Cardiac Wall Motion Research Articles

The protective effect of bergamot polyphenolic fraction on reno-cardiac damage induced by DOCA-salt and unilateral renal artery ligation in rats

Abstract Funding Acknowledgements None. The complex pathological interactions between renal and cardiovascular systems represent a real global epidemic and renovascular hypertension remains among the most prevalent, but also potentially reversible, risk factor for numerous reno-cardiac diseases in humans and pets. Here, we investigated the anti-inflammatory and reno-cardiac protective effects of a polyphenol-rich fraction of citrus bergamot (BPF) in an experimental model of hypertension induced by unilateral renal artery ligation (RAL). Adult male Wistar rats underwent unilateral renal artery ligation and treatment with 20 mg/kg deoxycorticosterone acetate (DOCA), twice a week for a period of 4 weeks, and 1% sodium chloride water. A subgroup of hypertensive rats received 100 mg/kg/day of BPF for 28 days, by gavage. Another group of animals was treated with a sub-cutaneous injection of vehicle (CTRL). RAL DOCA-Salt rats showed a significant increase in mean arterial blood pressure (MAP; p<0.05 vs CTRL) which strongly increased the resistive index (RI; p<0.05 vs CTRL) of contralateral renal artery flow and kidney volume after 4 weeks (p<0.001 vs CTRL). Renal dysfunction also led to a dysfunction of cardiac tissue strain associated with overt dyssynchrony in cardiac wall motion, as shown by the increased time-to-peak (T2P; p<0.05) and the decreased whole peak capacity (Pk; p<0.01) in displacement and strain rate (p<0.05) in longitudinal motion. The hearts of RAL DOCA-Salt rats showed a larger time delay between the fastest and the lowest region (Maximum Opposite Wall Delay-MOWD) (p<0.05 in displacement and p<0.01 in strain rate vs CTRL). A significant increase in the levels of the circulating pro-inflammatory cytokines and chemokines (p<0.05 for IL-12(40), p<0.01 for GM-CSF, KC, IL-13, and TNF- α) and in the NGAL expression of the ligated kidney (p<0.001) was observed.This pathological condition was prevented by BPF treatment, by reducing the increase of blood pressure in RAL DOCA-Salt rats (p<0.05) and exerting a protective effect on the volume of the contralateral kidney (p<0.01). BPF ameliorates cardiac tissue strain dysfunction by increasing Pk in displacement (p<0.01) and reducing the T2P in strain rate motion (p<0.05). These effects significantly improve MOWD (p<0.05) preventing the overt dyssynchrony in cardiac wall motion. The reno-cardiac protective effect of BPF was associated with a significant reduction in serum level of the pro-inflammatory cytokines and chemokines (p<0.05 for KC and IL-12(40), p<0.01 for GM-CSF, IL-13, and TNF- α)restoring physiological levels of NGAL (p<0.05) protein of the tethered kidney. In conclusion, the present results show that BPF promotes an efficient renovascular protection preventing the progression of inflammation and reno-cardiac damage, suggesting the potential clinical and veterinary role of dietary supplementation with the polyphenol-rich fraction of citrus bergamot in counteracting hypertension-induced reno-cardiac syndrome.

Enhancing reginal wall abnormality detection accuracy: Integrating machine learning, optical flow algorithms, and temporal convolutional networks in multi-view echocardiography.

