Detecting myocardial ischemia at rest with cardiac phase-resolved blood oxygen level-dependent cardiovascular magnetic resonance

Trajectory correction enables free-running chemical shift encoded imaging for accurate cardiac proton-density fat fraction quantification at 3T

BACKGROUND

Metabolic diseases can negatively alter epicardial fat accumulation and composition, which can be probed using quantitative cardiac chemical shift encoded (CSE) cardiovascular magnetic resonance (CMR) by mapping proton-density fat fraction (PDFF). To obtain motion-resolved high-resolution PDFF maps, we proposed a free-running cardiac CSE-CMR framework at 3T. To employ faster bipolar readout gradients, a correction for gradient imperfections was added using the gradient impulse response function (GIRF) and evaluated on intermediate images and PDFF quantification.

METHODS

Ten minutes free-running cardiac 3D radial CSE-CMR acquisitions were compared in vitro and in vivo at 3T. Monopolar and bipolar readout gradient schemes provided 8 echoes (TE1/ΔTE = 1.16/1.96 ms) and 13 echoes (TE1/ΔTE = 1.12/1.07 ms), respectively. Bipolar-gradient free-running cardiac fat and water images and PDFF maps were reconstructed with or without GIRF correction. PDFF values were evaluated in silico, in vitro on a fat/water phantom, and in vivo in 10 healthy volunteers and 3 diabetic patients.

RESULTS

In monopolar mode, fat-water swaps were demonstrated in silico and confirmed in vitro. Using bipolar readout gradients, PDFF quantification was reliable and accurate with GIRF correction with a mean bias of 0.03% in silico and 0.36% in vitro while it suffered from artifacts without correction, leading to a PDFF bias of 4.9% in vitro and swaps in vivo. Using bipolar readout gradients, in vivo PDFF of epicardial adipose tissue was significantly lower compared to subcutaneous fat (80.4 ± 7.1% vs 92.5 ± 4.3%, P < 0.0001).

CONCLUSIONS

Aiming for an accurate PDFF quantification, high-resolution free-running cardiac CSE-MRI imaging proved to benefit from bipolar echoes with k-space trajectory correction at 3T. This free-breathing acquisition framework enables to investigate epicardial adipose tissue PDFF in metabolic diseases.

Cardiac MRI at Low Field Strengths

Cardiac MR imaging is well established for assessment of cardiovascular structure and function, myocardial scar, quantitative flow, parametric mapping, and myocardial perfusion. Despite the clear evidence supporting the use of cardiac MRI for a wide range of indications, it is underutilized clinically. Recent developments in low-field MRI technology, including modern data acquisition and image reconstruction methods, are enabling high-quality low-field imaging that may improve the cost-benefit ratio for cardiac MRI. Studies to-date confirm that low-field MRI offers high measurement concordance and consistent interpretation with clinical imaging for several routine sequences. Moreover, low-field MRI may enable specific new clinical opportunities for cardiac imaging such as imaging near metal implants, MRI-guided interventions, combined cardiopulmonary assessment, and imaging of patients with severe obesity. In this review, we discuss the recent progress in low-field cardiac MRI with a focus on technical developments and early clinical validation studies. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 1.

Disentangle, Align and Fuse for Multimodal and Semi-Supervised Image Segmentation

Magnetic resonance (MR) protocols rely on several sequences to assess pathology and organ status properly. Despite advances in image analysis, we tend to treat each sequence, here termed modality, in isolation. Taking advantage of the common information shared between modalities (an organ's anatomy) is beneficial for multi-modality processing and learning. However, we must overcome inherent anatomical misregistrations and disparities in signal intensity across the modalities to obtain this benefit. We present a method that offers improved segmentation accuracy of the modality of interest (over a single input model), by learning to leverage information present in other modalities, even if few (semi-supervised) or no (unsupervised) annotations are available for this specific modality. Core to our method is learning a disentangled decomposition into anatomical and imaging factors. Shared anatomical factors from the different inputs are jointly processed and fused to extract more accurate segmentation masks. Image misregistrations are corrected with a Spatial Transformer Network, which non-linearly aligns the anatomical factors. The imaging factor captures signal intensity characteristics across different modality data and is used for image reconstruction, enabling semi-supervised learning. Temporal and slice pairing between inputs are learned dynamically. We demonstrate applications in Late Gadolinium Enhanced (LGE) and Blood Oxygenation Level Dependent (BOLD) cardiac segmentation, as well as in T2 abdominal segmentation. Code is available at https://github.com/vios-s/multimodal_segmentation.

