Myocardial Blood Flow Quantified Using Stress Cardiac Magnetic Resonance After Mild COVID-19 Infection.

BACKGROUND: Severe COVID-19 infection is known to alter myocardial perfusion through its effects on the endothelium and microvasculature. However, the majority of patients with COVID-19 infection experience only mild symptoms, and it is unknown if their myocardial perfusion is altered after infection. OBJECTIVES: The authors aimed to determine if there are abnormalities in myocardial blood flow (MBF), as measured by stress cardiac magnetic resonance (CMR), in individuals after a mild COVID-19 infection. METHODS: We conducted a prospective, comparative study of individuals who had a prior mild COVID-19 infection (n = 30) and matched controls (n = 26) using stress CMR. Stress and rest myocardial blood flow (sMBF, rMBF) were quantified using the dual sequence technique. Myocardial perfusion reserve was calculated as sMBF/rMBF. Unpaired t-tests were used to test differences between the groups. RESULTS: The median time interval between COVID-19 infection and CMR was 5.6 (IQR: 4-8) months. No patients with the COVID-19 infection required hospitalization. Symptoms including chest pain, shortness of breath, syncope, and palpitations were more commonly present in the group with prior COVID-19 infection than in the control group (57% vs 7%, P < 0.001). No significant differences in rMBF (1.08 ± 0.27 mL/g/min vs 0.97 ± 0.29 mL/g/min, P = 0.16), sMBF (3.08 ± 0.79 mL/g/min vs 3.06 ± 0.89 mL/g/min, P = 0.91), or myocardial perfusion reserve (2.95 ± 0.90 vs 3.39 ± 1.25, P = 0.13) were observed between the groups. CONCLUSIONS: This study suggests that there are no significant abnormalities in rest or stress myocardial perfusion, and thus microvascular function, in individuals after mild COVID-19 infection.

Automated Data Transformation and Feature Extraction for Oxygenation-Sensitive Cardiovascular Magnetic Resonance Images.

Oxygenation-sensitive cardiovascular magnetic resonance (OS-CMR) is a novel, powerful tool for assessing coronary function in vivo. The data extraction and analysis however are labor-intensive. The objective of this study was to provide an automated approach for the extraction, visualization, and biomarker selection of OS-CMR images. We created a Python-based tool to automate extraction and export of raw patient data, featuring 3336 attributes per participant, into a template compatible with common data analytics frameworks, including the functionality to select predictive features for the given disease state. Each analysis was completed in about 2 min. The features selected by both ANOVA and MIC significantly outperformed (p < 0.001) the null set and complete set of features in two datasets, with mean AUROC scores of 0.89eatures f 0.94lete set of features in two datasets, with mean AUROC scores that our tool is suitable for automated data extraction and analysis of OS-CMR images.

AI-powered assessment of biomarkers for growth prediction of abdominal aortic aneurysms.

Objective: The purpose of this study was to employ biomechanics-based biomarkers to locally characterize abdominal aortic aneurysm (AAA) tissue and investigate their relation to local aortic growth by means of an artificial intelligence model. Methods: The study focused on a population of 36 patients with AAAs undergoing serial monitoring with electrocardiogram-gated multiphase computed tomography angiography acquisitions. The geometries of the aortic lumen and wall were reconstructed from the baseline scans and used for the baseline assessment of regional aortic weakness with three functional biomarkers, time-averaged wall-shear stress, in vivo principal strain, and intra-luminal thrombus thickness. The biomarkers were encoded as regional averages on axial and circumferential sections perpendicularly to the aortic centerline. Local diametric growth was obtained as difference in diameter between baseline and follow-up at the level of each axial section. An artificial intelligence model was developed to predict accelerated aneurysmal growth with the Extra Trees algorithm used as a binary classifier where the positive class represented regions that grew more than 2.5 mm/year. Additional clinical biomarkers, such as maximum aortic diameter at baseline, were also investigated as predictors of growth. Results: The area under the curve for the constructed receiver operating characteristic curve for the Extra Trees classifier showed a very good performance in predicting relevant aortic growth (area under the curve = 0.92), with the three biomechanics-based functional biomarkers being objectively selected as the main predictors of growth. Conclusions: The use of features based on the functional and local characterization of the aortic tissue resulted in a superior performance in terms of growth prediction when compared with models based on geometrical assessments. With rapid growth linked to increasing risk for patients with AAAs, the ability to access functional information related to tissue weakening and disease progression at baseline has the potential to support early clinical decisions and improve disease management.

