Cardiac and Respiratory Self-Gating in Radial MRI Using an Adapted Singular Spectrum Analysis (SSA-FARY)

Free-running 5D whole-heart MRI for isotropic cardiac function measurements at 3T without contrast agents

PURPOSE

To optimize and characterize an interrupted 5D free-running framework at 3 T for detailed cardiac function assessment without the use of breath holding or contrast agents.

METHODS

A free-running 3D radial gradient echo sequence was periodically interrupted with aT 2 $$ {\mathrm{T}}_2 $$ preparation and a recovery module to optimize native blood-to-myocardium contrast at 3 T. Lipid signal was suppressed using a numerically optimized water-excitation RF pulse to reduce lipid streaking artifacts and to improve overall image quality. Optimal acquisition parameters were established for a 5-min scan time using extended phase graph simulations. A compressed sensing-based reconstruction incorporating cardiac and respiratory inter-bin deformation fields was employed to generate 5D images of the whole heart. The sharpness and contrast between the left ventricular blood pool and myocardium, along with the functional measurements of the left ventricle from the 5D datasets, were compared to routine 2D cine imaging in 16 healthy volunteers and three patients referred for clinically indicated CMR.

RESULTS

The proposed method resulted in lower contrast( 0 . 57 ± 0 . 12 $$ \Big(0.57\pm 0.12 $$ vs.2 . 09 ± 0 . 74 , p < 0 . 001 ) $$ 2.09\pm 0.74,p<0.001\Big) $$ and sharpness( 3 . 76 ± 1 . 11 mm $$ \Big(3.76\pm 1.11\kern0.3em \mathrm{mm} $$ vs.2 . 74 ± 0 . 95 mm , p < 0 . 001 ) $$ 2.74\pm 0.95\kern0.3em \mathrm{mm},p<0.001\Big) $$ , but enabled similar left-ventricle ejection fraction assessment( bias = 1 . 3 % $$ \Big(\mathrm{bias}=1.3\% $$ , limits ofagreement = [ - 3 . 3 % , 5 . 9 % ] $$ \mathrm{agreement}=\left[-3.3\%,5.9\%\right] $$ , intraclass correlationcoefficient = 0 . 87 , p = 0 . 03 ) $$ \mathrm{coefficient}=0.87,p=0.03\Big) $$ with high reproducibility compared to 2D cine.

CONCLUSION

The proposed contrast-free, interrupted free-running 5D imaging provides left ventricular functional assessments comparable to 2D cine at 3 T, while offering an improved patient experience through shorter scan times and free breathing.

Aorta Wall Stress during Exercise in Patients with an Ascending Thoracic Aortic Aneurysm: Insights from a Case Series

Individuals with ascending thoracic aortic aneurysm (ATAA) are recommended to avoid intense exercise for fear of marked increases in aortic wall stress (AWS). However, no study has measured AWS during exercise. The aim of this case series was to examine AWS during "light-to-moderate" aerobic exercise in individuals with ATAA and healthy control (CON) participants.Three clinically stable patients with ATAA (2 male, mean age: 74 ± 1 years) and 3 CON (2 male, mean age: 69 ± 7 years) were studied on 2 separate days. Day 1: a maximal cardiopulmonary exercise test was performed to measure peak aerobic power (VO2peak), maximal heart rate, and blood pressure (BP). Day 2: cardiac and aortic magnetic resonance imaging were performed at rest and during submaximal (3-5 metabolic equivalents) "stepper" exercise during which cardiac output (Qc), aorta diameters, wall thickness, and BP were measured. Circumferential ascending and descending AWS were calculated in accord with LaPlace Law, whereas aorta mechanical efficiency was derived as the AWS/Qc slope.Patients with ATAA demonstrated lower median VO2peak (18.2 vs. 24.1 mL/kg/min). During exercise, the absolute ascending (ATAA: 257 vs. CON: 269 kPa) and descending AWS increased (ATAA: 224 vs. CON: 207 kPa), and ∆AWS during exercise was similar between ATAA and CON (Ascending, ATAA: 79 vs. CON: 62 kPa; Descending, ATAA: 64 vs. CON: 55 kPa). During exercise, ascending and descending AWS were 76 to 83% below ATAA rupture thresholds (i.e., 800-1,200 kPa) in all patients. Finally, exercise Qc was 17% lower and the ascending AWS/Qc slope was 30% higher in ATAA (16 kPa/L/min) versus CON (12 kPa/L/min).Our findings demonstrate "light-to-moderate" aerobic exercise produces similar AWS responses between ATAA and CON and is well below aneurysmal rupture thresholds. The higher AWS/Qc slope in ATAA suggests decreased aortic mechanical efficiency and may be a useful measure for exercise prescription for these patients.

