Three-dimensional EPI with shot-selective CAIPIRIHANA for rapid high-resolution quantitative susceptibility mapping at 3 T.

PURPOSE: QSM provides insight into healthy brain aging and neuropathologies such as multiple sclerosis (MS), traumatic brain injuries, brain tumors, and neurodegenerative diseases. Phase data for QSM are usually acquired from 3D gradient-echo (3D GRE) scans with long acquisition times that are detrimental to patient comfort and susceptible to patient motion. This is particularly true for scans requiring whole-brain coverage and submillimeter resolutions. In this work, we use a multishot 3D echo plannar imaging (3D EPI) sequence with shot-selective 2D CAIPIRIHANA to acquire high-resolution, whole-brain data for QSM with minimal distortion and blurring. METHODS: To test clinical viability, the 3D EPI sequence was used to image a cohort of MS patients at 1-mm isotropic resolution at 3 T. Additionally, 3D EPI data of healthy subjects were acquired at 1-mm, 0.78-mm, and 0.65-mm isotropic resolution with varying echo train lengths (ETLs) and compared with a reference 3D GRE acquisition. RESULTS: The appearance of the susceptibility maps and the susceptibility values for segmented regions of interest were comparable between 3D EPI and 3D GRE acquisitions for both healthy and MS participants. Additionally, all lesions visible in the MS patients on the 3D GRE susceptibility maps were also visible on the 3D EPI susceptibility maps. The interplay among acquisition time, resolution, echo train length, and the effect of distortion on the calculated susceptibility maps was investigated. CONCLUSION: We demonstrate that the 3D EPI sequence is capable of rapidly acquiring submillimeter resolutions and providing high-quality, clinically relevant susceptibility maps.

Body size interacts with the structure of the central nervous system: A multi-center in vivo neuroimaging study.

Clinical research emphasizes the implementation of rigorous and reproducible study designs that rely on between-group matching or controlling for sources of biological variation such as subject's sex and age. However, corrections for body size (i.e. height and weight) are mostly lacking in clinical neuroimaging designs. This study investigates the importance of body size parameters in their relationship with spinal cord (SC) and brain magnetic resonance imaging (MRI) metrics. Data were derived from a cosmopolitan population of 267 healthy human adults (age 30.1±6.6 years old, 125 females). We show that body height correlated strongly or moderately with brain gray matter (GM) volume, cortical GM volume, total cerebellar volume, brainstem volume, and cross-sectional area (CSA) of cervical SC white matter (CSA-WM; 0.44≤r≤0.62). In comparison, age correlated weakly with cortical GM volume, precentral GM volume, and cortical thickness (-0.21≥r≥-0.27). Body weight correlated weakly with magnetization transfer ratio in the SC WM, dorsal columns, and lateral corticospinal tracts (-0.20≥r≥-0.23). Body weight further correlated weakly with the mean diffusivity derived from diffusion tensor imaging (DTI) in SC WM (r=-0.20) and dorsal columns (-0.21), but only in males. CSA-WM correlated strongly or moderately with brain volumes (0.39≤r≤0.64), and weakly with precentral gyrus thickness and DTI-based fractional anisotropy in SC dorsal columns and SC lateral corticospinal tracts (-0.22≥r≥-0.25). Linear mixture of sex and age explained 26±10% of data variance in brain volumetry and SC CSA. The amount of explained variance increased at 33±11% when body height was added into the mixture model. Age itself explained only 2±2% of such variance. In conclusion, body size is a significant biological variable. Along with sex and age, body size should therefore be included as a mandatory variable in the design of clinical neuroimaging studies examining SC and brain structure.

Neurochemical Predictors of Generalized Learning Induced by Brain Stimulation and Training.

Methods of cognitive enhancement for humans are most impactful when they generalize across tasks. However, the extent to which such "transfer" is possible via interventions is widely debated. In addition, the contribution of excitatory and inhibitory processes to such transfer is unknown. Here, in a large-scale neuroimaging individual differences study with humans (both sexes), we paired multitasking training and noninvasive brain stimulation (transcranial direct current stimulation, tDCS) over multiple days and assessed performance across a range of paradigms. In addition, we varied tDCS dosage (1.0 and 2.0 mA), electrode montage (left or right prefrontal regions), and training task (multitasking vs a control task) and assessed GABA and glutamate concentrations via ultrahigh field 7T magnetic resonance spectroscopy. Generalized benefits were observed in spatial attention, indexed by visual search performance, when multitasking training was combined with 1.0 mA stimulation targeting either the left or right prefrontal cortex (PFC). This transfer effect persisted for ∼30 d post intervention. Critically, the transferred benefits associated with right prefrontal tDCS were predicted by pretraining concentrations of glutamate in the PFC. Thus, the effects of this combined stimulation and training protocol appear to be linked predominantly to excitatory brain processes.

