Improved Robustness for Deep Learning-based Segmentation of Multi-Center Myocardial Perfusion MRI Datasets Using Data Adaptive Uncertainty-guided Space-time Analysis.

BACKGROUND: Fully automatic analysis of myocardial perfusion MRI datasets enables rapid and objective reporting of stress/rest studies in patients with suspected ischemic heart disease. Developing deep learning techniques that can analyze multi-center datasets despite limited training data and variations in software (pulse sequence) and hardware (scanner vendor) is an ongoing challenge. METHODS: Datasets from 3 medical centers acquired at 3T (n = 150 subjects; 21,150 first-pass images) were included: an internal dataset (inD; n = 95) and two external datasets (exDs; n = 55) used for evaluating the robustness of the trained deep neural network (DNN) models against differences in pulse sequence (exD-1) and scanner vendor (exD-2). A subset of inD (n = 85) was used for training/validation of a pool of DNNs for segmentation, all using the same spatiotemporal U-Net architecture and hyperparameters but with different parameter initializations. We employed a space-time sliding-patch analysis approach that automatically yields a pixel-wise "uncertainty map" as a byproduct of the segmentation process. In our approach, dubbed Data Adaptive Uncertainty-Guided Space-time (DAUGS) analysis, a given test case is segmented by all members of the DNN pool and the resulting uncertainty maps are leveraged to automatically select the "best" one among the pool of solutions. For comparison, we also trained a DNN using the established approach with the same settings (hyperparameters, data augmentation, etc.). RESULTS: The proposed DAUGS analysis approach performed similarly to the established approach on the internal dataset (Dice score for the testing subset of inD: 0.896 ± 0.050 vs. 0.890 ± 0.049; p = n.s.) whereas it significantly outperformed on the external datasets (Dice for exD-1: 0.885 ± 0.040 vs. 0.849 ± 0.065, p < 0.005; Dice for exD-2: 0.811 ± 0.070 vs. 0.728 ± 0.149, p < 0.005). Moreover, the number of image series with "failed" segmentation (defined as having myocardial contours that include bloodpool or are noncontiguous in ≥1 segment) was significantly lower for the proposed vs. the established approach (4.3% vs. 17.1%, p < 0.0005). CONCLUSIONS: The proposed DAUGS analysis approach has the potential to improve the robustness of deep learning methods for segmentation of multi-center stress perfusion datasets with variations in the choice of pulse sequence, site location or scanner vendor.

Improved Robustness for Deep Learning-based Segmentation of Multi-Center Myocardial Perfusion MRI Datasets Using Data Adaptive Uncertainty-guided Space-time Analysis.

Background: Fully automatic analysis of myocardial perfusion MRI datasets enables rapid and objective reporting of stress/rest studies in patients with suspected ischemic heart disease. Developing deep learning techniques that can analyze multi-center datasets despite limited training data and variations in software (pulse sequence) and hardware (scanner vendor) is an ongoing challenge. Methods: Datasets from 3 medical centers acquired at 3T (n = 150 subjects; 21,150 first-pass images) were included: an internal dataset (inD; n = 95) and two external datasets (exDs; n = 55) used for evaluating the robustness of the trained deep neural network (DNN) models against differences in pulse sequence (exD-1) and scanner vendor (exD-2). A subset of inD (n = 85) was used for training/validation of a pool of DNNs for segmentation, all using the same spatiotemporal U-Net architecture and hyperparameters but with different parameter initializations. We employed a space-time sliding-patch analysis approach that automatically yields a pixel-wise "uncertainty map" as a byproduct of the segmentation process. In our approach, dubbed Data Adaptive Uncertainty-Guided Space-time (DAUGS) analysis, a given test case is segmented by all members of the DNN pool and the resulting uncertainty maps are leveraged to automatically select the "best" one among the pool of solutions. For comparison, we also trained a DNN using the established approach with the same settings (hyperparameters, data augmentation, etc.). Results: The proposed DAUGS analysis approach performed similarly to the established approach on the internal dataset (Dice score for the testing subset of inD: 0.896 ± 0.050 vs. 0.890 ± 0.049; p = n.s.) whereas it significantly outperformed on the external datasets (Dice for exD-1: 0.885 ± 0.040 vs. 0.849 ± 0.065, p < 0.005; Dice for exD-2: 0.811 ± 0.070 vs. 0.728 ± 0.149, p < 0.005). Moreover, the number of image series with "failed" segmentation (defined as having myocardial contours that include bloodpool or are noncontiguous in ≥1 segment) was significantly lower for the proposed vs. the established approach (4.3% vs. 17.1%, p < 0.0005). Conclusions: The proposed DAUGS analysis approach has the potential to improve the robustness of deep learning methods for segmentation of multi-center stress perfusion datasets with variations in the choice of pulse sequence, site location or scanner vendor.