Blood Oxygen Level-Dependent MRI of the Myocardium with Multiecho Gradient-Echo Spin-Echo Imaging

Background Myocardial oxygenation imaging could help determine the presence of microvascular dysfunction associated with increased cardiovascular risk. However, it is challenging to depict the potentially small oxygenation alterations with current noninvasive cardiac MRI blood oxygen level-dependent (BOLD) techniques. Purpose To demonstrate the cardiac application of a gradient-echo spin-echo (GESE) echo-planar imaging sequence for dynamic and quantitative heartbeat-to-heartbeat BOLD MRI and evaluate the sequence in populations both healthy and with hypertension in combination with a breath hold-induced CO2 intervention. Materials and Methods GESE echo-planar imaging sequence was performed in 18 healthy participants and in eight prospectively recruited participants with hypertension on a 3.0-T MRI system. T2 and T2* maps were calculated per heartbeat with a four-parameter fitting technique. Septal regions of interests were used to determine T2 and T2* values per heartbeat and examined over the course of a breath hold to determine BOLD changes. T2 and T2* changes of healthy participants and participants with hypertension were compared by using a nonparametric Mann-Whitney test. Results GESE echo-planar imaging approach gave spatially stable T2 and T2* maps per heartbeat for healthy participants and participants with hypertension, with mean T2 values of 43 msec ± 5 (standard deviation) and 46 msec ± 9, respectively, and mean T2* values of 28 msec ± 5 and 22 msec ± 5, respectively. The healthy participants exhibited increasing T2 and T2* values over the course of a breath hold with a mean positive slope of 0.2 msec per heartbeat ± 0.1 for T2 and 0.2 msec per heartbeat ± 0.1 for T2*, whereas for participants with hypertension these dynamic T2 and T2* values had a mean negative slope of -0.2 msec per heartbeat ± 0.2 for T2 and -0.1 msec per heartbeat ± 0.2 for T2*. The difference in these mean slopes between healthy participants and participants with hypertension was significant for both T2 (P < .001) and T2* (P < .001). Conclusion Gradient-echo spin-echo echo-planar imaging sequence provided quantitative T2 and T2* maps per heartbeat and enabled dynamic heartbeat-to-heartbeat blood oxygen level-dependent (BOLD)-response imaging by analyzing changes in T2 and T2* over the time of a breath-hold intervention. This approach could identify differences in the BOLD response between healthy participants and participants with hypertension. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Friedrich in this issue.

The role of M-mode echocardiography in patients with heart failure and preserved ejection fraction: A prospective cohort study

Epicardial movement during diastole is inversely proportional to myocardial stiffness but systolic regional thickening cannot precisely identify ischemic territories. The aim of the present study was to test the hypothesis that a correlation may be present between M-mode echocardiography parameters and poor outcomes in patients with heart failure and preserved ejection fraction. Patients with known cardiovascular disease were included in the test group (n=1,244) and patients without known cardiovascular disease were included in the control group (n=1,952). Patient records of routine measurements, M-mode echocardiography and mortality were collected. The control population and test population had the same left ventricular end-diastolic dimension (P=0.062) and left ventricular end-diastolic volume (P=0.053). A lower mitral flow velocity (P<0.05), higher Tei index (P<0.0001) and reduced distribution of diastolic wall strain (P<0.0001) were reported in the test populations compared with the control population. Patients of the test population with lower diastolic wall strain (<0.28) demonstrated a higher mortality rate than those with higher diastolic wall strain (≥0.28; P<0.0001) at the 3-year follow-up. M-mode echocardiographic parameters may be of use for predicting poor outcomes in patients with heart failure and preserved ejection fraction.