Regional Wall Motion Abnormality (RWMA) serves as an early indicator of myocardial infarction (MI), the global leader in mortality. Accurate and early detection of RWMA is vital for the successful treatment of MI. Current automated echocardiography analyses typically concentrate on peak values from left ventricular (LV) displacement curves, based on LV contour annotations or key frames during the heart's systolic or diastolic phases within a single echocardiographic cycle. This approach may overlook the rich motion field features available in multi-cycle cardiac data, which could enhance RWMA detection. In this research, we put forward an innovative approach to detect RWMA by harnessing motion information across multiple echocardiographic cycles and multi-views. Our methodology synergizes U-Net-based segmentation with optical flow algorithms for detailed cardiac structure delineation, and Temporal Convolutional Networks (ConvNet) to extract nuanced motion features. We utilize a variety of machine learning and deep learning classifiers on both A2C and A4C views echocardiograms to enhance detection accuracy. A three-phase algorithm-originating from the HMC-QU dataset-incorporates U-Net for segmentation, followed by optical flow for cardiac wall motion field features. Temporal ConvNet, inspired by the Temporal Segment Network (TSN), is then applied to interpret these motion field features, independent of traditional cardiac parameter curves or specific key phase frame inputs. Employing five-fold cross-validation, our SVM classifier demonstrated high performance, with a sensitivity of 93.13%, specificity of 83.61%, precision of 88.52%, and an F1 score of 90.39%. When compared with other studies using the HMC-QU datasets, these Fig s stand out, underlining our method's effectiveness. The classifier also attained an overall accuracy of 89.25% and Area Under the Curve (AUC) of 95%, reinforcing its potential for reliable RWMA detection in echocardiographic analysis. This research not only demonstrates a novel technique but also contributes a more comprehensive and precise tool for early myocardial infarction diagnosis.

Abstract 17934: Performance of an Electrocardiographic Deep Learning Model for Detecting Wall Motion Abnormalities: An Analysis of Impact From Cardiovascular Comorbidities and Race in an External Validation Study

Introduction: Cardiac wall motion abnormalities (WMA) are key prognostic indicators for major cardiovascular events and mortality (MACE), yet poorly detected by current ECG screening such as for Q waves. Given that race and gender impact the ECG, it is also unknown if ECG screening for WMA could be applied broadly. We hypothesized that deep learning of the ECG could accurately identify WMA in diverse patients with varying comorbidities at 2 Institutions. Methods: We developed a DL Convolutional Neural Network model on N=35,210 ECG records from Stanford University (iECG, Philips), labeled by natural language processing of echocardiography reports within 60 days ( Panel A ). The tested model was then externally validated on an independent set of N=2338 ECGs from Emory University (Muse, GE). Patients with ventricular pacing were excluded. The patient cohort was segmented by cardiovascular comorbidity (<3, >=3) and white vs. non-white race to compare model performance. Results: The Stanford cohort (62.9±16.9 years, 47% female, 58% white, 6% black, 16% Asian, 23% >=3 comorbidities) differed from the Emory cohort (61.9±16.0 years, 48% female, 48% white, 41% black, 6% Asian, 52% >=3 comorbidities). Nevertheless, the Stanford model detected WMA in the Emory cohort with AUC 0.723. For patients with low and high comorbidity burdens, the AUC was 0.71 and 0.69, respectively. For White and Non-White patients, the AUC was 0.74/0.70 and 0.65/0.66, respectively ( Panel B ). Conclusions: Across racial groups and patients with variable burden of comorbidities, a DL model of electrocardiograms robustly identifies WMAs in an external validation cohort despite differences in the populations. The model’s performance across different comorbidity and racial demographic groups underscores its potential for general clinical application, offering an effective tool for clinical screening and enhanced risk stratification.

Abstract 19210: Interpretation of Deep Learning Model That Identifies Regional Myocardial Dysfunction From the Surface Ecg

Introduction: Cardiac wall motion abnormalities (WMA) are key prognostic indicators for major cardiovascular events and mortality, yet they are poorly detected by current ECG screening tools such as Q waves. Deep learning (DL) models have improved analysis of complex data and novel tools may provide interpretation of these models. Hypothesis: ECG features beyond the Q wave may be identified by analyzing an ECG-trained DL model to improve WMA detection over standard ECG measurements. Methods: We developed a DL neural network using 36,964 unique patients (62.9±16.9 years, 47% female, 58% white) labeled by natural language processing of echocardiography reports within 60 days. To understand how the CNN model identifies WMAs from the ECG input, we implemented Shapley Additive exPlanations (SHAP) and DeepExplainer on R-wave aligned beats over 300ms surrounding the QRS complexes to identify hot-spots across the ECG. We then retrained classifiers on 60mg windows of the ECG to identify which timepoints produced the greatest performance (ablation method). Results: Probing the CNN revealed that ECG regions throughout the QRS and T-waves identified WMA. Analysis output shows that such ECG regions arose throughout the QRS and T-waves, and not just at early regions corresponding to Q waves (Panel A) . An aggregate view of the ECG reports summarizes the most discriminatory ECG regions for all patients (Panel B) . Stepwise analysis of limited windows within aligned ECG beats also confirmed this analysis (Panel C) . We found that the highest performance window was 60-120ms (AUC 0.814), containing most of the QRS, with the lowest performance in the window of 0-60 ms (AUC 0.699) and 0.20-0.80ms (AUC 0.707). Conclusions: The DL model show promise in accurately detecting WMAs and provides key insights into critical ECG regions for WMA detection beyond the Q wave. Further analysis of WMA detection with these expanded regions is needed.