Exploring the limits of scan time reduction for ferumoxytol-enhanced whole-heart angiography in congenital heart disease patients

BACKGROUND

One major challenge in cardiovascular magnetic resonance is reducing scan times to be more compatible with clinical workflows. In 3D magnetic resonance imaging (MRI), strategies to shorten scan times mostly rely on ECG-triggering or self-navigation for motion management, but are affected by heart rate variabilities or respiratory drifts. A similarity-driven multi-dimensional binning algorithm (SIMBA) was introduced for 3D whole-heart angiography from ferumoxytol-enhanced free-running MRI. This study explores acceleration limits using SIMBA, and its compressed-sensing extension extra-dimensional motion-compensation (XD-MC)-SIMBA, while preserving image quality.

METHODS

Data from 6-min free-running acquisitions of 30 congenital heart disease (CHD) patients were retrospectively undersampled to simulate 5-, 4-, 3-, 2-, and 1-min datasets. SIMBA and XD-MC-SIMBA reconstructions were applied. and the consistency of the data selection together with sharpness metrics were computed as a function of undersampling. Image quality was rated on a 5-point Likert scale. Shorter 3-minute acquisitions were prospectively acquired in nine CHD patients.

RESULTS

SIMBA's motion state selection was consistent across undersampling levels, with only 2 of 30 cases showing completely different selections. Image quality metrics decreased with increased undersampling, with SIMBA scoring lower compared to XD-MC-SIMBA. The diagnostic quality was good, with lower scores for 2- and 1-min datasets. Using XD-MC-SIMBA, 43% (31/72) of cases showed improved scores compared to SIMBA and 58% (7/12) of 1-min datasets improved to good or excellent quality.

CONCLUSIONS

This study demonstrates that ferumoxytol-enhanced free-running MRI can be highly accelerated for 3D angiography in CHD.With the aid of compressed sensing, XD-MC-SIMBA supports the acceleration down to 3 minutes or less.

Rational approximation of golden angles: Accelerated reconstructions for radial MRI

PURPOSE

To develop a generic radial sampling scheme that combines the advantages of golden ratio sampling with simplicity of equidistant angular patterns. The irrational angle between consecutive spokes in golden ratio-based sampling schemes enables a flexible retrospective choice of temporal resolution, while preserving good coverage of k-space for each individual bin. Nevertheless, irrational increments prohibit precomputation of the point-spread function (PSF), can lead to numerical problems, and require more complex processing steps. To avoid these problems, a new sampling scheme based on a rational approximation of golden angles (RAGA) is developed.

METHODS

The theoretical properties of RAGA sampling are mathematically derived. Sidelobe-to-peak ratios (SPR) are numerically computed and compared to the corresponding golden ratio sampling schemes. The sampling scheme is implemented in the BART toolbox and in a radial gradient-echo sequence. Feasibility is shown for quantitative imaging in a phantom and a cardiac scan of a healthy volunteer.

RESULTS

RAGA sampling can accurately approximate golden ratio sampling and has almost identical PSF and SPR. In contrast to golden ratio sampling, each frame can be reconstructed with the same equidistant trajectory using different sampling masks, and the angle of each acquired spoke can be encoded as a small index, which simplifies processing of the acquired data.

CONCLUSION

RAGA sampling provides the advantages of golden ratio sampling while simplifying data processing, rendering it a valuable tool for dynamic and quantitative MRI.

Dixon-based B0 self-navigation in radial stack-of-stars multi-echo gradient echo imaging

PURPOSE

To develop a Dixon-basedB 0 $$ {\mathrm{B}}_0 $$ self-navigation approach to estimate and correct temporalB 0 $$ {\mathrm{B}}_0 $$ variations in radial stack-of-stars gradient echo imaging for quantitative body MRI.