Disentangled representation learning in cardiac image analysis

Typically, a medical image offers spatial information on the anatomy (and pathology) modulated by imaging specific characteristics. Many imaging modalities including Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) can be interpreted in this way. We can venture further and consider that a medical image naturally factors into some spatial factors depicting anatomy and factors that denote the imaging characteristics. Here, we explicitly learn this decomposed (disentangled) representation of imaging data, focusing in particular on cardiac images. We propose Spatial Decomposition Network (SDNet), which factorises 2D medical images into spatial anatomical factors and non-spatial modality factors. We demonstrate that this high-level representation is ideally suited for several medical image analysis tasks, such as semi-supervised segmentation, multi-task segmentation and regression, and image-to-image synthesis. Specifically, we show that our model can match the performance of fully supervised segmentation models, using only a fraction of the labelled images. Critically, we show that our factorised representation also benefits from supervision obtained either when we use auxiliary tasks to train the model in a multi-task setting (e.g. regressing to known cardiac indices), or when aggregating multimodal data from different sources (e.g. pooling together MRI and CT data). To explore the properties of the learned factorisation, we perform latent-space arithmetic and show that we can synthesise CT from MR and vice versa, by swapping the modality factors. We also demonstrate that the factor holding image specific information can be used to predict the input modality with high accuracy. Code will be made available at https://github.com/agis85/anatomy_modality_decomposition.

Unsupervised Myocardial Segmentation for Cardiac BOLD

A fully automated 2-D+time myocardial segmentation framework is proposed for cardiac magnetic resonance (CMR) blood-oxygen-level-dependent (BOLD) data sets. Ischemia detection with CINE BOLD CMR relies on spatio-temporal patterns in myocardial intensity, but these patterns also trouble supervised segmentation methods, the de facto standard for myocardial segmentation in cine MRI. Segmentation errors severely undermine the accurate extraction of these patterns. In this paper, we build a joint motion and appearance method that relies on dictionary learning to find a suitable subspace. Our method is based on variational pre-processing and spatial regularization using Markov random fields, to further improve performance. The superiority of the proposed segmentation technique is demonstrated on a data set containing cardiac phase-resolved BOLD MR and standard CINE MR image sequences acquired in baseline and ischemic condition across ten canine subjects. Our unsupervised approach outperforms even supervised state-of-the-art segmentation techniques by at least 10% when using Dice to measure accuracy on BOLD data and performs at par for standard CINE MR. Furthermore, a novel segmental analysis method attuned for BOLD time series is utilized to demonstrate the effectiveness of the proposed method in preserving key BOLD patterns.

Functional and anatomical characterization of brown adipose tissue in heart failure with blood oxygen level dependent magnetic resonance

Recent studies have suggested that brown adipose tissue (BAT) plays an important role in obesity, insulin resistance and heart failure. The characterization of BAT in vivo, however, has been challenging. No technique to comprehensively image BAT anatomy and function has been described. Moreover, the impact on BAT of the neuroendocrine activation seen in heart failure has only recently begun to be evaluated in vivo. The aim of this study was to use MRI to characterize the impact of heart failure on the morphology and function of BAT. Mice subjected to permanent ligation of the left coronary artery were imaged with MRI 6 weeks later. T2 weighted MRI of BAT volume and blood oxygen level dependent MRI of BAT function were performed. T2 * maps of BAT were obtained at multiple time points before and after administration of the β3 adrenergic agonist CL 316 243 (CL). Blood flow to BAT was studied after CL injection using the flow alternating inversion recovery (FAIR) approach. Excised BAT tissue was analyzed for lipid droplet content and for uncoupling protein 1 (UCP1) mRNA expression. BAT volume was significantly lower in heart failure (51 ± 1 mm(3) versus 65 ± 3 mm(3) ; p < 0.05), and characterized by a reduction in lipid globules and a fourfold increase in UCP1 mRNA (p < 0.05). CL injection increased BAT T2 * in healthy animals but not in mice with heart failure (24 ± 4% versus 6 ± 2%; p < 0.01), consistent with an increase in flow in control BAT. This was confirmed by a significant difference in the FAIR response in BAT in control and heart failure mice. Heart failure results in the chronic activation of BAT, decreased BAT lipid stores and decreased BAT volume, and it is associated with a marked decrease in ability to respond to acute physiological stimuli. This may have important implications for substrate utilization and overall metabolic homeostasis in heart failure. Copyright © 2016 John Wiley & Sons, Ltd.