Abstract 17420: Enhanced Identification of Cardiac Wall Motion Abnormalities: An Externally Validated Deep Neural Network Approach Outperforms Expert and Quantitative Analysis of Electrocardiograms

Background: Cardiac wall motion abnormalities (WMA) independently predict mortality and other adverse events beyond ejection fraction. Clinical screening relies on detection of ECG Q waves yet has poor predictive accuracy. Hypothesis: We hypothesized that ECG features beyond the Q wave could be identified by deep learning to improve WMA detection over standard ECG measurements. Methods: We collected ECGs and echocardiogram pairs (Panel A) in 35,210 unique patients who underwent both studies within 60 days at Stanford University. WMAs were determined by natural language processing (NLP) of clinical reports (Panel B). Five second ECG signals were extracted digitally and used to develop and test a DNN. For development, 80% of the pairs were used and 20% (N = 7042) were reserved for internal validation. We then tested the model performance on 2,338 echo/ECG pairs from Emory University. For comparison, qualitative physician statements from the ECG interpretation suggesting myocardial injury, ischemia, or infarct were labeled using NLP, and a logistic regression model was developed to include quantitative Q-wave measurements. Results: In the internal validation cohort (62.9±16.9 years, 47% female, 58% white), DNN of the ECG detected echocardiographic WMA with a c-statistic of 0.781 (CI: 0.764-0.798) which was superior to quantitative ECG Q wave measurement (0.632, CI: 0.605-0.661) or qualitative interpretation (0.595) (Panel C). Performance was not improved by incorporating demographic data. In the external cohort (61.9±16.0 years, 48% female, 48% white), the DNN AUC was 0.723 (CI:0.689-0.758). Conclusion: Deep learning improves detection of cardiac WMAs from the electrocardiogram in an externally validated model compared with standard ECG analysis. Application of this model may provide improved screening for regional myocardial injury. Further work will determine which features beyond the Q wave provide improved performance.

Abstract 18102: Impact of Regional Cardiac Wall Motion Abnormalities on Ejection Fraction and Mortality: Large Data Enabled by Natural Language Processing

Background: Cardiac wall motion abnormalities (WMA) independently predict mortality and other adverse events beyond ejection fraction. The impact on mortality and ejection fraction has not been studied in large cohorts with long term follow up. Aim: We aimed to describe the relationship of specific regional cardiac WMAs with ejection fraction and mortality in a large referral academic center. Methods: We collected echocardiograms, demographics, and follow-up data from 35,210 patients from Stanford University. Specific regional abnormalities were determined by natural language processing (NLP) of clinical reports. WMAs were stratified across 5 anatomic regions and compared. Ejection fraction effect were compared by the Mann-Whitney U test and visualized with violin plots. Mortality between regions was assessed with Cox proportional hazards model and visualized with Kaplan-Meier plot. Results: In the Stanford University cohort, (62.9±16.9 years, 47% female, 58% white), regional WMAs were associated variable reductions in EF with apical, anterior, and septal WMAs resulting in an EF 41.6-42.8 +/- 12.8%, which was greater than lateral (45.4 +/- 10.2%) and inferior/posterior (46.3 +/- 12.1) (p< .01 for all comparisons). Distributions for inferior/posterior and lateral were more left-skewed and apex/septal were more bimodal ( Panel A ). In follow-up, having at least one WMA increased the risk of overall mortality with a hazard ratio of 1.8 (CI: 1.61-2.00) over a > 4 year period ( Panel B ). Conclusion: In a very large population of echocardiograms performed in an academic referral center, wall motion abnormalities in different areas were associated with variable effects on ejection fraction and long-term mortality. Natural language processing enabled analysis of a large database of echocardiogram reports.