METHODS

The proposed method estimates temporalB 0 $$ {\mathrm{B}}_0 $$ variations using aB 0 $$ {\mathrm{B}}_0 $$ self-navigator estimated by a graph-cut-based water-fat separation algorithm on the oversampled k-space center. TheB 0 $$ {\mathrm{B}}_0 $$ self-navigator was employed to correct for phase differences between radial spokes (one-dimensional [1D] correction) and to perform a motion-resolved reconstruction to correct spatiotemporal pseudo-periodicB 0 $$ {\mathrm{B}}_0 $$ variations (three-dimensional [3D] correction). Numerical simulations, phantom experiments and in vivo neck scans were performed to evaluate the effects of temporalB 0 $$ {\mathrm{B}}_0 $$ variations on the field-map, proton density fat fraction (PDFF) andT 2 ∗ $$ {\mathrm{T}}_2^{\ast } $$ map, and to validate the proposed method.

RESULTS

TemporalB 0 $$ {\mathrm{B}}_0 $$ variations were found to cause signal loss and phase shifts on the multi-echo images that lead to an underestimation ofT 2 ∗ $$ {\mathrm{T}}_2^{\ast } $$ , while PDFF mapping was less affected. TheB 0 $$ {\mathrm{B}}_0 $$ self-navigator captured slowly varying temporalB 0 $$ {\mathrm{B}}_0 $$ drifts and temporal variations caused by respiratory motion. While the 1D correction effectively correctedB 0 $$ {\mathrm{B}}_0 $$ drifts in phantom studies, it was insufficient in vivo due to 3D spatially varying temporalB 0 $$ {\mathrm{B}}_0 $$ variations with amplitudes of up to 25 Hz at 3 T near the lungs. The proposed 3D correction locally improved the correction of field-map andT 2 ∗ $$ {\mathrm{T}}_2^{\ast } $$ and reduced image artifacts.

CONCLUSION

TemporalB 0 $$ {\mathrm{B}}_0 $$ variations particularly affectT 2 ∗ $$ {\mathrm{T}}_2^{\ast } $$ mapping in radial stack-of-stars imaging. The self-navigation approach can be applied without modifying the MR acquisition to correct forB 0 $$ {\mathrm{B}}_0 $$ drift and physiological motion-inducedB 0 $$ {\mathrm{B}}_0 $$ variations, especially in the presence of fat.

Simultaneous whole-liver water T 1 and T 2 mapping with isotropic resolution during free-breathing

PURPOSE

To develop and validate a data acquisition scheme combined with a motion-resolved reconstruction and dictionary-matching-based parameter estimation to enable free-breathing isotropic resolution self-navigated whole-liver simultaneous water-specific T 1 ( wT 1 ) and T 2 ( wT 2 ) mapping for the characterization of diffuse and oncological liver diseases.

METHODS

The proposed data acquisition consists of a magnetization preparation pulse and a two-echo gradient echo readout with a radial stack-of-stars trajectory, repeated with different preparations to achieve different T 1 and T 2 contrasts in a fixed acquisition time of 6 min. Regularized reconstruction was performed using self-navigation to account for motion during the free-breathing acquisition, followed by water-fat separation. Bloch simulations of the sequence were applied to optimize the sequence timing forB 1 insensitivity at 3 T, to correct for relaxation-induced blurring, and to map T 1 and T 2 using a dictionary. The proposed method was validated on a water-fat phantom with varying relaxation properties and in 10 volunteers against imaging and spectroscopy reference values. The performance and robustness of the proposed method were evaluated in five patients with abdominal pathologies.

RESULTS

Simulations demonstrate goodB 1 insensitivity of the proposed method in measuring T 1 and T 2 values. The proposed method produces co-registered wT 1 and wT 2 maps with a good agreement with reference methods (phantom: wT 1 = 1 . 02 wT 1,ref - 8 . 93 ms , R 2 = 0 . 991 ; wT 2 = 1 . 03 wT 2,ref + 0 . 73 ms , R 2 = 0 . 995 ). The proposed wT 1 and wT 2 mapping exhibits good repeatability and can be robustly performed in patients with pathologies.

CONCLUSIONS

The proposed method allows whole-liver wT 1 and wT 2 quantification with high accuracy at isotropic resolution in a fixed acquisition time during free-breathing.

Self-navigated subspace reconstruction for real-time MR imaging of the vocal tract

PURPOSE

Real-time MRI offers a continuous and dynamic view of the object being imaged. Researchers have applied real-time MRI to speech production, which allows for the visualization of the vocal tract during speech.

