Image-Decomposition-Enhanced Deep Learning for Detection of Rotor Cores in Cardiac Fibrillation

OBJECTIVE

Rotors, regions of spiral wave reentry in cardiac tissues, are considered as the drivers of atrial fibrillation (AF), the most common arrhythmia. Whereas physics-based approaches have been widely deployed to detect the rotors, in-depth knowledge in cardiac physiology and electrogram interpretation skills are typically needed. The recent leap forward in smart sensing, data acquisition, and Artificial Intelligence (AI) has offered an unprecedented opportunity to transform diagnosis and treatment in cardiac ailment, including AF. This study aims to develop an image-decomposition-enhanced deep learning framework for automatic identification of rotor cores on both simulation and optical mapping data.

METHODS

We adopt the Ensemble Empirical Mode Decomposition algorithm (EEMD) to decompose the original image, and the most representative component is then fed into a You-Only-Look-Once (YOLO) object-detection architecture for rotor detection. Simulation data from a bi-domain simulation model and optical mapping acquired from isolated rabbit hearts are used for training and validation.

RESULTS

This integrated EEMD-YOLO model achieves high accuracy on both simulation and optical mapping data (precision: 97.2%, 96.8%, recall: 93.8%, 92.2%, and F1 score: 95.5%, 94.4%, respectively).

CONCLUSION

The proposed EEMD-YOLO yields comparable accuracy in rotor detection with the gold standard in literature.

Mapping of Neuro-Cardiac Electrophysiology: Interlinking Epilepsy and Arrhythmia

The interplay between neurology and cardiology has gained significant attention in recent years, particularly regarding the shared pathophysiological mechanisms and clinical comorbidities observed in epilepsy and arrhythmias. Neuro-cardiac electrophysiology mapping involves the comprehensive assessment of both neural and cardiac electrical activity, aiming to unravel the intricate connections and potential cross-talk between the brain and the heart. The emergence of artificial intelligence (AI) has revolutionized the field by enabling the analysis of large-scale data sets, complex signal processing, and predictive modeling. AI algorithms have been applied to neuroimaging, electroencephalography (EEG), electrocardiography (ECG), and other diagnostic modalities to identify subtle patterns, classify disease subtypes, predict outcomes, and guide personalized treatment strategies. In this review, we highlight the potential clinical implications of neuro-cardiac mapping and AI in the management of epilepsy and arrhythmias. We address the challenges and limitations associated with these approaches, including data quality, interpretability, and ethical considerations. Further research and collaboration between neurologists, cardiologists, and AI experts are needed to fully unlock the potential of this interdisciplinary field.

The Impact of Microbiota on the Gut-Brain Axis: Examining the Complex Interplay and Implications

The association and interaction between the central nervous system (CNS) and enteric nervous system (ENS) is well established. Essentially ENS is the second brain, as we call it. We tried to understand the structure and function, to throw light on the functional aspect of neurons, and address various disease manifestations. We summarized how various neurological disorders influence the gut via the enteric nervous system and/or bring anatomical or physiological changes in the enteric nervous system or the gut and vice versa. It is known that stress has an effect on Gastrointestinal (GI) motility and causes mucosal erosions. In our literature review, we found that stress can also affect sensory perception in the central nervous system. Interestingly, we found that mutations in the neurohormone, serotonin (5-HT), would result in dysfunctional organ development and further affect mood and behavior. We focused on the developmental aspects of neurons and cognition and their relation to nutritional absorption via the gastrointestinal tract, the development of neurodegenerative disorders in relation to the alteration in gut microbiota, and contrariwise associations between CNS disorders and ENS. This paper further summarizes the synergetic relation between gastrointestinal and neuropsychological manifestations and emphasizes the need to include behavioral therapies in management plans.