Enabling Reliable Visual Detection of Chronic Myocardial Infarction with Native T1 Cardiac MRI Using Data-Driven Native Contrast Mapping.

Purpose To investigate whether infarct-to-remote myocardial contrast can be optimized by replacing generic fitting algorithms used to obtain native T1 maps with a data-driven machine learning pixel-wise approach in chronic reperfused infarct in a canine model. Materials and Methods A controlled large animal model (24 canines, equal male and female animals) of chronic myocardial infarction with histologic evidence of heterogeneous infarct tissue composition was studied. Unsupervised clustering techniques using self-organizing maps and t-distributed stochastic neighbor embedding were used to analyze and visualize native T1-weighted pixel-intensity patterns. Deep neural network models were trained to map pixel-intensity patterns from native T1-weighted image series to corresponding pixels on late gadolinium enhancement (LGE) images, yielding visually enhanced noncontrast maps, a process referred to as data-driven native mapping (DNM). Pearson correlation coefficients and Bland-Altman analyses were used to compare findings from the DNM approach against standard T1 maps. Results Native T1-weighted images exhibited distinct pixel-intensity patterns between infarcted and remote territories. Granular pattern visualization revealed higher infarct-to-remote cluster separability with LGE labeling as compared with native T1 maps. Apparent contrast-to-noise ratio from DNM (mean, 15.01 ± 2.88 [SD]) was significantly different from native T1 maps (5.64 ± 1.58; P < .001) but similar to LGE contrast-to-noise ratio (15.51 ± 2.43; P = .40). Infarcted areas based on LGE were more strongly correlated with DNM compared with native T1 maps (R2 = 0.71 for native T1 maps vs LGE; R2 = 0.85 for DNM vs LGE; P < .001). Conclusion Native T1-weighted pixels carry information that can be extracted with the proposed DNM approach to maximize image contrast between infarct and remote territories for enhanced visualization of chronic infarct territories. Keywords: Chronic Myocardial Infarction, Cardiac MRI, Data-Driven Native Contrast Mapping Supplemental material is available for this article. © RSNA, 2024.

The Society for Cardiovascular Magnetic Resonance Registry at 150,000.

BACKGROUND: Cardiovascular magnetic resonance (CMR) is increasingly utilized to evaluate expanding cardiovascular conditions. The Society for Cardiovascular Magnetic Resonance (SCMR) Registry is a central repository for real-world clinical data to support cardiovascular research, including those relating to outcomes, quality improvement, and machine learning. The SCMR Registry is built on a regulatory-compliant, cloud-based infrastructure that houses searchable content and Digital Imaging and Communications in Medicine images. The goal of this study is to summarize the status of the SCMR Registry at 150,000 exams. METHODS: The processes for data security, data submission, and research access are outlined. We interrogated the Registry and presented a summary of its contents. RESULTS: Data were compiled from 154,458 CMR scans across 20 United States sites, containing 299,622,066 total images (∼100 terabytes of storage). Across reported values, the human subjects had an average age of 58 years (range 1 month to >90 years old), were 44% (63,070/145,275) female, 72% (69,766/98,008) Caucasian, and had a mortality rate of 8% (9,962/132,979). The most common indication was cardiomyopathy (35,369/131,581, 27%), and most frequently used current procedural terminology code was 75561 (57,195/162,901, 35%). Macrocyclic gadolinium-based contrast agents represented 89% (83,089/93,884) of contrast utilization after 2015. Short-axis cines were performed in 99% (76,859/77,871) of tagged scans, short-axis late gadolinium enhancement (LGE) in 66% (51,591/77,871), and stress perfusion sequences in 30% (23,241/77,871). Mortality data demonstrated increased mortality in patients with left ventricular ejection fraction <35%, the presence of wall motion abnormalities, stress perfusion defects, and infarct LGE, compared to those without these markers. There were 456,678 patient-years of all-cause mortality follow-up, with a median follow-up time of 3.6 years. CONCLUSION: The vision of the SCMR Registry is to promote evidence-based utilization of CMR through a collaborative effort by providing a web mechanism for centers to securely upload de-identified data and images for research, education, and quality control. The Registry quantifies changing practice over time and supports large-scale real-world multicenter observational studies of prognostic utility.

Non-electrocardiogram-gated, free-breathing, off-resonance reduced, high-resolution, whole-heart myocardial T2 * mapping at 3 T within 5 min.