METHODS

This study proposed applying self-navigated subspace reconstruction for real-time vocal tract imaging. We performed experiments on a clinical 3 T MRI using standard RF coils and rapid acquisition. Additionally, 1000 frames were compressed during reconstruction to a few principal components, and iterative low-rank approximation was performed on compressed k-space, in conjunction with the orthogonal basis estimation for the subspace.

RESULTS

The simulation study involving a 32-time acceleration showed that the proposed method produced a reasonably small root mean square error (RMSE) of 0.159, compared to 0.278 for sliding window reconstruction, 0.2527 for SToRM and 0.294 for low-rank reconstruction. The study also presented in vivo images of a typical sagittal image with a temporal resolution of 7 ms/frame or 21 ms/frame for the three-slice scan.

CONCLUSION

Our study presented a subspace reconstruction technique that does not require a navigator echo, which can be used for real-time MRI, particularly in speech imaging applications.

Similarity-driven motion-resolved reconstruction for ferumoxytol-enhanced whole-heart MRI in congenital heart disease

A similarity-driven multi-dimensional binning algorithm (SIMBA) reconstruction of free-running cardiac magnetic resonance imaging data was previously proposed. While very efficient and fast, the original SIMBA focused only on the reconstruction of a single motion-consistent cluster, discarding the remaining data acquired. However, the redundant data clustered by similarity may be exploited to further improve image quality. In this work, we propose a novel compressed sensing (CS) reconstruction that performs an effective regularization over the clustering dimension, thanks to the integration of inter-cluster motion compensation (XD-MC-SIMBA). This reconstruction was applied to free-running ferumoxytol-enhanced datasets from 24 patients with congenital heart disease, and compared to the original SIMBA, the same XD-MC-SIMBA reconstruction but without motion compensation (XD-SIMBA), and a 5D motion-resolved CS reconstruction using the free-running framework (FRF). The resulting images were compared in terms of lung-liver and blood-myocardium sharpness, blood-myocardium contrast ratio, and visible length and sharpness of the coronary arteries. Moreover, an automated image quality score (IQS) was assigned using a pretrained deep neural network. The lung-liver sharpness and blood-myocardium sharpness were significantly higher in XD-MC-SIMBA and FRF. Consistent with these findings, the IQS analysis revealed that image quality for XD-MC-SIMBA was improved in 18 of 24 cases, compared to SIMBA. We successfully tested the hypothesis that multiple motion-consistent SIMBA clusters can be exploited to improve the quality of ferumoxytol-enhanced cardiac MRI when inter-cluster motion-compensation is integrated as part of a CS reconstruction.

ECG-free cine MRI with data-driven clustering of cardiac motion for quantification of ventricular function

BACKGROUND

Despite the widespread use of cine MRI for evaluation of cardiac function, existing real-time methods do not easily enable quantification of ventricular function. Moreover, segmented cine MRI assumes periodicity of cardiac motion. We aim to develop a self-gated, cine MRI acquisition scheme with data-driven cluster-based binning of cardiac motion.

METHODS

A Cartesian golden-step balanced steady-state free precession sequence with sorted k-space ordering was designed. Image data were acquired with breath-holding. Principal component analysis and k-means clustering were used for binning of cardiac phases. Cluster compactness in the time dimension was assessed using temporal variability, and dispersion in the spatial dimension was assessed using the Caliński-Harabasz index. The proposed and the reference electrocardiogram (ECG)-gated cine methods were compared using a four-point image quality score, SNR and CNR values, and Bland-Altman analyses of ventricular function.

RESULTS

A total of 10 subjects with sinus rhythm and 8 subjects with arrhythmias underwent cardiac MRI at 3.0 T. The temporal variability was 45.6 ms (cluster) versus 24.6 ms (ECG-based) (p < 0.001), and the Caliński-Harabasz index was 59.1 ± 9.1 (cluster) versus 22.0 ± 7.1 (ECG based) (p < 0.001). In subjects with sinus rhythm, 100% of the end-systolic and end-diastolic images from both the cluster and reference approach received the highest image quality score of 4. Relative to the reference cine images, the cluster-based multiphase (cine) image quality consistently received a one-point lower score (p < 0.05), whereas the SNR and CNR values were not significantly different (p = 0.20). In cases with arrhythmias, 97.9% of the end-systolic and end-diastolic images from the cluster approach received an image quality score of 3 or more. The mean bias values for biventricular ejection fraction and volumes derived from the cluster approach versus reference cine were negligible.