Second-Line Myocardial Perfusion Imaging to Detect Obstructive Stenosis: Head-to-Head Comparison of CMR and PET.

BACKGROUND: Guidelines recommend verification of myocardial ischemia by selective second-line myocardial perfusion imaging (MPI) following a coronary computed tomography angiography (CTA) with suspected obstructive coronary artery disease (CAD). Head-to-head data on the diagnostic performance of different MPI modalities in this setting are sparse. OBJECTIVES: The authors sought to compare, head-to-head, the diagnostic performance of selective MPI by 3.0-T cardiac magnetic resonance (CMR) and 82rubidium positron emission tomography (RbPET) in patients with suspected obstructive stenosis at coronary CTA using invasive coronary angiography (ICA) with fractional flow reserve (FFR) as reference. METHODS: Consecutive patients (n = 1,732, mean age: 59.1 ± 9.5 years, 57.2% men) referred for coronary CTA with symptoms suggestive of obstructive CAD were included. Patients with suspected stenosis were referred for both CMR and RbPET and subsequently ICA. Obstructive CAD was defined as FFR ≤0.80 or >90% diameter stenosis by visual assessment. RESULTS: In total, 445 patients had suspected stenosis on coronary CTA. Of these, 372 patients completed both CMR, RbPET and subsequent ICA with FFR. Hemodynamically obstructive CAD was identified in 164 of 372 (44.1%) patients. Sensitivities for CMR and RbPET were 59% (95% CI: 51%-67%) and 64% (95% CI: 56%-71%); P = 0.21, respectively, and specificities 84% (95% CI: 78%-89%) and 89% (95% CI: 84%-93%]); P = 0.08, respectively. Overall accuracy was higher for RbPET compared with CMR (73% vs 78%; P = 0.03). CONCLUSIONS: In patients with suspected obstructive stenosis at coronary CTA, CMR, and RbPET show similar and moderate sensitivities but high specificities compared with ICA with FFR. This patient group represents a diagnostic challenge with frequent mismatch between advanced MPI tests and invasive measurements. (Danish Study of Non-Invasive Diagnostic Testing in Coronary Artery Disease 2 [Dan-NICAD 2]; NCT03481712).

Electroencephalography signals and neurodevelopment after Kawasaki disease: A pilot study.

BACKGROUND: Acute Kawasaki disease (KD) induces central nervous system inflammation and excessive irritability. Long-term impacts on children's neurodevelopment have only been studied marginally. This pilot study aimed to describe the neuropsychological profile of primary school-aged children with a history of KD and to explore the impacts of KD on electroencephalography (EEG) markers associated with attention and brain maturation. METHODS: Fifteen children (8.8 ± 2.5 years) were recruited 4.9 ± 2.7 years after KD onset. Intellectual abilities, long-term memory, and auditory sustained attention were evaluated. Parents completed standardized questionnaires assessing (1) executive functioning; (2) internalizing and externalizing difficulties; (3) attention deficit hyperactivity disorder symptoms; and (4) autism spectrum disorder symptoms. Theta/beta ratio (TBR) and alpha peak (AP) were extracted from resting-state EEG and compared with 32 controls (8.9 ± 2.1 years). The alpha band was analyzed using a feature reduction algorithm to detect potential groupings. RESULTS: Performances showed preserved intellectual abilities and memory. Sustained attention performance was within the lower range for 4/14 participants (29%), with considerable parental reports of inattention (43%), working memory difficulties (50%), and hyperactivity-impulsivity (36%). No alterations in the TBR were found but the KD group presented a significantly lower AP amplitude ratio. A clear separation of KD cohort into two clusters showed that acute irritability is associated with a weaker AP. CONCLUSIONS: Despite overall preserved cognitive functions, there is a possible association between KD and attention deficit concerns. This first EEG-based study indicates alpha peak abnormality after KD, predominantly in children with acute irritability. Longitudinal studies are warranted to better characterize patients' neurodevelopmental trajectory.