PURPOSE: Widely used conventional 2D T2 * approaches that are based on breath-held, electrocardiogram (ECG)-gated, multi-gradient-echo sequences are prone to motion artifacts in the presence of incomplete breath holding or arrhythmias, which is common in cardiac patients. To address these limitations, a 3D, non-ECG-gated, free-breathing T2 * technique that enables rapid whole-heart coverage was developed and validated. METHODS: A continuous random Gaussian 3D k-space sampling was implemented using a low-rank tensor framework for motion-resolved 3D T2 * imaging. This approach was tested in healthy human volunteers and in swine before and after intravenous administration of ferumoxytol. RESULTS: Spatial-resolution matched T2 * images were acquired with 2-3-fold reduction in scan time using the proposed T2 * mapping approach relative to conventional T2 * mapping. Compared with the conventional approach, T2 * images acquired with the proposed method demonstrated reduced off-resonance and flow artifacts, leading to higher image quality and lower coefficient of variation in T2 *-weighted images of the myocardium of swine and humans. Mean myocardial T2 * values determined using the proposed and conventional approaches were highly correlated and showed minimal bias. CONCLUSION: The proposed non-ECG-gated, free-breathing, 3D T2 * imaging approach can be performed within 5 min or less. It can overcome critical image artifacts from undesirable cardiac and respiratory motion and bulk off-resonance shifts at the heart-lung interface. The proposed approach is expected to facilitate faster and improved cardiac T2 * mapping in those with limited breath-holding capacity or arrhythmias.

Temporal Uncertainty Localization to Enable Human-in-the-loop Analysis of Dynamic Contrast-enhanced Cardiac MRI Datasets.

Dynamic contrast-enhanced (DCE) cardiac magnetic resonance imaging (CMRI) is a widely used modality for diagnosing myocardial blood flow (perfusion) abnormalities. During a typical free-breathing DCE-CMRI scan, close to 300 time-resolved images of myocardial perfusion are acquired at various contrast "wash in/out" phases. Manual segmentation of myocardial contours in each time-frame of a DCE image series can be tedious and time-consuming, particularly when non-rigid motion correction has failed or is unavailable. While deep neural networks (DNNs) have shown promise for analyzing DCE-CMRI datasets, a "dynamic quality control" (dQC) technique for reliably detecting failed segmentations is lacking. Here we propose a new space-time uncertainty metric as a dQC tool for DNN-based segmentation of free-breathing DCE-CMRI datasets by validating the proposed metric on an external dataset and establishing a human-in-the-loop framework to improve the segmentation results. In the proposed approach, we referred the top 10% most uncertain segmentations as detected by our dQC tool to the human expert for refinement. This approach resulted in a significant increase in the Dice score (p<0.001) and a notable decrease in the number of images with failed segmentation (16.2% to 11.3%) whereas the alternative approach of randomly selecting the same number of segmentations for human referral did not achieve any significant improvement. Our results suggest that the proposed dQC framework has the potential to accurately identify poor-quality segmentations and may enable efficient DNN-based analysis of DCE-CMRI in a human-in-the-loop pipeline for clinical interpretation and reporting of dynamic CMRI datasets.

Temporal Uncertainty Localization to Enable Human-in-the-Loop Analysis of Dynamic Contrast-Enhanced Cardiac MRI Datasets.

Dynamic contrast-enhanced (DCE) cardiac magnetic resonance imaging (CMRI) is a widely used modality for diagnosing myocardial blood flow (perfusion) abnormalities. During a typical free-breathing DCE-CMRI scan, close to 300 time-resolved images of myocardial perfusion are acquired at various contrast "wash in/out" phases. Manual segmentation of myocardial contours in each time-frame of a DCE image series can be tedious and time-consuming, particularly when non-rigid motion correction has failed or is unavailable. While deep neural networks (DNNs) have shown promise for analyzing DCE-CMRI datasets, a "dynamic quality control" (dQC) technique for reliably detecting failed segmentations is lacking. Here we propose a new space-time uncertainty metric as a dQC tool for DNN-based segmentation of free-breathing DCE-CMRI datasets by validating the proposed metric on an external dataset and establishing a human-in-the-loop framework to improve the segmentation results. In the proposed approach, we referred the top 10% most uncertain segmentations as detected by our dQC tool to the human expert for refinement. This approach resulted in a significant increase in the Dice score (p < 0.001) and a notable decrease in the number of images with failed segmentation (16.2% to 11.3%) whereas the alternative approach of randomly selecting the same number of segmentations for human referral did not achieve any significant improvement. Our results suggest that the proposed dQC framework has the potential to accurately identify poor-quality segmentations and may enable efficient DNN-based analysis of DCE-CMRI in a human-in-the-loop pipeline for clinical interpretation and reporting of dynamic CMRI datasets.

Intramyocardial hemorrhage drives fatty degeneration of infarcted myocardium.