CONCLUSION

ECG-free cine cardiac MRI with data-driven clustering for binning of cardiac motion is feasible and enables quantification of cardiac function.

Rapid whole brain 3D T2 mapping respiratory-resolved Double-Echo Steady State (DESS) sequence with improved repeatability

PURPOSE

To propose a quantitative 3D double-echo steady-state (DESS) sequence that offers rapid and repeatable T2 mapping of the human brain using different encoding schemes that account for respiratory B0 variation.

METHODS

A retrospective self-gating module was firstly implemented into the standard DESS sequence in order to suppress the respiratory artifact via data binning. A compressed-sensing trajectory (CS-DESS) was then optimized to accelerate the acquisition. Finally, a spiral Cartesian encoding (SPICCS-DESS) was incorporated to further disrupt the coherent respiratory artifact. These different versions were compared to a standard DESS sequence (fully DESS) by assessing the T2 distribution and repeatability in different brain regions of eight volunteers at 3 T.

RESULTS

The respiratory artifact correction was determined to be optimal when the data was binned into seven respiratory phases. Compared to the fully DESS, T2 distribution was improved for the CS-DESS and SPICCS-DESS with interquartile ranges reduced significantly by a factor ranging from 2 to 12 in the caudate, putamen, and thalamus regions. In the gray and white matter areas, average absolute test-retest T2 differences across all volunteers were respectively 3.5 ± 2% and 3.1 ± 2.1% for the SPICCS-DESS, 4.6 ± 4.6% and 4.9 ± 5.1% for the CS-DESS, and 15% ± 13% and 7.3 ± 5.6% for the fully DESS. The SPICCS-DESS sequence's acquisition time could be reduced by half (<4 min) while maintaining its efficient T2 mapping.

CONCLUSION

The respiratory-resolved SPICCS-DESS sequence offers rapid, robust, and repeatable 3D T2 mapping of the human brain, which can be especially effective for longitudinal monitoring of cerebral pathologies.

Free-Breathing Liver Fat, R₂* and B₀ Field Mapping Using Multi-Echo Radial FLASH and Regularized Model-Based Reconstruction

This work introduced a stack-of-radial multi-echo asymmetric-echo MRI sequence for free-breathing liver volumetric acquisition. Regularized model-based reconstruction was implemented in Berkeley Advanced Reconstruction Toolbox (BART) to jointly estimate all physical parameter maps (water, fat, R2∗ , and B0 field inhomogeneity maps) and coil sensitivity maps from self-gated k -space data. Specifically, locally low rank and temporal total variation regularization were employed directly on physical parameter maps. The proposed free-breathing radial technique was tested on a water/fat & iron phantom, a young volunteer, and obesity/diabetes/hepatic steatosis patients. Quantitative fat fraction and R2∗ accuracy were confirmed by comparing our technique with the reference breath-hold Cartesian scan. The multi-echo radial sampling sequence achieves fast k -space coverage and is robust to motion. Moreover, the proposed motion-resolved model-based reconstruction allows for free-breathing liver fat and R2∗ quantification in multiple motion states. Overall, our proposed technique offers a convenient tool for non-invasive liver assessment with no breath holding requirement.

Free-breathing myocardial T1 mapping using inversion-recovery radial FLASH and motion-resolved model-based reconstruction

PURPOSE

To develop a free-breathing myocardialT 1 $$ {\mathrm{T}}_1 $$ mapping technique using inversion-recovery (IR) radial fast low-angle shot (FLASH) and calibrationless motion-resolved model-based reconstruction.

METHODS

Free-running (free-breathing, retrospective cardiac gating) IR radial FLASH is used for data acquisition at 3T. First, to reduce the waiting time between inversions, an analytical formula is derived that takes the incompleteT 1 $$ {\mathrm{T}}_1 $$ recovery into account for an accurateT 1 $$ {\mathrm{T}}_1 $$ calculation. Second, the respiratory motion signal is estimated from the k-space center of the contrast varying acquisition using an adapted singular spectrum analysis (SSA-FARY) technique. Third, a motion-resolved model-based reconstruction is used to estimate both parameter and coil sensitivity maps directly from the sorted k-space data. Thus, spatiotemporal total variation, in addition to the spatial sparsity constraints, can be directly applied to the parameter maps. Validations are performed on an experimental phantom, 11 human subjects, and a young landrace pig with myocardial infarction.