Changes in blood flow vortices inside the left ventricle in COVID-19 patients with intraventricular clot despite a normal coronary and myocardial motion.

Our clinical observations showed clot formations in different regions of the left ventricle of the heart in some COVID-19 patients with normal myocardial motion and coronary artery. The aim of this study was to examine the changes caused by COVID-19 disease on blood flow inside the heart as a possible etiology of intracardiac clot formation. In a synergic convergence of mathematics, computer science, and cardio-vascular medicine, we evaluated patients hospitalized due to COVID-19 without cardiac symptoms who underwent two-dimensional echocardiography. Patients with normal myocardial motions on echocardiography, normal coronary findings on noninvasive cardio-vascular diagnostic tests, and normal cardiac biochemical examinations but who presented with a clot in their left ventricle were included. To display the velocity vectors of the blood in the left ventricle, motion and deformation echocardiographic data were imported into MATLAB software. Analysis and output of the MATLAB program indicted anomalous blood flow vortices inside the left ventricular cavity, indicating irregular flow and turbulence of the blood inside the left ventricle in COVID-19 patients. Our results suggest that in some COVID-19 patients, cardiac wall motion is not satisfactorily able to circulate the blood fluid in normal directions and that, despite normal myocardium, changes in the directions of blood flow inside the left ventricle might lead to clots in different zones. This phenomenon may be related to changes in blood properties, such as viscosity.

Acute right insular ischaemic lesions and poststroke left ventricular dysfunction

IntroductionMyocardial injury related to acute ischaemic stroke is common even without primary cardiac disease. We intended to determine associations between values of left ventricular ejection fraction (LVEF) and ischaemic stroke...

Strain echocardiography in predicting LV dysfunction in RV apical pacing

Right ventricular (RV) pacing is associated with a reduction in left ventricular (LV) systolic function, thought to be mediated by pacing-induced ventricular dyssynchrony. The prevalence of heart failure after RV pacing is reported to range from 31±3%. We studied 60 subjects with high-grade atrioventricular block and Complete Heart Block (CHB) scheduled to undergo right ventricular apical pacing. 2D echocardiography was done at baseline, 1 month and 12 months. Pacing-induced cardiomyopathy was defined as a reduction in LVEF to <45%. Strain was evaluated off-line from digitally stored images using all advanced software package (cardiac wall motion quantification (CMQ); Toshiba Medical Systems). Longitudinal strain for individual myocardial segments was measured from the apical four-chamber, two-chamber and long axis views (16 segment AHA/ASE model). None had LV dysfunction at baseline based on 2D and strain echo imaging. Subsequently 18 patients were detected to develop low GLS score (less than -14.5) at 1 month. On subsequent follow up at 1 year, all 18 patients developed LV dysfunction on 2D Echocardiography. Thus Strain imaging with GLS score helped in early detection of LV dysfunction in RV apical pacing subjects. Pacing-induced cardiomyopathy had significant association with high grade AV block with pacemaker dependency. It had no significant associations with other comorbidities like diabetes, hypertension, ischemic heart disease or with the type of medication intake. However there was a statistically significant association with heart failure.

Efficacy and Safety of Angiotensin Receptor Blockers in a Pre-Clinical Model of Arrhythmogenic Cardiomyopathy.