Prospective of Pancreatic Cancer Diagnosis Using Cardiac Sensing

Pancreatic carcinoma (Ca Pancreas) is the third leading cause of cancer-related deaths in the world. The malignancies of the pancreas can be diagnosed with the help of various imaging modalities. An endoscopic ultrasound with a tissue biopsy is so far considered to be the gold standard in terms of the detection of Ca Pancreas, especially for lesions <2 mm. However, other methods, like computed tomography (CT), ultrasound, and magnetic resonance imaging (MRI), are also conventionally used. Moreover, newer techniques, like proteomics, radiomics, metabolomics, and artificial intelligence (AI), are slowly being introduced for diagnosing pancreatic cancer. Regardless, it is still a challenge to diagnose pancreatic carcinoma non-invasively at an early stage due to its delayed presentation. Similarly, this also makes it difficult to demonstrate an association between Ca Pancreas and other vital organs of the body, such as the heart. A number of studies have proven a correlation between the heart and pancreatic cancer. The tumor of the pancreas affects the heart at the physiological, as well as the molecular, level. An overexpression of the SMAD4 gene; a disruption in biomolecules, such as IGF, MAPK, and ApoE; and increased CA19-9 markers are a few of the many factors that are noted to affect cardiovascular systems with pancreatic malignancies. A comprehensive review of this correlation will aid researchers in conducting studies to help establish a definite relation between the two organs and discover ways to use it for the early detection of Ca Pancreas.

Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives

The measurement of physiologic pressure helps diagnose and prevent associated health complications. From typical conventional methods to more complicated modalities, such as the estimation of intracranial pressures, numerous invasive and noninvasive tools that provide us with insight into daily physiology and aid in understanding pathology are within our grasp. Currently, our standards for estimating vital pressures, including continuous BP measurements, pulmonary capillary wedge pressures, and hepatic portal gradients, involve the use of invasive modalities. As an emerging field in medical technology, artificial intelligence (AI) has been incorporated into analyzing and predicting patterns of physiologic pressures. AI has been used to construct models that have clinical applicability both in hospital settings and at-home settings for ease of use for patients. Studies applying AI to each of these compartmental pressures were searched and shortlisted for thorough assessment and review. There are several AI-based innovations in noninvasive blood pressure estimation based on imaging, auscultation, oscillometry and wearable technology employing biosignals. The purpose of this review is to provide an in-depth assessment of the involved physiologies, prevailing methodologies and emerging technologies incorporating AI in clinical practice for each type of compartmental pressure measurement. We also bring to the forefront AI-based noninvasive estimation techniques for physiologic pressure based on microwave systems that have promising potential for clinical practice.

Digital Pulmonology Practice with Phonopulmography Leveraging Artificial Intelligence: Future Perspectives Using Dual Microwave Acoustic Sensing and Imaging

Respiratory disorders, being one of the leading causes of disability worldwide, account for constant evolution in management technologies, resulting in the incorporation of artificial intelligence (AI) in the recording and analysis of lung sounds to aid diagnosis in clinical pulmonology practice. Although lung sound auscultation is a common clinical practice, its use in diagnosis is limited due to its high variability and subjectivity. We review the origin of lung sounds, various auscultation and processing methods over the years and their clinical applications to understand the potential for a lung sound auscultation and analysis device. Respiratory sounds result from the intra-pulmonary collision of molecules contained in the air, leading to turbulent flow and subsequent sound production. These sounds have been recorded via an electronic stethoscope and analyzed using back-propagation neural networks, wavelet transform models, Gaussian mixture models and recently with machine learning and deep learning models with possible use in asthma, COVID-19, asbestosis and interstitial lung disease. The purpose of this review was to summarize lung sound physiology, recording technologies and diagnostics methods using AI for digital pulmonology practice. Future research and development in recording and analyzing respiratory sounds in real time could revolutionize clinical practice for both the patients and the healthcare personnel.

Association of multiple sclerosis with chronic fatigue syndrome, restless legs syndrome, and various sleep disorders, along with the recent updates

Multiple sclerosis (MS) and myalgic encephalomyelitis (ME)/chronic fatigue syndrome (CFS) share the symptom of fatigue, and might even coexist together. Specifically focusing on genetics, pathophysiology, and neuroimaging data, the authors discuss an overview of the parallels, correlation, and differences in fatigue between MS and ME/CFS along with ME/CFS presence in MS. Studies have revealed that the prefrontal cortex and basal ganglia regions, which are involved in fatigue regulation, have similar neuroimaging findings in the brains of people with both MS and ME/CFS. Additionally, in both conditions, genetic factors have been implicated, with particular genes known to enhance susceptibility to MS and CFS. Management approaches for fatigue in MS and ME/CFS differ based on the underlying factors contributing to fatigue. The authors also focus on the recent updates and the relationship between MS and sleep disorders, including restless legs syndrome, focusing on pathophysiology and therapeutic approaches. Latest therapeutic approaches like supervised physical activity and moderate-intensity exercises have shown better outcomes.