Sudden blockage of arteries supplying the heart muscle contributes to millions of heart attacks (myocardial infarction, MI) around the world. Although re-opening these arteries (reperfusion) saves MI patients from immediate death, approximately 50% of these patients go on to develop chronic heart failure (CHF) and die within a 5-year period; however, why some patients accelerate towards CHF while others do not remains unclear. Here we show, using large animal models of reperfused MI, that intramyocardial hemorrhage - the most damaging form of reperfusion injury (evident in nearly 40% of reperfused ST-elevation MI patients) - drives delayed infarct healing and is centrally responsible for continuous fatty degeneration of the infarcted myocardium contributing to adverse remodeling of the heart. Specifically, we show that the fatty degeneration of the hemorrhagic MI zone stems from iron-induced macrophage activation, lipid peroxidation, foam cell formation, ceroid production, foam cell apoptosis and iron recycling. We also demonstrate that timely reduction of iron within the hemorrhagic MI zone reduces fatty infiltration and directs the heart towards favorable remodeling. Collectively, our findings elucidate why some, but not all, MIs are destined to CHF and help define a potential therapeutic strategy to mitigate post-MI CHF independent of MI size.

Reduced myocardial perfusion is common among subjects with ischemia and no obstructive coronary artery disease and heart failure with preserved ejection fraction: a report from the WISE-CVD continuation study.

Aim: Women with evidence of ischemia and no obstructive coronary artery disease (INOCA) have an increased risk of major adverse cardiac events, including heart failure with preserved ejection fraction (HFpEF). To investigate potential links between INOCA and HFpEF, we examined pathophysiological findings present in both INOCA and HFpEF. Methods: We performed adenosine stress cardiac magnetic resonance imaging (CMRI) in 56 participants, including 35 women with suspected INOCA, 13 women with HFpEF, and 8 reference control women. Myocardial perfusion imaging was performed at rest and with vasodilator stress with intravenous adenosine. Myocardial perfusion reserve index was quantified as the ratio of the upslope of increase in myocardial contrast at stress vs. rest. All CMRI measures were quantified using CVI42 software (Circle Cardiovascular Imaging Inc). Statistical analysis was performed using linear regression models, Fisher's exact tests, ANOVA, or Kruskal-Wallis tests. Results: Age (P = 0.007), Body surface area (0.05) were higher in the HFpEF group. Left ventricular ejection fraction (P = 0.02) was lower among the INOCA and HFpEF groups than reference controls after age adjustment. In addition, there was a graded reduction in myocardial perfusion reserve index in HFpEF vs. INOCA vs. reference controls (1.5 ± 0.3, 1.8 ± 0.3, 1.9 ± 0.3, P = 0.02), which was attenuated with age-adjustment. Conclusion: Reduced myocardial perfusion reserve appears to be a common pathophysiologic feature in INOCA and HFpEF patients.

A Patch-Wise Deep Learning Approach for Myocardial Blood Flow Quantification with Robustness to Noise and Nonrigid Motion.

Quantitative analysis of dynamic contrast-enhanced cardiovascular MRI (cMRI) datasets enables the assessment of myocardial blood flow (MBF) for objective evaluation of ischemic heart disease in patients with suspected coronary artery disease. State-of-the-art MBF quantification techniques use constrained deconvolution and are highly sensitive to noise and motion-induced errors, which can lead to unreliable outcomes in the setting of high-resolution MBF mapping. To overcome these limitations, recent iterative approaches incorporate spatial-smoothness constraints to tackle pixel-wise MBF mapping. However, such iterative methods require a computational time of up to 30 minutes per acquired myocardial slice, which is a major practical limitation. Furthermore, they cannot enforce robustness to residual nonrigid motion which can occur in clinical stress/rest studies of patients with arrhythmia. We present a non-iterative patch-wise deep learning approach for pixel-wise MBF quantification wherein local spatio-temporal features are learned from a large dataset of myocardial patches acquired in clinical stress/rest cMRI studies. Our approach is scanner-independent, computationally efficient, robust to noise, and has the unique feature of robustness to motion-induced errors. Numerical and experimental results obtained using real patient data demonstrate the effectiveness of our approach.Clinical Relevance- The proposed patch-wise deep learning approach significantly improves the reliability of high-resolution myocardial blood flow quantification in cMRI by improving its robustness to noise and nonrigid myocardial motion and is up to 300-fold faster than state-of-the-art iterative approaches.

Deep Learning-Based Segmentation and Uncertainty Assessment for Automated Analysis of Myocardial Perfusion MRI Datasets Using Patch-Level Training and Advanced Data Augmentation.