Arrhythmogenic Cardiomyopathy (ACM) is a familial heart disease, characterized by contractile dysfunction, ventricular arrhythmias (VAs), and the risk of sudden cardiac death. Currently, implantable cardioverter defibrillators and antiarrhythmics are the mainstays in ACM therapeutics. Angiotensin receptor blockers (ARBs) have been highlighted in the treatment of heart diseases, including ACM. Yet, recent research has additionally implicated ARBs in the genesis of VAs and myocardial lipolysis via the peroxisome proliferator-activated receptor gamma (PPARγ) pathway. The latter is of particular interest, as fibrofatty infiltration is a pathological hallmark in ACM. Here, we tested two ARBs, Valsartan and Telmisartan, and the PPAR agonist, Rosiglitazone, in an animal model of ACM, homozygous Desmoglein-2 mutant mice (Dsg2mut/mut). Cardiac function, premature ventricular contractions (PVCs), fibrofatty scars, PPARα/γ protein levels, and PPAR-mediated mRNA transcripts were assessed. Of note, not a single mouse treated with Rosiglitazone made it to the study endpoint (i.e., 100% mortality: n = 5/5). Telmisartan-treated Dsg2mut/mut mice displayed the preservation of contractile function (percent ejection fraction [%EF]; 74.8 ± 6.8%EF) compared to Vehicle- (42.5 ± 5.6%EF) and Valsartan-treated (63.1 ± 4.4%EF) mice. However, Telmisartan-treated Dsg2mut/mut mice showed increased cardiac wall motion abnormalities, augmented %PVCs, electrocardiographic repolarization/depolarization abnormalities, larger fibrotic lesions, and increased expression of PPARy-regulated gene transcripts compared to their Dsg2mut/mut counterparts. Alternatively, Valsartan-treated Dsg2mut/mut mice harbored fewer myocardial scars, reduced %PVC, and increased Wnt-mediated transcripts. Considering our findings, caution should be taken by physicians when prescribing medications that may increase PPARy signaling in patients with ACM.

Detection of left ventricular wall motion abnormalities from volume rendering of 4DCT cardiac angiograms using deep learning.

BackgroundThe presence of left ventricular (LV) wall motion abnormalities (WMA) is an independent indicator of adverse cardiovascular events in patients with cardiovascular diseases. We develop and evaluate the ability to detect cardiac wall motion abnormalities (WMA) from dynamic volume renderings (VR) of clinical 4D computed tomography (CT) angiograms using a deep learning (DL) framework.MethodsThree hundred forty-three ECG-gated cardiac 4DCT studies (age: 61 ± 15, 60.1% male) were retrospectively evaluated. Volume-rendering videos of the LV blood pool were generated from 6 different perspectives (i.e., six views corresponding to every 60-degree rotation around the LV long axis); resulting in 2058 unique videos. Ground-truth WMA classification for each video was performed by evaluating the extent of impaired regional shortening visible (measured in the original 4DCT data). DL classification of each video for the presence of WMA was performed by first extracting image features frame-by-frame using a pre-trained Inception network and then evaluating the set of features using a long short-term memory network. Data were split into 60% for 5-fold cross-validation and 40% for testing.ResultsVolume rendering videos represent ~800-fold data compression of the 4DCT volumes. Per-video DL classification performance was high for both cross-validation (accuracy = 93.1%, sensitivity = 90.0% and specificity = 95.1%, κ: 0.86) and testing (90.9, 90.2, and 91.4% respectively, κ: 0.81). Per-study performance was also high (cross-validation: 93.7, 93.5, 93.8%, κ: 0.87; testing: 93.5, 91.9, 94.7%, κ: 0.87). By re-binning per-video results into the 6 regional views of the LV we showed DL was accurate (mean accuracy = 93.1 and 90.9% for cross-validation and testing cohort, respectively) for every region. DL classification strongly agreed (accuracy = 91.0%, κ: 0.81) with expert visual assessment.ConclusionsDynamic volume rendering of the LV blood pool combined with DL classification can accurately detect regional WMA from cardiac CT.

Geometrically Reduced Modelling of Pulsatile Flow in Perivascular Networks

Flow of cerebrospinal fluid in perivascular spaces is a key mechanism underlying brain transport and clearance. In this paper, we present a mathematical and numerical formalism for reduced models of pulsatile viscous fluid flow in networks of generalized annular cylinders. We apply this framework to study cerebrospinal fluid flow in perivascular spaces induced by pressure differences, cardiac pulse wave-induced vascular wall motion and vasomotion. The reduced models provide approximations of the cross-section average pressure and cross-section flux, both defined over the topologically one-dimensional centerlines of the network geometry. Comparing the full and reduced model predictions, we find that the reduced models capture pulsatile flow characteristics and provide accurate pressure and flux predictions across the range of idealized and image-based scenarios investigated—at a fraction of the computational cost of the corresponding full models. The framework presented thus provides a robust and effective computational approach for large scale in-silico studies of pulsatile perivascular fluid flow and transport.