RESULTS

In comparison to an IR spin-echo reference, phantom results confirm goodT 1 $$ {\mathrm{T}}_1 $$ accuracy, when reducing the waiting time from 5 s to 1 s using the new correction. The motion-resolved model-based reconstruction further improvesT 1 $$ {\mathrm{T}}_1 $$ precision compared to the spatial regularization-only reconstruction. Aside from showing that a reliable respiratory motion signal can be estimated using modified SSA-FARY, in vivo studies demonstrate that dynamic myocardialT 1 $$ {\mathrm{T}}_1 $$ maps can be obtained within 2 min with good precision and repeatability.

CONCLUSION

Motion-resolved myocardialT 1 $$ {\mathrm{T}}_1 $$ mapping during free-breathing with good accuracy, precision and repeatability can be achieved by combining inversion-recovery radial FLASH, self-gating and a calibrationless motion-resolved model-based reconstruction.

Variational Manifold Learning From Incomplete Data: Application to Multislice Dynamic MRI

Current deep learning-based manifold learning algorithms such as the variational autoencoder (VAE) require fully sampled data to learn the probability density of real-world datasets. However, fully sampled data is often unavailable in a variety of problems, including the recovery of dynamic and high-resolution magnetic resonance imaging (MRI). We introduce a novel variational approach to learn a manifold from undersampled data. The VAE uses a decoder fed by latent vectors, drawn from a conditional density estimated from the fully sampled images using an encoder. Since fully sampled images are not available in our setting, we approximate the conditional density of the latent vectors by a parametric model whose parameters are estimated from the undersampled measurements using back-propagation. We use the framework for the joint alignment and recovery of multi-slice free breathing and ungated cardiac MRI data from highly undersampled measurements. Experimental results demonstrate the utility of the proposed scheme in dynamic imaging alignment and reconstructions.

Deep learning model for automatic segmentation of lungs and pulmonary metastasis in small animal MR images

Lungs are the most frequent site of metastases growth. The amount and size of pulmonary metastases acquired from MRI imaging data are the important criteria to assess the efficacy of new drugs in preclinical models. While efficient solutions both for MR imaging and the downstream automatic segmentation have been proposed for human patients, both MRI lung imaging and segmentation in preclinical animal models remains challenging due to the physiological motion (respiratory and cardiac movements), to the low amount of protons in this organ and to the particular challenge of precise segmentation of metastases. As a consequence post-mortem analysis is currently required to obtain information on metastatic volume. In this work, we have developed a complete methodological pipeline for automated analysis of lungs and metastases in mice, consisting of an MR sequence for image acquisition and a deep learning method for automatic segmentation of both lungs and metastases. On one hand, we optimized an MR sequence for mouse lung imaging with high contrast for high detection sensitivity. On the other hand we developed DeepMeta, a multiclass U-Net 3+ deep learning model to automatically segment the images. To assess if the proposed deep learning pipeline is able to provide an accurate segmentation of both lungs and pulmonary metastases, we have longitudinally imaged mice with fast- and slow-growing metastasis. Fifty-five balb/c mice were injected with two different derivatives of renal carcinoma cells. Mice were imaged with a SG-bSSFP (self-gated balanced steady state free precession) sequence at different time points after the injection of cancer cells. Both lung and metastases segmentations were manually performed by experts. DeepMeta was trained to perform lung and metastases segmentation based on the resulting ground truth annotations. Volumes of lungs and of pulmonary metastases as well as the number of metastases per mouse were measured on a separate test dataset of MR images. Thanks to the SG method, the 3D bSSFP images of lungs were artifact-free, enabling the downstream detection and serial follow-up of metastases. Moreover, both lungs and metastases segmentation was accurately performed by DeepMeta as soon as they reached the volume of ∼ 0.02 m m 3 . Thus we were able to distinguish two groups of mice in terms of number and volume of pulmonary metastases as well as in terms of the slow versus fast patterns of growth of metastases. We have shown that our methodology combining SG-bSSFP with deep learning, enables processing of the whole animal lungs and is thus a viable alternative to histology alone.