Myocardial effective transverse relaxation time T2* Correlates with left ventricular wall thickness: A 7.0 T MRI study

PURPOSE

Myocardial effective relaxation time T2* is commonly regarded as a surrogate for myocardial tissue oxygenation. However, it is legitimate to assume that there are multiple factors that influence T2*. To this end, this study investigates the relationship between T2* and cardiac macromorphology given by left ventricular (LV) wall thickness and left ventricular radius, and provides interpretation of the results in the physiological context.

METHODS

High spatio-temporally resolved myocardial CINE T2* mapping was performed in 10 healthy volunteers using a 7.0 Tesla (T) full-body MRI system. Ventricular septal wall thickness, left ventricular inner radius, and T2* were analyzed. Macroscopic magnetic field changes were elucidated using cardiac phase-resolved magnetic field maps.

RESULTS

Ventricular septal T2* changes periodically over the cardiac cycle, increasing in systole and decreasing in diastole. Ventricular septal wall thickness and T2* showed a significant positive correlation, whereas the inner LV radius and T2* were negatively correlated. The effect of macroscopic magnetic field gradients on T2* can be considered minor in the ventricular septum.

CONCLUSION

Our findings suggest that myocardial T2* is related to tissue blood volume fraction. Temporally resolved T2* mapping could be beneficial for myocardial tissue characterization and for understanding cardiac (patho)physiology in vivo. Magn Reson Med 77:2381-2389, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Dictionary-Driven Ischemia Detection From Cardiac Phase-Resolved Myocardial BOLD MRI at Rest

Cardiac Phase-resolved Blood-Oxygen-Level Dependent (CP-BOLD) MRI provides a unique opportunity to image an ongoing ischemia at rest. However, it requires post-processing to evaluate the extent of ischemia. To address this, here we propose an unsupervised ischemia detection (UID) method which relies on the inherent spatio-temporal correlation between oxygenation and wall motion to formalize a joint learning and detection problem based on dictionary decomposition. Considering input data of a single subject, it treats ischemia as an anomaly and iteratively learns dictionaries to represent only normal observations (corresponding to myocardial territories remote to ischemia). Anomaly detection is based on a modified version of One-class Support Vector Machines (OCSVM) to regulate directly the margins by incorporating the dictionary-based representation errors. A measure of ischemic extent (IE) is estimated, reflecting the relative portion of the myocardium affected by ischemia. For visualization purposes an ischemia likelihood map is created by estimating posterior probabilities from the OCSVM outputs, thus obtaining how likely the classification is correct. UID is evaluated on synthetic data and in a 2D CP-BOLD data set from a canine experimental model emulating acute coronary syndromes. Comparing early ischemic territories identified with UID against infarct territories (after several hours of ischemia), we find that IE, as measured by UID, is highly correlated (Pearson's r=0.84) with respect to infarct size. When advances in automated registration and segmentation of CP-BOLD images and full coverage 3D acquisitions become available, we hope that this method can enable pixel-level assessment of ischemia with this truly non-invasive imaging technique.

Cardiovascular imaging: what have we learned from animal models?

Cardiovascular imaging has become an indispensable tool for patient diagnosis and follow up. Probably the wide clinical applications of imaging are due to the possibility of a detailed and high quality description and quantification of cardiovascular system structure and function. Also phenomena that involve complex physiological mechanisms and biochemical pathways, such as inflammation and ischemia, can be visualized in a non-destructive way. The widespread use and evolution of imaging would not have been possible without animal studies. Animal models have allowed for instance, (i) the technical development of different imaging tools, (ii) to test hypothesis generated from human studies and finally, (iii) to evaluate the translational relevance assessment of in vitro and ex-vivo results. In this review, we will critically describe the contribution of animal models to the use of biomedical imaging in cardiovascular medicine. We will discuss the characteristics of the most frequent models used in/for imaging studies. We will cover the major findings of animal studies focused in the cardiovascular use of the repeatedly used imaging techniques in clinical practice and experimental studies. We will also describe the physiological findings and/or learning processes for imaging applications coming from models of the most common cardiovascular diseases. In these diseases, imaging research using animals has allowed the study of aspects such as: ventricular size, shape, global function, and wall thickening, local myocardial function, myocardial perfusion, metabolism and energetic assessment, infarct quantification, vascular lesion characterization, myocardial fiber structure, and myocardial calcium uptake. Finally we will discuss the limitations and future of imaging research with animal models.