In this work, we develop a patch-level training approach and a task-driven intensity-based augmentation method for deep-learning-based segmentation of motion-corrected perfusion cardiac magnetic resonance imaging (MRI) datasets. Further, the proposed method generates an image-based uncertainty map thanks to a novel spatial sliding-window approach used during patch-level training, hence allowing for uncertainty quantification. Using the quantified uncertainty, we detect the out-of-distribution test data instances so that the end-user can be alerted that the test data is not suitable for the trained network. This feature has the potential to enable a more reliable integration of the proposed deep learning-based framework into clinical practice. We test our approach on external MRI data acquired using a different acquisition protocol to demonstrate the robustness of our performance to variations in pulse-sequence parameters. The presented results further demonstrate that our deep-learning image segmentation approach trained with the proposed data-augmentation technique incorporating spatiotemporal (2D+time) patches is superior to the state-of-the-art 2D approach in terms of generalization performance.

Retrospective Detection and Suppression of Dark-Rim Artifacts in First-Pass Perfusion Cardiac MRI Enabled by Deep Learning.

The dark-rim artifact (DRA) remains an important challenge in the routine clinical use of first-pass perfusion (FPP) cardiac magnetic resonance imaging (cMRI). The DRA mimics the appearance of perfusion defects in the subendocardial wall and reduces the accuracy of diagnosis in patients with suspected ischemic heart disease. The main causes for DRA are known to be Gibbs ringing and bulk motion of the heart. The goal of this work is to propose a deep-learning-enabled automatic approach for the detection of motion-induced DRAs in FPP cMRI datasets. To this end, we propose a new algorithm that can detect the DRA in individual time frames by analyzing multiple reconstructions of the same time frame (k-space data) with varying temporal windows. In addition to DRA detection, our approach is also capable of suppressing the extent and severity of DRAs as a byproduct of the same reconstruction-analysis process. In this proof-of-concept study, our proposed method showed a good performance for automatic detection of subendocardial DRAs in stress perfusion cMRI studies of patients with suspected ischemic heart disease. To the best of our knowledge, this is the first approach that performs deep-learning-enabled detection and suppression of DRAs in cMRI.Clinical Relevance- Our approach enables clinicians to provide a more accurate diagnosis of ischemic heart disease by detecting and suppressing subendocardial dark-rim artifacts in first-pass perfusion cMRI datasets.

Diastolic dysfunction in women with ischemia and no obstructive coronary artery disease: Mechanistic insight from magnetic resonance imaging.

BACKGROUND: Ischemia with no obstructive coronary artery disease (INOCA) is prevalent in women and is associated with increased risk of developing heart failure with preserved ejection fraction (HFpEF); however, the mechanism(s) contributing to this progression remains unclear. Given that diastolic dysfunction is common in women with INOCA, defining mechanisms related to diastolic dysfunction in INOCA could identify therapeutic targets to prevent HFpEF. METHODS: Cardiac MRI was performed in 65 women with INOCA and 12 reference controls. Diastolic function was defined by left ventricular early diastolic circumferential strain rate (eCSRd). Contributors to diastolic dysfunction were chosen a priori as coronary vascular dysfunction (myocardial perfusion reserve index [MPRI]), diffuse myocardial fibrosis (extracellular volume [ECV]), and aortic stiffness (aortic pulse wave velocity [aPWV]). RESULTS: Compared to controls, eCSRd was lower in INOCA (1.61 ± 0.33/s vs. 1.36 ± 0.31/s, P = 0.016); however, this difference was not exaggerated when the INOCA group was sub-divided by low and high MPRI (P > 0.05) nor was ECV elevated in INOCA (29.0 ± 1.9% vs. 28.0 ± 3.2%, control vs. INOCA; P = 0.38). However, aPWV was higher in INOCA vs. controls (8.1 ± 3.2 m/s vs. 6.1 ± 1.5 m/s; P = 0.045), and was associated with eCSRd (r = -0.50, P < 0.001). By multivariable linear regression analysis, aPWV was an independent predictor of decreased eCSRd (standardized ß = -0.39, P = 0.003), as was having an elevated left ventricular mass index (standardized ß = -0.25, P = 0.024) and lower ECV (standardized ß = 0.30, P = 0.003). CONCLUSIONS: These data provide mechanistic insight into diastolic dysfunction in women with INOCA, identifying aortic stiffness and ventricular remodeling as putative therapeutic targets.

Left ventricular circumferential strain and coronary microvascular dysfunction: A report from the Women's Ischemia Syndrome Evaluation Coronary Vascular Dysfunction (WISE-CVD) Project.