Abstract 14284: "Weeding" Out Wellens' Syndrome

Introduction: Wellens’ syndrome, most often associated with proximal left anterior descending coronary artery stenosis, is considered highly specific for impending myocardial infarction (MI). Its diagnosis relies on symmetric biphasic or deeply inverted T waves in leads V1-V4, normal precordial R wave progression and normal to slightly elevated cardiac markers. Pseudo-Wellens’ syndrome presents similarly, but in the absence of coronary artery disease (CAD). We present a challenging case of a patient with history of excessive recreational cannabis use and incidental electrocardiogram (ECG) findings concerning for Wellens’ syndrome who ultimately was diagnosed with Pseudo-Wellens’ syndrome. With familiarity of cannabis associated Pseudo-Wellens’ syndrome, unnecessary invasive diagnostic and therapeutic procedures were avoided. Case: A 20 year old male without significant past medical history presented for evaluation of vomiting and abdominal pain of 2 days duration. In view of his daily cannabis use, he was diagnosed with cannabis hyperemesis syndrome. An ECG to measure QTc prior to administration of ondansetron demonstrated ST elevation in lead V2. A repeat ECG showed T-wave inversions in leads V2-4. Troponin levels were negative. The patient denied chest pain or shortness of breath. An echocardiogram showed normal cardiac wall motion and an estimated ejection fraction of 55-60%. The diagnosis of Pseudo-Wellens’ syndrome secondary to cannabis use was made and the patient was subsequently discharged home following extensive cannabis cessation counseling. Discussion: The definitive treatment of Wellens’ Syndrome relies on cardiac catheterization even in patients without cardiac history or risk factors for CAD. This immediate intervention is required to halt the evolution of MI and is crucial for a positive outcome. In contrast, Pseudo-Wellens’ syndrome occurs in the absence of CAD and has been reported in younger, healthy patients in association with illicit drug use. In the era of increased legalized marijuana use, it is imperative that health care providers are familiar with cannabis induced Pseudo-Wellens’ syndrome as to avoid the potential for inappropriate invasive diagnostic modalities in otherwise healthy individuals.

Association of major adverse cardiovascular events in stroke patients with cardiac wall motion abnormalities

Association of major adverse cardiovascular events in stroke patients with cardiac wall motion abnormalities

Predicting post-operative right ventricular failure using video-based deep learning

Despite progressive improvements over the decades, the rich temporally resolved data in an echocardiogram remain underutilized. Human assessments reduce the complex patterns of cardiac wall motion, to a small list of measurements of heart function. All modern echocardiography artificial intelligence (AI) systems are similarly limited by design – automating measurements of the same reductionist metrics rather than utilizing the embedded wealth of data. This underutilization is most evident where clinical decision making is guided by subjective assessments of disease acuity. Predicting the likelihood of developing post-operative right ventricular failure (RV failure) in the setting of mechanical circulatory support is one such example. Here we describe a video AI system trained to predict post-operative RV failure using the full spatiotemporal density of information in pre-operative echocardiography. We achieve an AUC of 0.729, and show that this ML system significantly outperforms a team of human experts at the same task on independent evaluation.

Association of Major Adverse Cardiovascular Events in Patients With Stroke and Cardiac Wall Motion Abnormalities.