B 1 + inhomogeneity mitigation for diffusion weighted MRI at 7T using TR-FOCI pulses.

PURPOSE: The purpose of this study is to improve the image quality of diffusion-weighted images obtained with a single RF transmit channel 7 T MRI setup using time-resampled frequency-offset corrected inversion (TR-FOCI) pulses to refocus the spins in a twice-refocused spin-echo readout scheme. METHODS: We replaced the conventional Shinnar-Le Roux-pulses in the twice refocused diffusion sequence with TR-FOCI pulses. The slice profiles were evaluated in simulation and experimentally in phantoms. The image quality was evaluated in vivo comparing the Shinnar-Le Roux and TR-FOCI implementation using a b value of 0 and of 1000 s/mm2. RESULTS: The b0 and diffusion-weighted images acquired using the modified sequence improved the image quality across the whole brain. A region of interest-based analysis showed an SNR increase of 113% and 66% for the nondiffusion-weighted (b0) and the diffusion-weighted (b = 1000 s/mm2) images in the temporal lobes, respectively. Investigation of all slices showed that the adiabatic pulses mitigated B 1 + $$ {B}_1^{+} $$ inhomogeneity globally using a conventional single-channel transmission setup. CONCLUSION: The TR-FOCI pulse can be used in a twice-refocused spin-echo diffusion pulse sequence to mitigate the impact of B 1 + $$ {B}_1^{+} $$ inhomogeneity on the signal intensity across the brain at 7 T. However, further work is needed to address SAR limitations.

Velocity selective spin labeling using parallel transmission.

PURPOSE: Ultra-high field (UHF) provides improved SNR which greatly benefits SNR starved imaging techniques such as perfusion imaging. However, transmit field (B1 + ) inhomogeneities commonly observed at UHF hinders the excitation uniformity. Here we show how replacing standard excitation pulses with parallel transmit pulses can improve efficiency of velocity selective labeling. METHODS: The standard tip-down and tip-up excitation pulses found in a velocity selective preparation module were replaced with tailored non-selective kT -points pulse solutions. Bloch simulations and experimental validation on a custom-built flow phantom and in vivo was performed to evaluate different pulse configurations in circularly polarized mode (CP-mode) and parallel transmit (pTx) mode. RESULTS: Tailored pTx pulses significantly improved velocity selective labeling fidelity and signal uniformity. The transverse magnetization normalized RMS error was reduced from 0.489 to 0.047 when compared to standard rectangular pulses played in CP-mode. Simulations showed that manipulation of time symmetry in the tailored pTx pulses is vital in minimizing residual magnetization. In addition, in vivo experiments achieved a 44% lower RF power output and a shorter pulse duration when compared to using adiabatic pulses in CP-mode. CONCLUSION: Using tailored pTx pulses for excitation within a velocity selective labeling preparation mitigated transmit field artifacts and improved SNR and contrast fidelity. The improvement in labeling efficiency highlights the potential of using pTx to improve robustness and accessibility of flow-based sequences such as velocity selective spin labeling at ultra-high field.

Improving portable low-field MRI image quality through image-to-image translation using paired low- and high-field images.

Low-field portable magnetic resonance imaging (MRI) scanners are more accessible, cost-effective, sustainable with lower carbon emissions than superconducting high-field MRI scanners. However, the images produced have relatively poor image quality, lower signal-to-noise ratio, and limited spatial resolution. This study develops and investigates an image-to-image translation deep learning model, LoHiResGAN, to enhance the quality of low-field (64mT) MRI scans and generate synthetic high-field (3T) MRI scans. We employed a paired dataset comprising T1- and T2-weighted MRI sequences from the 64mT and 3T and compared the performance of the LoHiResGAN model with other state-of-the-art models, including GANs, CycleGAN, U-Net, and cGAN. Our proposed method demonstrates superior performance in terms of image quality metrics, such as normalized root-mean-squared error, structural similarity index measure, peak signal-to-noise ratio, and perception-based image quality evaluator. Additionally, we evaluated the accuracy of brain morphometry measurements for 33 brain regions across the original 3T, 64mT, and synthetic 3T images. The results indicate that the synthetic 3T images created using our proposed LoHiResGAN model significantly improve the image quality of low-field MRI data compared to other methods (GANs, CycleGAN, U-Net, cGAN) and provide more consistent brain morphometry measurements across various brain regions in reference to 3T. Synthetic images generated by our method demonstrated high quality both quantitatively and qualitatively. However, additional research, involving diverse datasets and clinical validation, is necessary to fully understand its applicability for clinical diagnostics, especially in settings where high-field MRI scanners are less accessible.