BOLD-MRI evaluation of subcutaneous and visceral adipose tissue oxygenation status: effect of dietary salt intake

To investigate the feasibility of blood oxygen level dependent magnetic resonance imaging (BOLD-MRI) in evaluating human subcutaneous and visceral adipose tissue (AT) oxygenation status, as well as their responses to dietary salt loading/depletion, we enrolled 16 healthy subjects [mean body mass index (BMI): 24.8 ± 2.7 kg/m(2)] to conduct a dietary intervention study, beginning with a 3-day run-in period for usual diet, followed by a 7-day high-salt diet (≥ 15 g NaCl/day) and a 7-day low-salt diet (≤ 5 g NaCl/day). Abdominal BOLD-MRI scan was performed to evaluate oxygenation in waist subcutaneous and perirenal (visceral) AT. Two subjects with lower BMI were excluded because of the difficulty to identify subcutaneous AT. High salt diet led to a consistent increase in R2* signal (a parameter for increased hypoxia) both in subcutaneous and visceral AT (all P < 0.0001), which was completely regressed to baseline levels by low salt diet. In addition, subcutaneous AT R2* values at any time points, were all higher than that of visceral AT (all P < 0.0001). Pearson correlation analysis revealed that the visceral AT R2* levels were negatively associated obesity indicators (waist circumference, waist-to-hip ratio and BMI). On the contrary, although a trend towards negative associations between the subcutaneous AT R2* and obesity indicators was observed, none of the associations reached statistical significances. Thus, our data demonstrate the possibility of simultaneous detection of human subcutaneous and visceral AT oxygenation status using BOLD-MRI. In addition, there is a more close relationship visceral AT oxygenation status and the development of obesity.

Functional relevance of coronary artery disease by cardiac magnetic resonance and cardiac computed tomography: myocardial perfusion and fractional flow reserve

Coronary artery disease (CAD) is one of the leading causes of morbidity and mortality and it is responsible for an increasing resource burden. The identification of patients at high risk for adverse events is crucial to select those who will receive the greatest benefit from revascularization. To this aim, several non-invasive functional imaging modalities are usually used as gatekeeper to invasive coronary angiography, but the diagnostic yield of elective invasive coronary angiography remains unfortunately low. Stress myocardial perfusion imaging by cardiac magnetic resonance (stress-CMR) has emerged as an accurate technique for diagnosis and prognostic stratification of the patients with known or suspected CAD thanks to high spatial and temporal resolution, absence of ionizing radiation, and the multiparametric value including the assessment of cardiac anatomy, function, and viability. On the other side, cardiac computed tomography (CCT) has emerged as unique technique providing coronary arteries anatomy and more recently, due to the introduction of stress-CCT and noninvasive fractional flow reserve (FFR-CT), functional relevance of CAD in a single shot scan. The current review evaluates the technical aspects and clinical experience of stress-CMR and CCT in the evaluation of functional relevance of CAD discussing the strength and weakness of each approach.

Structured dictionaries for ischemia estimation in cardiac BOLD MRI at rest

Cardiac Phase-resolved Blood-Oxygen-Level-Dependent (CP-BOLD) MRI examines changes in myocardial oxygenation in response to ischemia without contrast and stress agents. Since signal intensity changes are subtle, quantitative approaches are necessary to examine variations in myocardial BOLD signals and identify ischemic myocardial territories. Here, using data from animal studies, we extract myocardial time series (BOLD signal as a function of cardiac phase) and explore such variations using a structured dictionary-learning framework, considering shift-invariant learning and spatial priors. We use it: to learn a model of baseline (absence of disease) myocardial time series; and in datasets where disease is assumed, to obtain a spatial map of ischemia presence, identifying myocardial time series from ischemic territories in an unsupervised fashion, by exploiting structural properties, or the lack thereof, in the data. By providing new visualization and quantification approaches, we hope to accelerate the clinical translation of cardiac BOLD MRI for noninvasive ischemia assessment.