Practicing Digital Gastroenterology through Phonoenterography Leveraging Artificial Intelligence: Future Perspectives Using Microwave Systems

Production of bowel sounds, established in the 1900s, has limited application in existing patient-care regimes and diagnostic modalities. We review the physiology of bowel sound production, the developments in recording technologies and the clinical application in various scenarios, to understand the potential of a bowel sound recording and analysis device-the phonoenterogram in future gastroenterological practice. Bowel sound production depends on but is not entirely limited to the type of food consumed, amount of air ingested and the type of intestinal contractions. Recording technologies for extraction and analysis of these include the wavelet-based filtering, autoregressive moving average model, multivariate empirical mode decompression, radial basis function network, two-dimensional positional mapping, neural network model and acoustic biosensor technique. Prior studies evaluate the application of bowel sounds in conditions such as intestinal obstruction, acute appendicitis, large bowel disorders such as inflammatory bowel disease and bowel polyps, ascites, post-operative ileus, sepsis, irritable bowel syndrome, diabetes mellitus, neurodegenerative disorders such as Parkinson's disease and neonatal conditions such as hypertrophic pyloric stenosis. Recording and analysis of bowel sounds using artificial intelligence is crucial for creating an accessible, inexpensive and safe device with a broad range of clinical applications. Microwave-based digital phonoenterography has huge potential for impacting GI practice and patient care.

Machine Learning-Based Classification of Abnormal Liver Tissues Using Relative Permittivity

The search for non-invasive, fast, and low-cost diagnostic tools has gained significant traction among many researchers worldwide. Dielectric properties calculated from microwave signals offer unique insights into biological tissue. Material properties, such as relative permittivity (εr) and conductivity (σ), can vary significantly between healthy and unhealthy tissue types at a given frequency. Understanding this difference in properties is key for identifying the disease state. The frequency-dependent nature of the dielectric measurements results in large datasets, which can be postprocessed using artificial intelligence (AI) methods. In this work, the dielectric properties of liver tissues in three mouse models of liver disease are characterized using dielectric spectroscopy. The measurements are grouped into four categories based on the diets or disease state of the mice, i.e., healthy mice, mice with non-alcoholic steatohepatitis (NASH) induced by choline-deficient high-fat diet, mice with NASH induced by western diet, and mice with liver fibrosis. Multi-class classification machine learning (ML) models are then explored to differentiate the liver tissue groups based on dielectric measurements. The results show that the support vector machine (SVM) model was able to differentiate the tissue groups with an accuracy up to 90%. This technology pipeline, thus, shows great potential for developing the next generation non-invasive diagnostic tools.

Advances in Cardiac Pacing: Arrhythmia Prediction, Prevention and Control Strategies

Cardiac arrhythmias constitute a tremendous burden on healthcare and are the leading cause of mortality worldwide. An alarming number of people have been reported to manifest sudden cardiac death as the first symptom of cardiac arrhythmias, accounting for about 20% of all deaths annually. Furthermore, patients prone to atrial tachyarrhythmias such as atrial flutter and fibrillation often have associated comorbidities including hypertension, ischemic heart disease, valvular cardiomyopathy and increased risk of stroke. Technological advances in electrical stimulation and sensing modalities have led to the proliferation of medical devices including pacemakers and implantable defibrillators, aiming to restore normal cardiac rhythm. However, given the complex spatiotemporal dynamics and non-linearity of the human heart, predicting the onset of arrhythmias and preventing the transition from steady state to unstable rhythms has been an extremely challenging task. Defibrillatory shocks still remain the primary clinical intervention for lethal ventricular arrhythmias, yet patients with implantable cardioverter defibrillators often suffer from inappropriate shocks due to false positives and reduced quality of life. Here, we aim to present a comprehensive review of the current advances in cardiac arrhythmia prediction, prevention and control strategies. We provide an overview of traditional clinical arrhythmia management methods and describe promising potential pacing techniques for predicting the onset of abnormal rhythms and effectively suppressing cardiac arrhythmias. We also offer a clinical perspective on bridging the gap between basic and clinical science that would aid in the assimilation of promising anti-arrhythmic pacing strategies.

