec; long, 1534 and 40 msec). A mixed linear model assessed the effect of heart rate on T2 values in volunteer measurements.Results: The study included 69 healthy volunteers (median age, 34 years; 44 female and 25 male). Heart rate influenced T2 values acquired with three R-R resting periods (r = −0.38, P =.002; linear regression slope, −0.7 msec/10 beats per minute [95% CI: −1.2, −0.1]). In simulation and phantom measurements, T2 values acquired with three R-R resting periods strongly correlated with heart rate, irrespective of myocardial T1 and T2 properties (r ≤ −0.88; P <.01 for all measurements). Heart rate dependency was reduced with increased resting periods in simulations and phantom measurements. Short myocardial T1 and T2 values derived from T2 bSSFP with nine R-R resting periods were not dependent on heart rate (r = −0.41; P =.33).Conclusion: T2 bSSFP with three R-R resting periods underestimates T2 values with increasing heart rates. Use of longer resting periods with T2 bSSFP mapping sequences reduced heart rate dependency.

Purpose: To investigate and mitigate the influence of physiological and acquisition-related parameters on myocardial blood flow (MBF) measurements obtained with myocardial Arterial Spin Labeling (myoASL). Methods: A Flow-sensitive Alternating Inversion Recovery (FAIR) myoASL sequence with bSSFP and spoiled GRE (spGRE) readout is investigated for MBF quantification. Bloch-equation simulations and phantom experiments were performed to evaluate how variations in acquisition flip angle (FA), acquisition matrix size (AMS), heart rate (HR) and blood (Formula presented.) relaxation time ((Formula presented.)) affect quantification of myoASL-MBF. In vivo myoASL-images were acquired in nine healthy subjects. A corrected MBF quantification approach was proposed based on subject-specific (Formula presented.) values and, for spGRE imaging, subtracting an additional saturation-prepared baseline from the original baseline signal. Results: Simulated and phantom experiments showed a strong dependence on AMS and FA ((Formula presented.) >0.73), which was eliminated in simulations and alleviated in phantom experiments using the proposed saturation-baseline correction in spGRE. Only a very mild HR dependence ((Formula presented.) >0.59) was observed which was reduced when calculating MBF with individual (Formula presented.). For corrected spGRE, in vivo mean global spGRE-MBF ranged from 0.54 to 2.59 mL/g/min and was in agreement with previously reported values. Compared to uncorrected spGRE, the intra-subject variability within a measurement (0.60 mL/g/min), between measurements (0.45 mL/g/min), as well as the inter-subject variability (1.29 mL/g/min) were improved by up to 40% and were comparable with conventional bSSFP. Conclusion: Our results show that physiological and acquisition-related factors can lead to spurious changes in myoASL-MBF if not accounted for. Using individual (Formula presented.) and a saturation-baseline can reduce these variations in spGRE and improve reproducibility of FAIR-myoASL against acquisition parameters.

Cardiovascular disease (CVD) is the leading single cause of morbidity and mortality, causing over 17. 9 million deaths worldwide per year with associated costs of over $800 billion. Improving prevention, diagnosis, and treatment of CVD is therefore a global priority. Cardiovascular magnetic resonance (CMR) has emerged as a clinically important technique for the assessment of cardiovascular anatomy, function, perfusion, and viability. However, diversity and complexity of imaging, reconstruction and analysis methods pose some limitations to the widespread use of CMR. Especially in view of recent developments in the field of machine learning that provide novel solutions to address existing problems, it is necessary to bridge the gap between the clinical and scientific communities. This review covers five essential aspects of CMR to provide a comprehensive overview ranging from CVDs to CMR pulse sequence design, acquisition protocols, motion handling, image reconstruction and quantitative analysis of the obtained data. (1) The basic MR physics of CMR is introduced. Basic pulse sequence building blocks that are commonly used in CMR imaging are presented. Sequences containing these building blocks are formed for parametric mapping and functional imaging techniques. Commonly perceived artifacts and potential countermeasures are discussed for these methods. (2) CMR methods for identifying CVDs are illustrated. Basic anatomy and functional processes are described to understand the cardiac pathologies and how they can be captured by CMR imaging. (3) The planning and conduct of a complete CMR exam which is targeted for the respective pathology is shown. Building blocks are illustrated to create an efficient and patient-centered workflow. Further strategies to cope with challenging patients are discussed. (4) Imaging acceleration and reconstruction techniques are presented that enable acquisition of spatial, temporal, and parametric dynamics of the cardiac cycle. The handling of respiratory and cardiac motion strategies as well as their integration into the reconstruction processes is showcased. (5) Recent advances on deep learning-based reconstructions for this purpose are summarized. Furthermore, an overview of novel deep learning image segmentation and analysis methods is provided with a focus on automatic, fast and reliable extraction of biomarkers and parameters of clinical relevance.