Synthetic generation of myocardial blood-oxygen-level-dependent MRI time series via structural sparse decomposition modeling

This paper aims to identify approaches that generate appropriate synthetic data (computer generated) for cardiac phase-resolved blood-oxygen-level-dependent (CP-BOLD) MRI. CP-BOLD MRI is a new contrast agent- and stress-free approach for examining changes in myocardial oxygenation in response to coronary artery disease. However, since signal intensity changes are subtle, rapid visualization is not possible with the naked eye. Quantifying and visualizing the extent of disease relies on myocardial segmentation and registration to isolate the myocardium and establish temporal correspondences and ischemia detection algorithms to identify temporal differences in BOLD signal intensity patterns. If transmurality of the defect is of interest pixel-level analysis is necessary and thus a higher precision in registration is required. Such precision is currently not available affecting the design and performance of the ischemia detection algorithms. In this work, to enable algorithmic developments of ischemia detection irrespective to registration accuracy, we propose an approach that generates synthetic pixel-level myocardial time series. We do this by 1) modeling the temporal changes in BOLD signal intensity based on sparse multi-component dictionary learning, whereby segmentally derived myocardial time series are extracted from canine experimental data to learn the model; and 2) demonstrating the resemblance between real and synthetic time series for validation purposes. We envision that the proposed approach has the capacity to accelerate development of tools for ischemia detection while markedly reducing experimental costs so that cardiac BOLD MRI can be rapidly translated into the clinical arena for the noninvasive assessment of ischemic heart disease.

High-salt intake induced visceral adipose tissue hypoxia and its association with circulating monocyte subsets in humans

OBJECTIVE

To investigate the feasibility of blood oxygen level dependent magnetic resonance imaging (BOLD-MRI) in evaluating human visceral adipose tissue (AT) oxygenation induced by salt loading/depletion and its association with changes in circulating monocyte subsets.

METHODS

A dietary intervention study was performed in 23 healthy volunteers beginning with a 3-day usual diet followed by a 7-day high-salt diet (≥15 g NaCl/day) and a 7-day low-salt diet (≤5 g NaCl/day). BOLD-MRI was used to evaluate oxygenation in perirenal AT.

RESULTS

Salt loading led to a consistent AT hypoxia (increase in the R2* signal, 25.2 ± 0.90 s(-1) vs. baseline 21.5 ± 0.71 s(-1) , P < 0.001) and suppression of circulating renin-angiotensin-aldosterone system (RAAS), as well as an expansion of the CD14++CD16+ monocytes and monocyte pro-inflammatory activation. In salt depletion phase, the hypoxic state of AT and the expanded CD14++CD16+ monocyte pool were regressed to baseline levels, accompanied by a rebound activation of RAAS. Moreover, AT oxygenation level was positively correlated with the CD14++CD16+ monocytes (r = 0.419, P < 0.001).

CONCLUSIONS

This work provides proof-of-principle evidence supporting the feasibility of BOLD-MRI in monitoring visceral AT oxygenation in humans induced by dietary salt loading/depletion. In addition, the CD14++CD16+ monocytes may participate in the pathogenesis of high-salt intake induced AT hypoxia.

Oxygenation-sensitive cardiovascular magnetic resonance

Oxygenation-sensitive cardiovascular magnetic resonance (CMR) is a non-contrast technique that allows the non-invasive assessment of myocardial oxygenation. It capitalizes on the fact that deoxygenated hemoglobin in blood can act as an intrinsic contrast agent, changing proton signals in a fashion that can be imaged to reflect the level of blood oxygenation. Increases in O(2) saturation increase the BOLD imaging signal (T2 or T2*), whereas decreases diminish it. This review presents the basic concepts and limitations of the BOLD technique, and summarizes the preclinical and clinical studies in the assessment of myocardial oxygenation with a focus on recent advances. Finally, it provides future directions and a brief look at emerging techniques of this evolving CMR field.