AIMS: Women with ischemia but no obstructive coronary artery disease (INOCA) often have coronary microvascular dysfunction (CMD). Left ventricular (LV) circumferential strain (CS) is often lower in INOCA compared to healthy controls; however, it remains unclear whether CS differs between INOCA women with and without CMD. We hypothesized that CS would be lower in women with CMD, consistent with CMD-induced LV mechanical dysfunction. METHODS AND RESULTS: Cardiac magnetic resonance (cMR) images were examined from women enrolled in the Women's Ischemia Syndrome Evaluation-Coronary Vascular Dysfunction Project. CS by feature tracking in INOCA women with CMD, defined as myocardial perfusion reserve index (MPRI) <1.84 during adenosine-stress perfusion cMR, was compared with CS in women without CMD. In a subset who had invasive coronary function testing (CFT), the relationship between CS and CFT metrics, LV ejection fraction (LVEF) and cardiovascular risk factors was investigated. Among 317 women with INOCA, 174 (55%) had CMD measured by MPRI. CS was greater in women with CMD compared to those without CMD (23.2 ± 2.5% vs. 22.1 ± 3.0%, respectively, P = 0.001). In the subset with CFT (n = 153), greater CS was associated with increased likelihood of reduced vasodilator capacity (OR = 1.33, 95%CI = 1.02-1.72, p = 0.03) and discriminated abnormal vs. normal coronary vascular function compared to CAD risk factors, LVEF and LV concentricity (AUC: 0.82 [0.73-0.96 95%CI] vs. 0.65 [0.60-0.71 95%CI], respectively, P = 0.007). CONCLUSION: The data indicate that LV circumferential strain is related to and predicts CMD, although in a direction contrary with our hypothesis, which may represent an early sign of LV mechanical dysfunction in CMD.

N-Terminal pro-B-type natriuretic peptide and coronary microvascular dysfunction in women with preserved ejection fraction: A report from the Women's Ischemia Syndrome Evaluation-Coronary Vascular Dysfunction (WISE-CVD) study.

BACKGROUND: Women with symptoms and signs of ischemia, preserved left ventricular ejection fraction (LVEF), and no obstructive coronary artery disease (CAD), often have coronary microvascular dysfunction (CMD), and are at risk of future heart failure with preserved ejection fraction (HFpEF). N-terminal pro-B-type natriuretic peptide (NT-proBNP) is used to evaluate HF and myocardial ischemia. Relationships between NT-proBNP and CMD are not well defined in this population. METHODS: We evaluated resting NT-proBNP levels in 208 women with symptoms and signs of ischemic heart disease, preserved LVEF and no obstructive CAD undergoing clinically indicated invasive coronary flow reserve (CFR) as a measure of CMD-related ischemia and resting left ventricular end-diastolic pressure (LVEDP). Chi-square testing was used for categorical variables and ANOVA or Kruskal-Wallis tests were used for continuous variables. RESULTS: Overall, 79% had an elevated resting LVEDP, and mean NT-proBNP was 115 ± 158 pg/mL. NT-proBNP levels correlated directly with age (r = 0.28, p = <0.0001), and indirectly with body mass index (r = -0.21, p = 0.0006), but did not independently associate with CFR. When stratified by NT-proBNP thresholds, higher NT-proBNP was initially associated with lower CFR, which did not persist with adjustment for multiple testing (p = 0.01 and 0.36, respectively). CONCLUSION: Among women with symptoms and signs of ischemia, preserved LVEF, no obstructive CAD, and undergoing clinically indicated functional coronary angiography (FCA) for suspected CMD, while a majority had elevated resting LVEDP, we failed to find an independent association between CFR and NT-proBNP, although stratified clinical thresholds may relate to lower CFR. Further work is needed to investigate if these findings support the hypothesis that CMD-related ischemia may be a precursor to HFpEF.

Multicenter Study on the Diagnostic Performance of Native-T1 Cardiac Magnetic Resonance of Chronic Myocardial Infarctions at 3T.

BACKGROUND: Preclinical studies and pilot patient studies have shown that chronic infarctions can be detected and characterized from cardiac magnetic resonance without gadolinium-based contrast agents using native-T1 maps at 3T. We aimed to investigate the diagnostic capacity of this approach for characterizing chronic myocardial infarctions (MIs) in a multi-center setting. METHODS: Patients with a prior MI (n=105) were recruited at 3 different medical centers and were imaged with native-T1 mapping and late gadolinium enhancement (LGE) at 3T. Infarct location, size, and transmurality were determined from native-T1 maps and LGE. Sensitivity, specificity, receiver-operating characteristic metrics, and inter- and intraobserver variabilities were assessed relative to LGE. RESULTS: Across all subjects, T1 of MI territory was 1621±110 ms, and remote territory was 1225±75 ms. Sensitivity, specificity, and area under curve for detecting MI location based on native-T1 mapping relative to LGE were 88%, 92%, and 0.93, respectively. Native-T1 maps were not different for measuring infarct size (native-T1 maps: 12.1±7.5%; LGE: 11.8±7.2%, P=0.82) and were in agreement with LGE (R2=0.92, bias, 0.09±2.6%). Corresponding inter- and intraobserver assessments were also highly correlated (interobserver: R2=0.90, bias, 0.18±2.4%; and intraobserver: R2=0.91, bias, 0.28±2.1%). Native T1 maps were not different for measuring MI transmurality (native-T1 maps: 49.1±15.8%; LGE: 47.2±19.0%, P=0.56) and showed agreement (R2=0.71; bias, 1.32±10.2%). Corresponding inter- and intraobserver assessments were also in agreement (interobserver: R2=0.81, bias, 0.1±9.4%; and intraobserver: R2=0.91, bias, 0.28±2.1%, respectively). While the overall accuracy for detecting MI with native-T1 maps at 3T was high, logistic regression analysis showed that MI location was a prominent confounder. CONCLUSIONS: Native-T1 mapping can be used to image chronic MI with high degree of accuracy, and as such, it is a viable alternative for scar imaging in patients with chronic MI who are contraindicated for LGE. Technical advancements may be needed to overcome the imaging confounders that currently limit native-T1 mapping from reaching equivalent detection levels as LGE.

