Artifact-reduced two-dimensional cine steady state free precession for myocardial blood- oxygen-level-dependent imaging

Role of Cardiovascular Magnetic Resonance in Ischemic Cardiomyopathy

Ischemic heart disease is the most common cause of cardiovascular morbidity and mortality. Cardiac magnetic resonance (CMR) improves on other noninvasive modalities in detection, assessment, and prognostication of ischemic heart disease. The incorporation of CMR in clinical trials allows for smaller patient samples without the sacrifice of power needed to demonstrate clinical efficacy. CMR can accurately quantify infarct acuity, size, and complications; guide therapy; and prognosticate recovery. Timing of revascularization remains the holy grail of ischemic heart disease, and viability assessment using CMR may be the missing link needed to help reduce morbidity and mortality associated with the disease.

Sorted Golden-step phase encoding: an improved Golden-step imaging technique for cardiac and respiratory self-gated cine cardiovascular magnetic resonance imaging

BACKGROUND

Numerous self-gated cardiac imaging techniques have been reported in the literature. Most can track either cardiac or respiratory motion, and many incur some overhead to imaging data acquisition. We previously described a Cartesian cine imaging technique, pseudo-projection motion tracking with golden-step phase encoding, capable of tracking both cardiac and respiratory motion at no cost to imaging data acquisition. In this work, we describe improvements to the technique by dramatically reducing its vulnerability to eddy current and flow artifacts and demonstrating its effectiveness in expanded cardiovascular applications.

METHODS

As with our previous golden-step technique, the Cartesian phase encodes over time were arranged based on the integer golden step, and readouts near ky = 0 (pseudo-projections) were used to derive motion. In this work, however, the readouts were divided into equal and consecutive temporal segments, within which the readouts were sorted according to ky. The sorting reduces the phase encode jump between consecutive readouts while maintaining the pseudo-randomness of ky to sample both cardiac and respiratory motion without comprising the ability to retrospectively set the temporal resolution of the original technique. On human volunteers, free-breathing, electrocardiographic (ECG)-free cine scans were acquired for all slices of the short axis stack and the 4-chamber view of the long axis. Retrospectively, cardiac motion and respiratory motion were automatically extracted from the pseudo-projections to guide cine reconstruction. The resultant image quality in terms of sharpness and cardiac functional metrics was compared against breath-hold ECG-gated reference cines.

RESULTS

With sorting, motion tracking of both cardiac and respiratory motion was effective for all slices orientations imaged, and artifact occurrence due to eddy current and flow was efficiently eliminated. The image sharpness derived from the self-gated cines was found to be comparable to the reference cines (mean difference less than 0.05 mm- 1 for short-axis images and 0.075 mm- 1 for long-axis images), and the functional metrics (mean difference < 4 ml) were found not to be statistically different from those from the reference.

CONCLUSIONS

This technique dramatically reduced the eddy current and flow artifacts while preserving the ability of cost-free motion tracking and the flexibility of choosing arbitrary navigator zone width, number of cardiac phases, and duration of scanning. With the restriction of the artifacts removed, the Cartesian golden-step cine imaging can now be applied to cardiac imaging slices of more diverse orientation and anatomy at greater reliability.

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.

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.

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.

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

BACKGROUND

Fast noninvasive identification of ischemic territories at rest (before tissue-specific changes) and assessment of functional status can be valuable in the management of severe coronary artery disease. This study investigated the use of cardiac phase-resolved blood oxygen level-dependent (CP-BOLD) cardiovascular magnetic resonance in detecting myocardial ischemia at rest secondary to severe coronary artery stenosis.

METHODS AND RESULTS

CP-BOLD, standard cine, and T2-weighted images were acquired in canines (n=11) at baseline and within 20 minutes of ischemia induction (severe left anterior descending stenosis) at rest. After 3 hours of ischemia, left anterior descending stenosis was removed, and T2-weighted and late-gadolinium-enhancement images were acquired. From standard cine and CP-BOLD images, end-systolic and end-diastolic myocardium was segmented. Affected and remote sections of the myocardium were identified from postreperfusion late-gadolinium-enhancement images. Systolic-to-diastolic ratio (S/D), quotient of mean end-systolic and end-diastolic signal intensities (on CP-BOLD and standard cine), was computed for affected and remote segments at baseline and ischemia. Ejection fraction and segmental wall thickening were derived from CP-BOLD images at baseline and ischemia. On CP-BOLD images, S/D was >1 (remote and affected territories) at baseline; S/D was diminished only in affected territories during ischemia, and the findings were statistically significant (ANOVA, post hoc P<0.01). The dependence of S/D on ischemia was not observed in standard cine images. Computer simulations confirmed the experimental findings. Receiver-operating characteristic analysis showed that S/D identifies affected regions with performance (area under the curve, 0.87) similar to ejection fraction (area under the curve, 0.89) and segmental wall thickening (area under the curve, 0.75).