BackgroundThe association of cardiac wall motion abnormalities (CWMAs) in patients with stroke who have major adverse cardiovascular events (MACE) remains unclear. The purpose of this study was to estimate the 50‐month risk of MACE, including stroke recurrence, acute coronary events, and vascular death in patients with stroke who have CWMAs.Methods and ResultsWe performed a retrospective analysis of prospectively collected acute stroke data (acute stroke and transient ischemic attack) over 50 months by electronic medical records. Data included demographic and clinical information, vascular imaging, and echocardiography data including CWMAs and MACE. Of a total of 2653 patients with acute stroke/transient ischemic attack, CWMA was observed in 355 (13.4%). In patients with CWMAs, the embolic stroke of undetermined source (50.7%) was the most frequent index stroke subtype and stroke recurrences (P=0.001). In multivariate Cox regression after adjustment for demographics, traditional risk, and confounding factors, CWMA was independently associated with a higher risk of MACE (adjusted hazard ratio [HR], 1.74; 95% CI, 1.37–2.21 [P=0.001]). Similarly, CWMA independently conferred an increased risk for ischemic stroke recurrence (adjusted HR, 1.50; 95% CI, 1.01–2.17 [P=0.04]), risk of acute coronary events (aHR, 2.50; 95% CI, 1.83–3.40 [P=0.001]) and vascular death (adjusted HR, 1.57; 95% CI, 1.04–2.40 [P=0.03]), in comparison to the patients with stroke without CWMA.ConclusionsIn a multiethnic cohort of ischemic stroke with CWMA, CWMA was associated with 1.7‐fold higher risks of MACE independent of established risk factors. Embolic stroke of undetermined source was the most common stroke association with CWMA. Patients with stroke should be screened for CWMA to identify those at higher risk of MACE.

Enhancement of in vivo cardiac photoacoustic signal specificity using spatiotemporal singular value decomposition.

.Significance: Photoacoustic imaging (PAI) can be used to infer molecular information about myocardial health non-invasively in vivo using optical excitation at ultrasonic spatial resolution. For clinical and preclinical linear array imaging systems, conventional delay-and-sum (DAS) beamforming is typically used. However, DAS cardiac PA images are prone to artifacts such as diffuse quasi-static clutter with temporally varying noise-reducing myocardial signal specificity. Typically, multiple frame averaging schemes are utilized to improve the quality of cardiac PAI, which affects the spatial and temporal resolution and reduces sensitivity to subtle PA signal variation. Furthermore, frame averaging might corrupt myocardial oxygen saturation quantification due to the presence of natural cardiac wall motion. In this paper, a spatiotemporal singular value decomposition (SVD) processing algorithm is proposed to reduce DAS PAI artifacts and subsequent enhancement of myocardial signal specificity.Aim: Demonstrate enhancement of PA signals from myocardial tissue compared to surrounding tissues and blood inside the left-ventricular (LV) chamber using spatiotemporal SVD processing with electrocardiogram (ECG) and respiratory signal (ECG-R) gated in vivo murine cardiac PAI.Approach: In vivo murine cardiac PAI was performed by collecting single wavelength (850 nm) photoacoustic channel data on eight healthy mice. A three-dimensional (3D) volume of complex PAI data over a cardiac cycle was reconstructed using a custom ECG-R gating algorithm and DAS beamforming. Spatiotemporal SVD was applied on a two-dimensional Casorati matrix generated using the 3D volume of PAI data. The singular value spectrum (SVS) was then filtered to remove contributions from diffuse quasi-static clutter and random noise. Finally, SVD processed beamformed images were derived using filtered SVS and inverse SVD computations.Results: Qualitative comparison with DAS and minimum variance (MV) beamforming shows that SVD processed images had better myocardial signal specificity, contrast, and target detectability. DAS, MV, and SVD images were quantitatively evaluated by calculating contrast ratio (CR), generalized contrast-to-noise ratio (gCNR), and signal-to-noise ratio (SNR). Quantitative evaluations were done at three cardiac time points (during systole, at end-systole (ES), and during diastole) identified from co-registered ultrasound M-Mode image. Mean CR, gCNR, and SNR values of SVD images at ES were 245, 115.15, and 258.17 times higher than DAS images with statistical significance evaluated with one-way analysis of variance.Conclusions: Our results suggest that significantly better-quality images can be realized using spatiotemporal SVD processing for in vivo murine cardiac PAI.

New regional drug delivery system by direct epicardial placement of slow-release prostacyclin agonist promise therapeutic angiogenesis in a porcine chronic myocardial infarction.