Deep Learning-Based Approach to Automatically Assess Coronary Distensibility Following Kawasaki Disease.

Kawasaki disease is an acute vasculitis affecting children, which can lead to coronary artery (CA) aneurysms. Optical coherence tomography (OCT) has identified CA wall damage in KD patients, but it is unclear if these findings correlate with any distensibility changes in the CA and how these changes evolve over time. This paper seeks to establish the link between OCT findings and vessel distensibility with a novel deep learning coronary artery segmentation system and use the segmentation framework to automatically analyze the temporal evolution of coronary stiffness over many years. 27 KD patients underwent catheterization with coronary angiography of the left coronary artery (LCA), followed by OCT of proximal and distal segments of the LCA. Changes in the CA caliber over the cardiac cycle were measured automatically and compared against OCT findings suggestive of KD-related vascular damage. In addition, 34 KD patients with regressed or persistent CA aneurysms were followed with serial CA angiography over an average of 14.5 years. Distensibility changes were calculated using a deep learning coronary artery segmentation framework and evaluated longitudinally. Distensibility in the coronary arteries after KD negatively correlated with increasing severity of OCT findings of KD-related vessel damage. KD patients have a significant increase in CA wall stiffness at 1 year after diagnosis, which then plateaus subsequently, compared to controls. Also, patients with persistent CA aneurysms have a statistically significant increase in wall stiffness over time in comparison to those with regressed CA aneurysms. Distensibility changes in the CA of KD patients calculated using our automated deep learning approach correlates with the severity of OCT findings of KD-related CA damage. This decreased distensibility peaks at 1 year in KD patients when following longitudinally and is more severe in those with persistent CA aneurysms.

Automated Segmental Analysis of Fully Quantitative Myocardial Blood Flow Maps by First-Pass Perfusion Cardiovascular Magnetic Resonance.

First pass gadolinium-enhanced cardiovascular magnetic resonance (CMR) perfusion imaging allows fully quantitative pixel-wise myocardial blood flow (MBF) assessment, with proven diagnostic value for coronary artery disease. Segmental analysis requires manual segmentation of the myocardium. This work presents a fully automatic method of segmenting the left ventricular myocardium from MBF pixel maps, validated on a retrospective dataset of 247 clinical CMR perfusion studies, each including rest and stress images of three slice locations, performed on a 1.5T scanner. Pixel-wise MBF maps were segmented using an automated pipeline including region growing, edge detection, principal component analysis, and active contours to segment the myocardium, detect key landmarks, and divide the myocardium into sectors appropriate for analysis. Automated segmentation results were compared against a manually defined reference standard using three quantitative metrics: Dice coefficient, Cohen Kappa and myocardial border distance. Sector-wise average MBF and myocardial perfusion reserve (MPR) were compared using Pearson's correlation coefficient and Bland-Altman Plots. The proposed method segmented stress and rest MBF maps of 243 studies automatically. Automated and manual myocardial segmentation had an average (± standard deviation) Dice coefficient of 0.86 ± 0.06, Cohen Kappa of 0.86 ± 0.06, and Euclidian distances of 1.47 ± 0.73 mm and 1.02 ± 0.51 mm for the epicardial and endocardial border, respectively. Automated and manual sector-wise MBF and MPR values correlated with Pearson's coefficient of 0.97 and 0.92, respectively, while Bland-Altman analysis showed bias of 0.01 and 0.07 ml/g/min. The validated method has been integrated with our fully automated MBF pixel mapping pipeline to aid quantitative assessment of myocardial perfusion CMR.