Left atrial stiffness in women with ischemia and no obstructive coronary artery disease: Novel insight from left atrial feature tracking.

BACKGROUND: Women with signs and symptoms of ischemia and no obstructive coronary artery disease (INOCA) are at risk of heart failure with preserved ejection fraction (HFpEF); however, the mechanism for HFpEF progression remains unclear. Studies in INOCA have largely focused on left ventricular function. The left atrium serves an important role in maintaining transmitral flow, and is impaired in HFpEF; however, it remains unclear if left atrial function is impaired in INOCA. HYPOTHESIS: Left atrial function is progressively worse in INOCA and HFpEF compared to controls. METHODS: We compared 39 reference control subjects to 64 women with INOCA and 22 subjects with HFpEF. Left atrial strain was assessed by feature tracking using magnetic resonance cine images. RESULTS: Peak left atrial strain was reduced in HFpEF compared to controls (22.9 ± 4.8% vs 25.9 ± 3.2%, P < .01), but similar in INOCA (24.8 ± 4.5%) compared to HFpEF and controls (P = .18). However, left ventricular end-diastolic pressure (LVEDP) was elevated in 33% of INOCA participants, suggesting that left atrial stiffness (LVEDP/LA strain) is elevated in a large portion of women with INOCA. CONCLUSIONS: Taken together, we interpret these data to support our working hypothesis that INOCA is a pre-HFpEF state, with left atrial stiffness preceding overt left atrial dysfunction; representing a putative therapeutic target to prevent HFpEF progression in this at-risk population.

Heart Rate-Independent 3D Myocardial Blood Oxygen Level-Dependent MRI at 3.0 T with Simultaneous 13N-Ammonia PET Validation.

Background Despite advances, blood oxygen level-dependent (BOLD) cardiac MRI for myocardial perfusion is limited by inadequate spatial coverage, imaging speed, multiple breath holds, and imaging artifacts, particularly at 3.0 T. Purpose To develop and validate a robust, contrast agent-unenhanced, free-breathing three-dimensional (3D) cardiac MRI approach for reliably examining changes in myocardial perfusion between rest and adenosine stress. Materials and Methods A heart rate-independent, free-breathing 3D T2 mapping technique at 3.0 T that can be completed within the period of adenosine stress (≤4 minutes) was developed by using computer simulations, ex vivo heart preparations, and dogs. Studies in dogs were performed with and without coronary stenosis and validated with simultaneously acquired nitrogen 13 (13N) ammonia PET perfusion in a clinical PET/MRI system. The MRI approach was also prospectively evaluated in healthy human volunteers (from January 2017 to September 2017). Myocardial BOLD responses (MBRs) between normal and ischemic myocardium were compared with mixed model analysis. Results Dogs (n = 10; weight range, 20-25 kg; mongrel dogs) and healthy human volunteers (n = 10; age range, 22-53 years; seven men) were evaluated. In healthy dogs, T2 MRI at adenosine stress was greater than at rest (mean rest vs stress, 38.7 msec ± 2.5 [standard deviation] vs 45.4 msec ± 3.3, respectively; MBR, 1.19 ± 0.08; both, P < .001). At the same conditions, mean rest versus stress PET perfusion was 1.1 mL/mg/min ± 0.11 versus 2.3 mL/mg/min ± 0.82, respectively (P < .001); myocardial perfusion reserve (MPR) was 2.4 ± 0.82 (P < .001). The BOLD response and PET MPR were positively correlated (R = 0.67; P < .001). In dogs with coronary stenosis, perfusion anomalies were detected on the basis of MBR (normal vs ischemic, 1.09 ± 0.05 vs 1.00 ± 0.04, respectively; P < .001) and MPR (normal vs ischemic, 2.7 ± 0.08 vs 1.7 ± 1.1, respectively; P < .001). Human volunteers showed increased myocardial T2 at stress (rest vs stress, 44.5 msec ± 2.6 vs 49.0 msec ± 5.5, respectively; P = .004; MBR, 1.1 msec ± 8.08). Conclusion This three-dimensional cardiac blood oxygen level-dependent (BOLD) MRI approach overcame key limitations associated with conventional cardiac BOLD MRI by enabling whole-heart coverage within the standard duration of adenosine infusion, and increased the magnitude and reliability of BOLD contrast, which may be performed without requiring breath holds. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Almeida in this issue.