Although prostacyclin is an endogenous factor for the protection and regeneration of damaged tissue, the use of clinically available prostacyclin analogues for treating chronic pathological conditions is limited owing to their short half-lives. A new reagent, ONO-1301SR, which is a unique synthetic prostacyclin agonist polymerized with lactic and glycolic acid, has been demonstrated to constitutively release prostacyclin analogues to adjacent tissues, suggesting its therapeutic potential via slow-release delivery into a specific organ. In this study, we investigated the regenerative effect of direct epicardial delivery of the ONO-1301SR on a heart with a chronic myocardial infarct. An ameroid constrictor was placed on the left anterior descending coronary artery of Göttingen minipigs for 4weeks to induce ischemic cardiomyopathy; this was followed by direct epicardial placement of ONO-1301SR-immersed gelatinous sheet, or only a gelatinous sheet on the anterolateral surface of the heart. Epicardial placement of ONO-1301SR resulted in significant recovery of global cardiac functions and regional wall motion of the lateral wall. Importantly, after epicardial placement of ONO-1301SR for 4weeks, the myocardial blood flow significantly increased in the lateral region as assessed by 13N-ammonia positron emission tomography; this finding was consistent with significantly increased capillary density in the peri-infarct area with up-regulated angiogenic cytokine expression. Conclusion: Use of the slow-release drug delivery system of prostacyclin agonist yielded regenerative angiogenesis, including increased regional blood perfusion and systolic function in a porcine model of chronic myocardial infarction.

A contemporary review of clinical significances of percutaneous coronary intervention for chronic total occlusions, with some Japanese insights.

The clinical "significance" of percutaneous coronary intervention for coronary chronic total occlusion (CTO-PCI) has been evaluated. In the beginning, the effects on clinical endpoints were investigated by comparisons between cases of success and failure of CTO-PCI, which mostly demonstrated better long-term outcomes in the successful cases. Similarly, improvement of cardiac function or wall motion was proven by serial observational studies. Accordingly, several prospective randomized trials (RCTs), which should confirm such accumulated potential benefits, were recently conducted by comparison with studies of patients that had received optical medical therapy (OMT) alone. While they mostly demonstrated significant improvement of angina symptoms and quality of life (QOL) in the CTO-PCI group, they failed to prove a reduction of clinical events or improvement of left ventricle wall motion, compared with OMT. Concurrent guidelines or consensus documents emphasize that the principal indication for CTO-PCI is to improve symptoms. To determine strategy, the following must be discussed in each individual case: the probability of procedural success, the expectation of long-term patency, and an assessment of the balance between procedure-related complications and overall benefits. In essence, we believe the following facts to be the current sincere appraisal of CTO-PCI: (1) improvements of symptoms and QOL are established, but the others remain inconclusive, and; (2) their margins for improvement are narrowing and numbers of candidates are shrinking. Precision medicine or individualization may be the right directions to take, to enhance the potential of this treatment. This course of action demands discrimination of those candidates who will truly receive benefits from invasive treatment, and that still requires further clinical studies or actions.

Parametric Methods for the Regional Assessment of Cardiac Wall Motion Abnormalities: Comparison Study

Left ventricular (LV) dysfunction is mainly assessed by global contractile indices such as ejection fraction and LV Volumes in cardiac MRI. While these indices give information about the presence or not of LV alteration, they are not able to identify the location and the size of such alteration. The aim of this study is to compare the performance of three parametric imaging techniques used in cardiac MRI for the regional quantification of cardiac dysfunction. The proposed approaches were evaluated on 20 patients with myocardial infarction and 20 subjects with normal function. Three parametric images approaches: covariance analysis, parametric images based on Hilbert transform and those based on the monogenic signal were evaluated using cine-MRI frames acquired in three planes of views. The results show that parametric images generated from the monogenic signal were superior in term of sensitivity (89.69%), specificity (86.51%) and accuracy (89.06%) to those based on covariance analysis and Hilbert transform in the detection of contractile dysfunction related to myocardial infarction. Therefore, the parametric image based on the monogenic signal is likely to provide additional regional indices about LV dysfunction and it may be used in clinical practice as a tool for the analysis of the myocardial alterations.