CONCLUSIONS

Preclinical studies and computer simulations showed that CP-BOLD cardiovascular magnetic resonance could be useful in detecting myocardial ischemia at rest. Patient studies are needed for clinical translation.

Myocardial oxygenation in coronary artery disease: insights from blood oxygen level-dependent magnetic resonance imaging at 3 tesla

OBJECTIVES

The purpose of this study was to assess the diagnostic accuracy of blood oxygen-level dependent (BOLD) MRI in suspected coronary artery disease (CAD).

BACKGROUND

By exploiting the paramagnetic properties of deoxyhemoglobin, BOLD magnetic resonance imaging can detect myocardial ischemia. We applied BOLD imaging and first-pass perfusion techniques to: 1) examine the pathophysiological relationship between coronary stenosis, perfusion, ventricular scar, and myocardial oxygenation; and 2) evaluate the diagnostic performance of BOLD imaging in the clinical setting.

METHODS

BOLD and first-pass perfusion images were acquired at rest and stress (4 to 5 min intravenous adenosine, 140 μg/kg/min) and assessed quantitatively (using a BOLD signal intensity index [stress/resting signal intensity], and absolute quantification of perfusion by model-independent deconvolution). A BOLD signal intensity index threshold to identify ischemic myocardium was first determined in a derivation arm (25 CAD patients and 20 healthy volunteers). To determine diagnostic performance, this was then applied in a separate group comprising 60 patients with suspected CAD referred for diagnostic angiography.

RESULTS

Prospective evaluation of BOLD imaging yielded an accuracy of 84%, a sensitivity of 92%, and a specificity of 72% for detecting myocardial ischemia and 86%, 92%, and 72%, respectively, for identifying significant coronary stenosis. Segment-based analysis revealed evidence of dissociation between oxygenation and perfusion (r = -0.26), with a weaker correlation of quantitative coronary angiography with myocardial oxygenation (r = -0.20) than with perfusion (r = -0.40; p = 0.005 for difference). Hypertension increased the odds of an abnormal BOLD response, but diabetes mellitus, hypercholesterolemia, and the presence of ventricular scar were not associated with significant deoxygenation.

CONCLUSIONS

BOLD imaging provides valuable insights into the pathophysiology of CAD; myocardial hypoperfusion is not necessarily commensurate with deoxygenation. In the clinical setting, BOLD imaging achieves favorable accuracy for identifying the anatomic and functional significance of CAD.

Ischemic extent as a biomarker for characterizing severity of coronary artery stenosis with blood oxygen-sensitive MRI

PURPOSE

To investigate whether a statistical analysis of myocardial blood-oxygen-level-dependent (mBOLD) signal intensities can lead to the identification and quantification of the ischemic area supplied by the culprit artery.

MATERIALS AND METHODS

Cardiac BOLD images were acquired in a canine model (n = 9) with controllable LCX stenosis at rest and during adenosine infusion on a 1.5T clinical scanner. Statistical distributions of myocardial pixel-intensities derived from BOLD images were used to compute an area metric (ischemic extent, IE). True myocardial perfusion was estimated from microsphere analysis. IE was compared against a standard metric (segment-intensity-response, SIR). Additional animals (n = 3) were used to investigate the feasibility of the approach for identifying ischemic territories due to LAD stenosis from mBOLD images.

RESULTS

Regression analyses showed that IE and myocardial flow ratio between rest and adenosine infusion (MFR) were exponentially related (R(2) > 0.70, P < 0.001, for end-systole and end-diastole), while SIR and MFR were linearly related to end-systole (R(2) = 0.51, P < 0.04) and unrelated to end-diastole (R(2) ≈ 0, P = 0.91). Receiver-operating-characteristic analysis that IE was superior to SIR for detecting critical stenosis (MFR ≤ 2) in end-systole and end-diastole. Feasibility studies on LAD narrowing demonstrated that the proposed approach could also identify oxygenation changes in the LAD territories.

CONCLUSION

The proposed evaluation of cardiac BOLD magnetic resonance imaging (MRI) offers marked improvement in sensitivity and specificity for detecting critical coronary stenosis at 1.5T compared to the mean segmental intensity approach. Patient studies are now warranted to determine its clinical utility.