Super-resolution QSM in little or no additional time for imaging (NATIve) using 2D EPI imaging in 3 orthogonal planes.

Quantitative Susceptibility Mapping has the potential to provide additional insights into neurological diseases but is typically based on a quite long (5-10 min) 3D gradient-echo scan which is highly sensitive to motion. We propose an ultra-fast acquisition based on three orthogonal (sagittal, coronal and axial) 2D simultaneous multi-slice EPI scans with 1 mm in-plane resolution and 3 mm thick slices. Images in each orientation are corrected for susceptibility-related distortions and co-registered with an iterative non-linear Minimum Deformation Averaging (Volgenmodel) approach to generate a high SNR, super-resolution data set with an isotropic resolution of close to 1 mm. The net acquisition time is 3 times the volume acquisition time of EPI or about 12 s, but the three volumes could also replace "dummy scans" in fMRI, making it feasible to acquire QSM in little or No Additional Time for Imaging (NATIve). NATIve QSM values agreed well with reference 3D GRE QSM in the basal ganglia in healthy subjects. In patients with multiple sclerosis, there was also a good agreement between the susceptibility values within lesions and control ROIs and all lesions which could be seen on 3D GRE QSMs could also be visualized on NATIve QSMs. The approach is faster than conventional 3D GRE by a factor of 25-50 and faster than 3D EPI by a factor of 3-5. As a 2D technique, NATIve QSM was shown to be much more robust to motion than the 3D GRE and 3D EPI, opening up the possibility of studying neurological diseases involving iron accumulation and demyelination in patients who find it difficult to lie still for long enough to acquire QSM data with conventional methods.

Improved dynamic distortion correction for fMRI using single-echo EPI and a readout-reversed first image (REFILL).

The boundaries between tissues with different magnetic susceptibilities generate inhomogeneities in the main magnetic field which change over time due to motion, respiration and system instabilities. The dynamically changing field can be measured from the phase of the fMRI data and corrected. However, methods for doing so need multi-echo data, time-consuming reference scans and/or involve error-prone processing steps, such as phase unwrapping, which are difficult to implement robustly on the MRI host. The improved dynamic distortion correction method we propose is based on the phase of the single-echo EPI data acquired for fMRI, phase offsets calculated from a triple-echo, bipolar reference scan of circa 3-10 s duration using a method which avoids the need for phase unwrapping and an additional correction derived from one EPI volume in which the readout direction is reversed. This Reverse-Encoded First Image and Low resoLution reference scan (REFILL) approach is shown to accurately measure B0 as it changes due to shim, motion and respiration, even with large dynamic changes to the field at 7 T, where it led to a > 20% increase in time-series signal to noise ratio compared to data corrected with the classic static approach. fMRI results from REFILL-corrected data were free of stimulus-correlated distortion artefacts seen when data were corrected with static field mapping. The method is insensitive to shim changes and eddy current differences between the reference scan and the fMRI time series, and employs calculation steps that are simple and robust, allowing most data processing to be performed in real time on the scanner image reconstruction computer. These improvements make it feasible to routinely perform dynamic distortion correction in fMRI.

Altered brain connectivity in Long Covid during cognitive exertion: a pilot study.

Introduction: Debilitating Long-Covid symptoms occur frequently after SARS-COVID-19 infection. Methods: Functional MRI was acquired in 10 Long Covid (LCov) and 13 healthy controls (HC) with a 7 Tesla scanner during a cognitive (Stroop color-word) task. BOLD time series were computed for 7 salience and 4 default-mode network hubs, 2 hippocampus and 7 brainstem regions (ROIs). Connectivity was characterized by the correlation coefficient between each pair of ROI BOLD time series. We tested for HC versus LCov differences in connectivity between each pair of the 20 regions (ROI-to-ROI) and between each ROI and the rest of the brain (ROI-to-voxel). For LCov, we also performed regressions of ROI-to-ROI connectivity with clinical scores. Results: Two ROI-to-ROI connectivities differed between HC and LCov. Both involved the brainstem rostral medulla, one connection to the midbrain, another to a DM network hub. Both were stronger in LCov than HC. ROI-to-voxel analysis detected multiple other regions where LCov connectivity differed from HC located in all major lobes. Most, but not all connections, were weaker in LCov than HC. LCov, but not HC connectivity, was correlated with clinical scores for disability and autonomic function and involved brainstem ROI. Discussion: Multiple connectivity differences and clinical correlations involved brainstem ROIs. Stronger connectivity in LCov between the medulla and midbrain may reflect a compensatory response. This brainstem circuit regulates cortical arousal, autonomic function and the sleep-wake cycle. In contrast, this circuit exhibited weaker connectivity in ME/CFS. LCov connectivity regressions with disability and autonomic scores were consistent with altered brainstem connectivity in LCov.