Musical reward prediction errors engage the nucleus accumbens and motivate learning.

Enjoying music reliably ranks among life's greatest pleasures. Like many hedonic experiences, it engages several reward-related brain areas, with activity in the nucleus accumbens (NAc) most consistently reflecting the listener's subjective response. Converging evidence suggests that this activity arises from musical "reward prediction errors" (RPEs) that signal the difference between expected and perceived musical events, but this hypothesis has not been directly tested. In the present fMRI experiment, we assessed whether music could elicit formally modeled RPEs in the NAc by applying a well-established decision-making protocol designed and validated for studying RPEs. In the scanner, participants chose between arbitrary cues that probabilistically led to dissonant or consonant music, and learned to make choices associated with the consonance, which they preferred. We modeled regressors of trial-by-trial RPEs, finding that NAc activity tracked musically elicited RPEs, to an extent that explained variance in the individual learning rates. These results demonstrate that music can act as a reward, driving learning and eliciting RPEs in the NAc, a hub of reward- and music enjoyment-related activity.

Difference Between Persistent Aneurysm, Regressed Aneurysm, and Coronary Dilation in Kawasaki Disease: An Optical Coherence Tomography Study.

BACKGROUND: Coronary artery (CA) aneurysms are a serious complication of Kawasaki disease (KD). Conventional imaging techniques often described segments with regressed aneurysms as normal, whereas studies have shown significant endothelial dysfunction. METHODS: KD patients with aneurysms scheduled for routine coronary angiography underwent optical coherence tomography (OCT) imaging between 2013 and 2016. Microstructural coronary changes were compared between normal CA segments and those with dilation, regressed aneurysms, and persistent aneurysms. RESULTS: OCT was performed on 33 patients aged 12.0 ± 5.4 years, 8.5 ± 5.4 years after KD diagnosis. Of the 79 segments analyzed, 25 had persistent aneurysms, 22 regressed aneurysms, 11 CA dilation, and 21 no CA involvement. Intimal thickness was 489 ± 173 µm, 304 ± 158 µm, 102 ± 68 µm, and 63 ± 29 µm, respectively (P < 0.001). There was a linear correlation between the maximum aneurysm size and the intimal thickness, as well as coronary dimension at the time of OCT. Fibrosis (54 segments, 68%) and cellular infiltration (22 segments, 28%) were found more often in segments with CA involvement, but also those without (P = 0.01; P = 0.02). Destruction of the media (34 segments, 43%), calcifications (6 segments, 8%), neovascularization (18 segments, 23%), and white thrombi (8 segments, 10%) were found almost exclusively in segments with a history of aneurysms. CONCLUSIONS: Intimal hyperplasia, fibrosis, and cellular infiltration were found in all categories of CA involvement, whereas calcification, destruction of the media, neovascularization, and white thrombi were found essentially only in segments with saccular or fusiform aneurysms. Prospective studies with outcome correlations are needed to see if this is associated with an increased risk of late adverse events.

Diagnostic Performance of Fully Automated Pixel-Wise Quantitative Myocardial Perfusion Imaging by Cardiovascular Magnetic Resonance.