Separation of cardiogenic oscillations from airflow waveforms using singular spectrum analysis

BACKGROUND AND OBJECTIVE

Airflow fluctuations caused by cardiac contraction can trigger inappropriate ventilator pressure support in anesthesia machines and intensive care unit mechanical ventilators. Removal of this cardiogenic artifact from the airflow signal would improve ventilator function. The application of singular spectrum analysis (SSA) to remove cardiogenic oscillations from ventilator airflow signals recorded from intubated, mechanically ventilated patients under general anesthesia was evaluated in this study.

METHODS

Airflow (liters/minute) and CO₂ (mmHg) data were collected at a sampling rate of 125 Hz from the intraoperative monitoring systems using special-purpose software. Simultaneous electrocardiogram signals (mV) were also collected at a sampling rate of 250 Hz. One-dimensional SSA was performed offline on normalized airflow signals using a window length sufficient to span one period of typical respiratory variation. The main components of the airflow waveform are respiratory excursions and cardiogenic oscillations, with respiratory excursions more slowly varying and of higher magnitude. The smooth respiratory waveform was formed from elementary reconstructed series corresponding to the highest singular values obtained with SSA analysis. The quality of respiratory waveform extraction with SSA was determined by calculating the weighted correlation between the selected elementary reconstructed series.

RESULTS

Airflow data was recorded from 6 patients. The respiratory component of the airflow signal without cardiogenic oscillations was reconstructed from elementary series corresponding to singular values of highest magnitude. The weighted correlations obtained were greater than 0.96 in the majority of patients studied. Cardiogenic oscillations were reconstructed from elementary reconstructed series corresponding to singular values of lower magnitude.

CONCLUSIONS

SSA is effective in extracting higher amplitude respiratory excursions while excluding lower amplitude cardiogenic oscillations and noise from the airflow signal. This study demonstrates that suppression of the cardiogenic artefact with SSA is computationally feasible to augment ventilator performance.

Recent Advances in Radio-Frequency Coil Technologies: Flexible, Wireless, and Integrated Coil Arrays

Radio-frequency (RF) coils are to magnetic resonance imaging (MRI) scanners what eyes are to the human body. Because of their critical importance, there have been constant innovations driving the rapid development of RF coil technologies. Over the past four decades, the breadth and depth of the RF coil technology evolution have far exceeded the space allowed for this review article. However, these past developments have laid the very foundation on which some of the recent technical breakthroughs are built upon. Here, we narrow our focus on some of the most recent RF coil advances, specifically, on flexible, wireless, and integrated coil arrays. To provide a detailed review, we discuss the theoretical underpinnings, experimental implementations, promising results, as well as future outlooks covering these exciting topics. These recent innovations have greatly improved patient comfort and ease of scan, while also increasing the signal-to-noise ratio, image resolution, temporal throughput, and diagnostic and treatment accuracy. Together with advances in other MRI subfields, they will undoubtedly continue to drive the field forward and lead us to an ever more exciting future. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 1.

Cardiac MR: From Theory to Practice

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.

Energetic Basis for Exercise-Induced Pulmonary Congestion in Heart Failure With Preserved Ejection Fraction

BACKGROUND

Transient pulmonary congestion during exercise is emerging as an important determinant of reduced exercise capacity in heart failure with preserved ejection fraction (HFpEF). We sought to determine whether an abnormal cardiac energetic state underpins this process.

METHODS

We recruited patients across the spectrum of diastolic dysfunction and HFpEF (controls, n=11; type 2 diabetes, n=9; HFpEF, n=14; and severe diastolic dysfunction attributable to cardiac amyloidosis, n=9). Cardiac energetics were measured using phosphorus spectroscopy to define the myocardial phosphocreatine to ATP ratio. Cardiac function was assessed by cardiovascular magnetic resonance cine imaging and echocardiography and lung water using magnetic resonance proton density mapping. Studies were performed at rest and during submaximal exercise using a magnetic resonance imaging ergometer.