HumanBrainAtlas: an in vivo MRI dataset for detailed segmentations.

We introduce HumanBrainAtlas, an initiative to construct a highly detailed, open-access atlas of the living human brain that combines high-resolution in vivo MR imaging and detailed segmentations previously possible only in histological preparations. Here, we present and evaluate the first step of this initiative: a comprehensive dataset of two healthy male volunteers reconstructed to a 0.25 mm isotropic resolution for T1w, T2w, and DWI contrasts. Multiple high-resolution acquisitions were collected for each contrast and each participant, followed by averaging using symmetric group-wise normalisation (Advanced Normalisation Tools). The resulting image quality permits structural parcellations rivalling histology-based atlases, while maintaining the advantages of in vivo MRI. For example, components of the thalamus, hypothalamus, and hippocampus are often impossible to identify using standard MRI protocols-can be identified within the present data. Our data are virtually distortion free, fully 3D, and compatible with the existing in vivo Neuroimaging analysis tools. The dataset is suitable for teaching and is publicly available via our website (hba.neura.edu.au), which also provides data processing scripts. Instead of focusing on coordinates in an averaged brain space, our approach focuses on providing an example segmentation at great detail in the high-quality individual brain. This serves as an illustration on what features contrasts and relations can be used to interpret MRI datasets, in research, clinical, and education settings.

Biased perception of distributions: Anchoring, interpolation and smoothing as potential causes.

Perceiving the degree of variation in the social and non-social environment is a cognitive task that is important for many judgments and decisions. In the present research, we investigated cognitive underpinnings of how people estimate the average value of segments of a statistical distribution (e.g., what is the average income of the richest 25% of a population?). In three experiments (total N = 222), participants learned about the values of experimentally created distributions of income values and city sizes and later estimated the mean value of the four quarters of values. We expected participants to draw on heuristic shortcuts to generate such judgments. More specifically, we hypothesized that participants use the endpoints of the distributions as anchors and determine the mean values by linear interpolation. In addition, we tested the contribution of three further processes (Range-Frequency adjustments, Normal Smoothing, Linear Smoothing). Quantitative model tests suggest that anchoring and Linear Smoothing both affected mean interquartile judgments. This conclusion is corroborated by tests of qualitative predictions of the models under consideration.

Brainstem volume changes in myalgic encephalomyelitis/chronic fatigue syndrome and long COVID patients.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and long COVID patients have overlapping neurological, autonomic, pain, and post-exertional symptoms. We compared volumes of brainstem regions for 10 ME/CFS (CCC or ICC criteria), 8 long COVID (WHO Delphi consensus), and 10 healthy control (HC) subjects on 3D, T1-weighted MRI images acquired using sub-millimeter isotropic resolution using an ultra-high field strength of 7 Tesla. Group comparisons with HC detected significantly larger volumes in ME/CFS for pons (p = 0.004) and whole brainstem (p = 0.01), and in long COVID for pons (p = 0.003), superior cerebellar peduncle (p = 0.009), and whole brainstem (p = 0.005). No significant differences were found between ME/CFS and long COVID volumes. In ME/CFS, we detected positive correlations between the pons and whole brainstem volumes with "pain" and negative correlations between the midbrain and whole brainstem volumes with "breathing difficulty." In long COVID patients a strong negative relationship was detected between midbrain volume and "breathing difficulty." Our study demonstrated an abnormal brainstem volume in both ME/CFS and long COVID consistent with the overlapping symptoms.

NeXtQSM-A complete deep learning pipeline for data-consistent Quantitative Susceptibility Mapping trained with hybrid data.