OBJECTIVES: The authors developed a fully automated framework to quantify myocardial blood flow (MBF) from contrast-enhanced cardiac magnetic resonance (CMR) perfusion imaging and evaluated its diagnostic performance in patients. BACKGROUND: Fully quantitative CMR perfusion pixel maps were previously validated with microsphere MBF measurements and showed potential in clinical applications, but the methods required laborious manual processes and were excessively time-consuming. METHODS: CMR perfusion imaging was performed on 80 patients with known or suspected coronary artery disease (CAD) and 17 healthy volunteers. Significant CAD was defined by quantitative coronary angiography (QCA) as ≥70% stenosis. Nonsignificant CAD was defined by: 1) QCA as <70% stenosis; or 2) coronary computed tomography angiography as <30% stenosis and a calcium score of 0 in all vessels. Automatically generated MBF maps were compared with manual quantification on healthy volunteers. Diagnostic performance of the automated MBF pixel maps was analyzed on patients using absolute MBF, myocardial perfusion reserve (MPR), and relative measurements of MBF and MPR. RESULTS: The correlation between automated and manual quantification was excellent (r = 0.96). Stress MBF and MPR in the ischemic zone were lower than those in the remote myocardium in patients with significant CAD (both p < 0.001). Stress MBF and MPR in the remote zone of the patients were lower than those in the normal volunteers (both p < 0.001). All quantitative metrics had good area under the curve (0.864 to 0.926), sensitivity (82.9% to 91.4%), and specificity (75.6% to 91.1%) on per-patient analysis. On a per-vessel analysis of the quantitative metrics, area under the curve (0.837 to 0.864), sensitivity (75.0% to 82.7%), and specificity (71.8% to 80.9%) were good. CONCLUSIONS: Fully quantitative CMR MBF pixel maps can be generated automatically, and the results agree well with manual quantification. These methods can discriminate regional perfusion variations and have high diagnostic performance for detecting significant CAD. (Technical Development of Cardiovascular Magnetic Resonance Imaging; NCT00027170).

Robust universal nonrigid motion correction framework for first-pass cardiac MR perfusion imaging.

PURPOSE: To present and assess an automatic nonrigid image registration framework that compensates motion in cardiac magnetic resonance imaging (MRI) perfusion series and auxiliary images acquired under a wide range of conditions to facilitate myocardial perfusion quantification. MATERIALS AND METHODS: Our framework combines discrete feature matching for large displacement estimation with a dense variational optical flow formulation in a multithreaded architecture. This framework was evaluated on 291 clinical subjects to register 1.5T and 3.0T steady-state free-precession (FISP) and fast low-angle shot (FLASH) dynamic contrast myocardial perfusion images, arterial input function (AIF) images, and proton density (PD)-weighted images acquired under breath-hold (BH) and free-breath (FB) settings. RESULTS: Our method significantly improved frame-to-frame appearance consistency compared to raw series, expressed in correlation coefficient (R2 = 0.996 ± 3.735E-3 vs. 0.978 ± 2.024E-2, P < 0.0001) and mutual information (3.823 ± 4.098E-1 vs. 2.967 ± 4.697E-1, P < 0.0001). It is applicable to both BH (R2 = 0.998 ± 3.217E-3 vs. 0.990 ± 7.527E-3) and FB (R2 = 0.995 ± 3.410E-3 vs. 0.968 ± 2.257E-3) paradigms as well as FISP and FLASH sequences. The method registers PD images to perfusion T1 series (9.70% max increase in R2 vs. no registration, P < 0.001) and also corrects motion in low-resolution AIF series (R2 = 0.987 ± 1.180E-2 vs. 0.964 ± 3.860E-2, P < 0.001). Finally, we showed the myocardial perfusion contrast dynamic was preserved in the motion-corrected images compared to the raw series (R2 = 0.995 ± 6.420E-3). CONCLUSION: The critical step of motion correction prior to pixel-wise cardiac MR perfusion quantification can be performed with the proposed universal system. It is applicable to a wide range of perfusion series and auxiliary images with different acquisition settings. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1060-1072.

Evaluation of an automated method for arterial input function detection for first-pass myocardial perfusion cardiovascular magnetic resonance.