RESULTS

Paralleling the stepwise decline in diastolic function across the groups (E/e' ratio; P<0.001) was an increase in NT-proBNP (N-terminal pro-brain natriuretic peptide; P<0.001) and a reduction in phosphocreatine/ATP ratio (control, 2.15 [2.09, 2.29]; type 2 diabetes, 1.71 [1.61, 1.91]; HFpEF, 1.66 [1.44, 1.89]; cardiac amyloidosis, 1.30 [1.16, 1.53]; P<0.001). During 20-W exercise, lower left ventricular diastolic filling rates (r=0.58; P<0.001), lower left ventricular diastolic reserve (r=0.55; P<0.001), left atrial dilatation (r=-0.52; P<0.001), lower right ventricular contractile reserve (right ventricular ejection fraction change, r=0.57; P<0.001), and right atrial dilation (r=-0.71; P<0.001) were all linked to lower phosphocreatine/ATP ratio. Along with these changes, pulmonary proton density mapping revealed transient pulmonary congestion in patients with HFpEF (+4.4% [0.5, 6.4]; P=0.002) and cardiac amyloidosis (+6.4% [3.3, 10.0]; P=0.004), which was not seen in healthy controls (-0.1% [-1.9, 2.1]; P=0.89) or type 2 diabetes without HFpEF (+0.8% [-1.7, 1.9]; P=0.82). The development of exercise-induced pulmonary congestion was associated with lower phosphocreatine/ATP ratio (r=-0.43; P=0.004).

CONCLUSIONS

A gradient of myocardial energetic deficit exists across the spectrum of HFpEF. Even at low workload, this energetic deficit is related to markedly abnormal exercise responses in all 4 cardiac chambers, which is associated with detectable pulmonary congestion. The findings support an energetic basis for transient pulmonary congestion in HFpEF.

Deep Learning-Based ECG-Free Cardiac Navigation for Multi-Dimensional and Motion-Resolved Continuous Magnetic Resonance Imaging

For the clinical assessment of cardiac vitality, time-continuous tomographic imaging of the heart is used. To further detect e.g., pathological tissue, multiple imaging contrasts enable a thorough diagnosis using magnetic resonance imaging (MRI). For this purpose, time-continous and multi-contrast imaging protocols were proposed. The acquired signals are binned using navigation approaches for a motion-resolved reconstruction. Mostly, external sensors such as electrocardiograms (ECG) are used for navigation, leading to additional workflow efforts. Recent sensor-free approaches are based on pipelines requiring prior knowledge, e.g., typical heart rates. We present a sensor-free, deep learning-based navigation that diminishes the need for manual feature engineering or the necessity of prior knowledge compared to previous works. A classifier is trained to estimate the R-wave timepoints in the scan directly from the imaging data. Our approach is evaluated on 3-D protocols for continuous cardiac MRI, acquired in-vivo and free-breathing with single or multiple imaging contrasts. We achieve an accuracy of > 98% on previously unseen subjects, and a well comparable image quality with the state-of-the-art ECG-based reconstruction. Our method enables an ECG-free workflow for continuous cardiac scans with simultaneous anatomic and functional imaging with multiple contrasts. It can be potentially integrated without adapting the sampling scheme to other continuous sequences by using the imaging data for navigation and reconstruction.

Fully self-gated whole-heart 4D flow imaging from a 5-minute scan

PURPOSE

To develop and validate an acquisition and processing technique that enables fully self-gated 4D flow imaging with whole-heart coverage in a fixed 5-minute scan.

THEORY AND METHODS

The data are acquired continuously using Cartesian sampling and sorted into respiratory and cardiac bins using the self-gating signal. The reconstruction is performed using a recently proposed Bayesian method called ReVEAL4D. ReVEAL4D is validated using data from 8 healthy volunteers and 2 patients and compared with compressed sensing technique, L1-SENSE.

RESULTS

Healthy subjects-Compared with 2D phase-contrast MRI (2D-PC), flow quantification from ReVEAL4D shows no significant bias. In contrast, the peak velocity and peak flow rate for L1-SENSE are significantly underestimated. Compared with traditional parallel MRI-based 4D flow imaging, ReVEAL4D demonstrates small but significant biases in net flow and peak flow rate, with no significant bias in peak velocity. All 3 indices are significantly and more markedly underestimated by L1-SENSE. Patients-Flow quantification from ReVEAL4D agrees well with the 2D-PC reference. In contrast, L1-SENSE markedly underestimated peak velocity.

CONCLUSIONS

The combination of highly accelerated 5-minute Cartesian acquisition, self-gating, and ReVEAL4D enables whole-heart 4D flow imaging with accurate flow quantification.