Deep learning based Quantitative Susceptibility Mapping (QSM) has shown great potential in recent years, obtaining similar results to established non-learning approaches. Many current deep learning approaches are not data consistent, require in vivo training data or solve the QSM problem in consecutive steps resulting in the propagation of errors. Here we aim to overcome these limitations and developed a framework to solve the QSM processing steps jointly. We developed a new hybrid training data generation method that enables the end-to-end training for solving background field correction and dipole inversion in a data-consistent fashion using a variational network that combines the QSM model term and a learned regularizer. We demonstrate that NeXtQSM overcomes the limitations of previous deep learning methods. NeXtQSM offers a new deep learning based pipeline for computing quantitative susceptibility maps that integrates each processing step into the training and provides results that are robust and fast.

Lower hypothalamic volume with lower body mass index is associated with shorter survival in patients with amyotrophic lateral sclerosis.

BACKGROUND AND PURPOSE: Weight loss in patients with amyotrophic lateral sclerosis (ALS) is associated with faster disease progression and shorter survival. Decreased hypothalamic volume is proposed to contribute to weight loss due to loss of appetite and/or hypermetabolism. We aimed to investigate the relationship between hypothalamic volume and body mass index (BMI) in ALS and Alzheimer's disease (AD), and the associations of hypothalamic volume with weight loss, appetite, metabolism and survival in patients with ALS. METHODS: We compared hypothalamic volumes from magnetic resonance imaging scans with BMI for patients with ALS (n = 42), patients with AD (n = 167) and non-neurodegenerative disease controls (n = 527). Hypothalamic volumes from patients with ALS were correlated with measures of appetite and metabolism, and change in anthropomorphic measures and disease outcomes. RESULTS: Lower hypothalamic volume was associated with lower and higher BMI in ALS (quadratic association; probability of direction = 0.96). This was not observed in AD patients or controls. Hypothalamic volume was not associated with loss of appetite (p = 0.58) or hypermetabolism (p = 0.49). Patients with lower BMI and lower hypothalamic volume tended to lose weight (p = 0.08) and fat mass (p = 0.06) over the course of their disease, and presented with an increased risk of earlier death (hazard ratio [HR] 3.16, p = 0.03). Lower hypothalamic volume alone trended for greater risk of earlier death (HR 2.61, p = 0.07). CONCLUSION: These observations suggest that lower hypothalamic volume in ALS contributes to positive and negative energy balance, and  is not universally associated with loss of appetite or hypermetabolism. Critically, lower hypothalamic volume with lower BMI was associated with weight loss and earlier death.

Modelling the depth-dependent VASO and BOLD responses in human primary visual cortex.

Functional magnetic resonance imaging (fMRI) using a blood-oxygenation-level-dependent (BOLD) contrast is a common method for studying human brain function noninvasively. Gradient-echo (GRE) BOLD is highly sensitive to the blood oxygenation change in blood vessels; however, the spatial signal specificity can be degraded due to signal leakage from activated lower layers to superficial layers in depth-dependent (also called laminar or layer-specific) fMRI. Alternatively, physiological variables such as cerebral blood volume using the VAscular-Space-Occupancy (VASO) contrast have shown higher spatial specificity compared to BOLD. To better understand the physiological mechanisms such as blood volume and oxygenation changes and to interpret the measured depth-dependent responses, models are needed which reflect vascular properties at this scale. For this purpose, we extended and modified the "cortical vascular model" previously developed to predict layer-specific BOLD signal changes in human primary visual cortex to also predict a layer-specific VASO response. To evaluate the model, we compared the predictions with experimental results of simultaneous VASO and BOLD measurements in a group of healthy participants. Fitting the model to our experimental data provided an estimate of CBV change in different vascular compartments upon neural activity. We found that stimulus-evoked CBV change mainly occurs in small arterioles, capillaries, and intracortical arteries and that the contribution from venules and ICVs is smaller. Our results confirm that VASO is less susceptible to large vessel effects compared to BOLD, as blood volume changes in intracortical arteries did not substantially affect the resulting depth-dependent VASO profiles, whereas depth-dependent BOLD profiles showed a bias towards signal contributions from intracortical veins.