BACKGROUND: Quantitative assessment of myocardial blood flow (MBF) with first-pass perfusion cardiovascular magnetic resonance (CMR) requires a measurement of the arterial input function (AIF). This study presents an automated method to improve the objectivity and reduce processing time for measuring the AIF from first-pass perfusion CMR images. This automated method is used to compare the impact of different AIF measurements on MBF quantification. METHODS: Gadolinium-enhanced perfusion CMR was performed on a 1.5 T scanner using a saturation recovery dual-sequence technique. Rest and stress perfusion series from 270 clinical studies were analyzed. Automated image processing steps included motion correction, intensity correction, detection of the left ventricle (LV), independent component analysis, and LV pixel thresholding to calculate the AIF signal. The results were compared with manual reference measurements using several quality metrics based on the contrast enhancement and timing characteristics of the AIF. The median and 95% confidence interval (CI) of the median were reported. Finally, MBF was calculated and compared in a subset of 21 clinical studies using the automated and manual AIF measurements. RESULTS: Two clinical studies were excluded from the comparison due to a congenital heart defect present in one and a contrast administration issue in the other. The proposed method successfully processed 99.63% of the remaining image series. Manual and automatic AIF time-signal intensity curves were strongly correlated with median correlation coefficient of 0.999 (95% CI [0.999, 0.999]). The automated method effectively selected bright LV pixels, excluded papillary muscles, and required less processing time than the manual approach. There was no significant difference in MBF estimates between manually and automatically measured AIFs (p = NS). However, different sizes of regions of interest selection in the LV cavity could change the AIF measurement and affect MBF calculation (p = NS to p = 0.03). CONCLUSION: The proposed automatic method produced AIFs similar to the reference manual method but required less processing time and was more objective. The automated algorithm may improve AIF measurement from the first-pass perfusion CMR images and make quantitative myocardial perfusion analysis more robust and readily available.

Anatomically distinct dopamine release during anticipation and experience of peak emotion to music.

Music, an abstract stimulus, can arouse feelings of euphoria and craving, similar to tangible rewards that involve the striatal dopaminergic system. Using the neurochemical specificity of [(11)C]raclopride positron emission tomography scanning, combined with psychophysiological measures of autonomic nervous system activity, we found endogenous dopamine release in the striatum at peak emotional arousal during music listening. To examine the time course of dopamine release, we used functional magnetic resonance imaging with the same stimuli and listeners, and found a functional dissociation: the caudate was more involved during the anticipation and the nucleus accumbens was more involved during the experience of peak emotional responses to music. These results indicate that intense pleasure in response to music can lead to dopamine release in the striatal system. Notably, the anticipation of an abstract reward can result in dopamine release in an anatomical pathway distinct from that associated with the peak pleasure itself. Our results help to explain why music is of such high value across all human societies.

The rewarding aspects of music listening are related to degree of emotional arousal.

BACKGROUND: Listening to music is amongst the most rewarding experiences for humans. Music has no functional resemblance to other rewarding stimuli, and has no demonstrated biological value, yet individuals continue listening to music for pleasure. It has been suggested that the pleasurable aspects of music listening are related to a change in emotional arousal, although this link has not been directly investigated. In this study, using methods of high temporal sensitivity we investigated whether there is a systematic relationship between dynamic increases in pleasure states and physiological indicators of emotional arousal, including changes in heart rate, respiration, electrodermal activity, body temperature, and blood volume pulse. METHODOLOGY: Twenty-six participants listened to self-selected intensely pleasurable music and "neutral" music that was individually selected for them based on low pleasure ratings they provided on other participants' music. The "chills" phenomenon was used to index intensely pleasurable responses to music. During music listening, continuous real-time recordings of subjective pleasure states and simultaneous recordings of sympathetic nervous system activity, an objective measure of emotional arousal, were obtained. PRINCIPAL FINDINGS: Results revealed a strong positive correlation between ratings of pleasure and emotional arousal. Importantly, a dissociation was revealed as individuals who did not experience pleasure also showed no significant increases in emotional arousal. CONCLUSIONS/SIGNIFICANCE: These results have broader implications by demonstrating that strongly felt emotions could be rewarding in themselves in the absence of a physically tangible reward or a specific functional goal.