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.@en

Purpose: The aim of this study is to develop and optimize an adiabatic (Formula presented.) ((Formula presented.)) mapping method for robust quantification of spin-lock (SL) relaxation in the myocardium at 3T. Methods: Adiabatic SL (aSL) preparations were optimized for resilience against (Formula presented.) and (Formula presented.) inhomogeneities using Bloch simulations. Optimized (Formula presented.) -aSL, Bal-aSL and (Formula presented.) -aSL modules, each compensating for different inhomogeneities, were first validated in phantom and human calf. Myocardial (Formula presented.) mapping was performed using a single breath-hold cardiac-triggered bSSFP-based sequence. Then, optimized (Formula presented.) preparations were compared to each other and to conventional SL-prepared (Formula presented.) maps (RefSL) in phantoms to assess repeatability, and in 13 healthy subjects to investigate image quality, precision, reproducibility and intersubject variability. Finally, aSL and RefSL sequences were tested on six patients with known or suspected cardiovascular disease and compared with LGE, (Formula presented.), and ECV mapping. Results: The highest (Formula presented.) preparation efficiency was obtained in simulations for modules comprising 2 HS pulses of 30 ms each. In vivo (Formula presented.) maps yielded significantly higher quality than RefSL maps. Average myocardial (Formula presented.) values were 183.28 (Formula presented.) 25.53 ms, compared with 38.21 (Formula presented.) 14.37 ms RefSL-prepared (Formula presented.). (Formula presented.) maps showed a significant improvement in precision (avg. 14.47 (Formula presented.) 3.71% aSL, 37.61 (Formula presented.) 19.42% RefSL, p < 0.01) and reproducibility (avg. 4.64 (Formula presented.) 2.18% aSL, 47.39 (Formula presented.) 12.06% RefSL, p < 0.0001), with decreased inter-subject variability (avg. 8.76 (Formula presented.) 3.65% aSL, 51.90 (Formula presented.) 15.27% RefSL, p < 0.0001). Among aSL preparations, (Formula presented.) -aSL achieved the better inter-subject variability. In patients, (Formula presented.) -aSL preparations showed the best artifact resilience among the adiabatic preparations. (Formula presented.) times show focal alteration colocalized with areas of hyper-enhancement in the LGE images. Conclusion: Adiabatic preparations enable robust in vivo quantification of myocardial SL relaxation times at 3T.@en

Magnetic Resonance Imaging (MRI) is the clinical gold standard for the assessment of myocardial viability but requires injection of exogenous gadolinium-based contrast agents. Recently, T1ρ-mapping has been proposed as a fully non-invasive alternative for imaging myocardial fibrosis without the need for contrast agent injection. However, its applicability at high fields is hindered by susceptibility to MRI system imperfections, such as inhomogeneities in the B0 and B1+ fields. In this work we propose a single breath-hold ECG-triggered single-shot bSSFP sequence to enable T1ρ-mapping in vivo at 3T. Adiabatic T1ρ preparations are evaluated to reduce B0 and B1+ sensitivity in comparison with conventional spin-lock (SL) modules. Numerical Bloch simulations were performed to identify optimal parameters for the adiabatic pulses. Experiments yield T1ρ values in the myocardium equal to 48.13±54.08 ms for the best adiabatic preparation and 16.01±20.75 ms for the reference non-adiabatic SL, with 26.91% against 89.74% relative difference in T1ρ values across two shimming conditions. Both phantom and in vivo measurements show increased myocardium/blood contrast and improved resilience against system imperfections compared to non-adiabatic T1ρ preparations, enabling the use at 3T. Clinical relevance- Adiabatically-prepared T1ρ-mapping sequences form a promising candidate for non-contrast evaluation of ischemic and non-ischemic cardiomyopathies at 3T.@en

Multimodal platforms combining electrical neural recording and stimulation, optogenetics, optical imaging, and magnetic resonance (MRI) imaging are emerging as a promising platform to enhance the depth of characterization in neuroscientific research. Electrically conductive, optically transparent, and MRI-compatible electrodes can optimally combine all modalities. Graphene as a suitable electrode candidate material can be grown via chemical vapor deposition (CVD) processes and sandwiched between transparent biocompatible polymers. However, due to the high graphene growth temperature (≥ 900 °C) and the presence of polymers, fabrication is commonly based on a manual transfer process of pre-grown graphene sheets, which causes reliability issues. In this paper, we present CVD-based multilayer graphene electrodes fabricated using a wafer-scale transfer-free process for use in optically transparent and MRI-compatible neural interfaces. Our fabricated electrodes feature very low impedances which are comparable to those of noble metal electrodes of the same size and geometry. They also exhibit the highest charge storage capacity (CSC) reported to date among all previously fabricated CVD graphene electrodes. Our graphene electrodes did not reveal any photo-induced artifact during 10-Hz light pulse illumination. Additionally, we show here, for the first time, that CVD graphene electrodes do not cause any image artifact in a 3T MRI scanner. These results demonstrate that multilayer graphene electrodes are excellent candidates for the next generation of neural interfaces and can substitute the standard conventional metal electrodes. Our fabricated graphene electrodes enable multimodal neural recording, electrical and optogenetic stimulation, while allowing for optical imaging, as well as, artifact-free MRI studies. [Figure not available: see fulltext.].@en