Subcortical and default mode network connectivity is impaired in myalgic encephalomyelitis/chronic fatigue syndrome.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex chronic condition with core symptoms of fatigue and cognitive dysfunction, suggesting a key role for the central nervous system in the pathophysiology of this disease. Several studies have reported altered functional connectivity (FC) related to motor and cognitive deficits in ME/CFS patients. In this study, we compared functional connectivity differences between 31 ME/CFS and 15 healthy controls (HCs) using 7 Tesla MRI. Functional scans were acquired during a cognitive Stroop color-word task, and blood oxygen level-dependent (BOLD) time series were computed for 27 regions of interest (ROIs) in the cerebellum, brainstem, and salience and default mode networks. A region-based comparison detected reduced FC between the pontine nucleus and cerebellum vermis IX (p = 0.027) for ME/CFS patients compared to HCs. Our ROI-to-voxel analysis found significant impairment of FC within the ponto-cerebellar regions in ME/CFS. Correlation analyses of connectivity with clinical scores in ME/CFS patients detected associations between FC and 'duration of illness' and 'memory scores' in salience network hubs and cerebellum vermis and between FC and 'respiratory rate' within the medulla and the default mode network FC. This novel investigation is the first to report the extensive involvement of aberrant ponto-cerebellar connections consistent with ME/CFS symptomatology. This highlights the involvement of the brainstem and the cerebellum in the pathomechanism of ME/CFS.

QSMxT: Robust masking and artifact reduction for quantitative susceptibility mapping.

PURPOSE: Quantitative susceptibility mapping (QSM) estimates the spatial distribution of tissue magnetic susceptibilities from the phase of a gradient-echo signal. QSM algorithms require a signal mask to delineate regions with reliable phase for subsequent susceptibility estimation. Existing masking techniques used in QSM have limitations that introduce artifacts, exclude anatomical detail, and rely on parameter tuning and anatomical priors that narrow their application. Here, a robust masking and reconstruction procedure is presented to overcome these limitations and enable automated QSM processing. Moreover, this method is integrated within an open-source software framework: QSMxT. METHODS: A robust masking technique that automatically separates reliable from less reliable phase regions was developed and combined with a two-pass reconstruction procedure that operates on the separated sources before combination, extracting more information and suppressing streaking artifacts. RESULTS: Compared with standard masking and reconstruction procedures, the two-pass inversion reduces streaking artifacts caused by unreliable phase and high dynamic ranges of susceptibility sources. It is also robust across a range of acquisitions at 3 T in volunteers and phantoms, at 7 T in tumor patients, and in an in silico head phantom, with significant artifact and error reductions, greater anatomical detail, and minimal parameter tuning. CONCLUSION: The two-pass masking and reconstruction procedure separates reliable from less reliable phase regions, enabling a more accurate QSM reconstruction that mitigates artifacts, operates without anatomical priors, and requires minimal parameter tuning. The technique and its integration within QSMxT makes QSM processing more accessible and robust to streaking artifacts.

7-Tesla Functional Cardiovascular MR Using Vectorcardiographic Triggering-Overcoming the Magnetohydrodynamic Effect.

Objective: Ultra-high-field B0 ≥ 7 tesla (7T) cardiovascular magnetic resonance (CMR) offers increased resolution. However, electrocardiogram (ECG) gating is impacted by the magneto-hydrodynamic effect distorting the ECG trace. We explored the technical feasibility of a 7T magnetic resonance scanner using an ECG trigger learning algorithm to quantitatively assess cardiac volumes and vascular flow. Methods: 7T scans were performed on 10 healthy volunteers on a whole-body research MRI MR scanner (Siemens Healthineers, Erlangen, Germany) with 8 channel Tx/32 channels Rx cardiac coils (MRI Tools GmbH, Berlin, Germany). Vectorcardiogram ECG was performed using a learning phase outside of the magnetic field, with a trigger algorithm overcoming severe ECG signal distortions. Vectorcardiograms were quantitatively analyzed for false negative and false positive events. Cine CMR was performed after 3rd-order B0 shimming using a high-resolution breath-held ECG-retro-gated segmented spoiled gradient echo, and 2D phase contrast flow imaging. Artefacts were assessed using a semi-quantitative scale. Results: 7T CMR scans were acquired in all patients (100%) using the vectorcardiogram learning method. 3,142 R-waves were quantitatively analyzed, yielding sensitivity of 97.6% and specificity of 98.7%. Mean image quality score was 0.9, sufficient to quantitate both cardiac volumes, ejection fraction, and aortic and pulmonary blood flow. Mean left ventricular ejection fraction was 56.4%, right ventricular ejection fraction was 51.4%. Conclusion: Reliable cardiac ECG triggering is feasible in healthy volunteers at 7T utilizing a state-of-the-art three-lead trigger device despite signal distortion from the magnetohydrodynamic effect. This provides sufficient image quality for quantitative analysis. Other ultra-high-field imaging applications such as human brain functional MRI with physiologic noise correction may benefit from this method of ECG triggering.