Design, methodology and baseline characteristics of the Women's Ischemia Syndrome Evaluation-Coronary Vascular Dysfunction (WISE-CVD).

A significant number of women with signs and symptoms of ischemia with no obstructive coronary artery disease (INOCA) have coronary vascular dysfunction detected by invasive coronary reactivity testing (CRT). However, the noninvasive assessment of coronary vascular dysfunction has been limited. METHODS: The Women's Ischemia Syndrome Evaluation-Coronary Vascular Dysfunction (WISE-CVD) was a prospective study of women with suspected INOCA aimed to investigate whether (1) cardiac magnetic resonance imaging (CMRI) abnormalities in left ventricular morphology and function and myocardial perfusion predict CRT measured coronary microvascular dysfunction, (2) these persistent CMRI abnormalities at 1-year follow-up predict persistent symptoms of ischemia, and (3) these CMRI abnormalities predict cardiovascular outcomes. By design, a sample size of 375 women undergoing clinically indicated invasive coronary angiography for suspected INOCA was projected to complete baseline CMRI, a priori subgroup of 200 clinically indicated CRTs, and a priori subgroup of 200 repeat 1-year follow-up CMRIs. RESULTS: A total of 437 women enrolled between 2008 and 2015, 374 completed baseline CMRI, 279 completed CRT, and 214 completed 1-year follow-up CMRI. Mean age was 55±â€¯11 years, 93% had 20%-50% coronary stenosis, and 7% had <20% stenosis by angiography. CONCLUSIONS: The WISE-CVD study investigates the utility of noninvasive CMRI to predict coronary vascular dysfunction in comparison to invasive CRT, and the prognostic value of CMRI abnormalities for persistent symptoms of ischemia and cardiovascular outcomes in women with INOCA. WISE-CVD will provide new understanding of a noninvasive imaging modality for future clinical trials.

Adenosine vs Regadenoson Pharmacologic Stress Differs in Women with Suspected Coronary Microvascular Dysfunction: A Report from the Women's Ischemia Syndrome Evaluation-Coronary Vascular Dysfunction (WISE-CVD) Study.

Background: Stress cardiac magnetic resonance (CMR) imaging with myocardial perfusion reserve index (MPRI) measurement has emerged as a noninvasive method for assessing coronary microvascular dysfunction (CMD) in the absence of obstructive coronary artery disease (CAD). Pharmacologic stress with adenosine or regadenoson is typically used with comparable coronary vasodilation, but higher unadjusted MPRI has been reported with regadenoson in healthy men. This difference has not been assessed in symptomatic or healthy women. Methods: In a prospective cohort study, 139 symptomatic women with suspected CMD and no obstructive CAD underwent stress CMR and invasive coronary flow reserve (CFR) testing. Adenosine was the default vasodilator (n=99), while regadenoson was used if history of asthma or prior adenosine intolerance (n=40). Stress CMR was also performed in 40 age-matched healthy controls using adenosine (n=20) and regadenoson (n=20). Unpaired t-tests and analysis of covariance were performed to compare MPRI with adenosine and regadenoson in the symptomatic women and healthy controls. Results: Compared to regadenoson cases, adenosine cases had lower invasive CFR (2.64±0.62 vs 2.94±0.68, p=0.01) and pharmacologic heart rate change (28±16 vs 38±15 bpm, p=0.0008). Unadjusted MPRI was lower in the adenosine compared to regadenoson cases (1.73±0.38 vs 2.27±0.59, p<0.0001). When adjusted for heart rate, rate-pressure-product, and invasive CFR, MPRI remained lower in the adenosine cases (p<0.0001). Invasive CFR to adenosine correlated with adenosine MPRI (r 0.17, p=0.02) but not regadenoson MPRI (r -0.14, p=0.19). There was no significant difference in MPRI in the controls who received adenosine vs regadenoson (2.27±0.33 vs 2.38±0.44, p=0.36). Conclusion: In women undergoing stress CMR for suspected CMD, those who received adenosine had lower MPRI than those who received regadenoson. However, there were no differences in MPRI in the healthy controls. These findings suggest there may be physiologic differences in adenosine and regadenoson response in the coronary microcirculation of symptomatic women.