NATURAL LANGUAGE PROCESSING BASED MACHINE LEARNING MODEL USING CARDIAC MRI REPORTS TO IDENTIFY HYPERTROPHIC CARDIOMYOPATHY PATIENTS

Hypertrophic Cardiomyopathy (HCM) is the most common genetic heart disease in the US and is known to cause sudden death (SCD) in young adults. While significant advancements have been made in HCM diagnosis and management, there is a need to identify HCM cases from electronic health record (EHR) data to develop automated tools based on natural language processing guided machine learning (ML) models for accurate HCM case identification to improve management and reduce adverse outcomes of HCM patients. Cardiac Magnetic Resonance (CMR) Imaging, plays a significant role in HCM diagnosis and risk stratification. CMR reports, generated by clinician annotation, offer rich data in the form of cardiac measurements as well as narratives describing interpretation and phenotypic description. The purpose of this study is to develop an NLP-based interpretable model utilizing impressions extracted from CMR reports to automatically identify HCM patients. CMR reports of patients with suspected HCM diagnosis between the years 1995 to 2019 were used in this study. Patients were classified into three categories of yes HCM, no HCM and, possible HCM. A random forest (RF) model was developed to predict the performance of both CMR measurements and impression features to identify HCM patients. The RF model yielded an accuracy of 86% (608 features) and 85% (30 features). These results offer promise for accurate identification of HCM patients using CMR reports from EHR for efficient clinical management transforming health care delivery for these patients.

Sustained Improvement in Diastolic Reserve Following Percutaneous Pericardiotomy in a Porcine Model of Heart Failure With Preserved Ejection Fraction

BACKGROUND

Heart failure with preserved ejection fraction is increasing in prevalence, but few effective treatments are available. Elevated left ventricular (LV) diastolic filling pressures represent a key therapeutic target. Pericardial restraint contributes to elevated LV end-diastolic pressure, and acute studies have shown that pericardiotomy attenuates the rise in LV end-diastolic pressure with volume loading. However, whether these acute effects are sustained chronically remains unknown.

METHODS

Minimally invasive pericardiotomy was performed percutaneously using a novel device in a porcine model of heart failure with preserved ejection fraction. Hemodynamics were assessed at baseline and following volume loading with pericardium intact, acutely following pericardiotomy, and then again chronically after 4 weeks. Cardiac structure was assessed by magnetic resonance imaging.

RESULTS

The increase in LV end-diastolic pressure with volume loading was mitigated by 41% (95% CI, 27%-45%, P<0.0001; ΔLV end-diastolic pressure reduced from +9±3 mm Hg to +5±3 mm Hg, P=0.0003, 95% CI, -2.2 to -5.5). The effect was sustained at 4 weeks (+5±2 mm Hg, P=0.28 versus acute). There was no statistically significant effect of pericardiotomy on ventricular remodeling compared with age-matched controls. None of the animals developed hemodynamic or pathological indicators of pericardial constriction or frank systolic dysfunction.

CONCLUSIONS

The acute hemodynamic benefits of pericardiotomy are sustained for at least 4 weeks in a swine model of heart failure with preserved ejection fraction, without excessive chamber remodeling, pericarditis, or clinically significant systolic dysfunction. These data support trials evaluating minimally invasive pericardiotomy as a novel treatment for heart failure with preserved ejection fraction in humans.

Patient-Care Team Contact Patterns Impact Treatment Length of Stay in the Emergency Department

Real-time location systems (RTLS) has found extensive application in the healthcare setting, that is shown to improve safety, save cost, and increase patient satisfaction. More specifically, some studies have shown the efficacy of RTLS leading to an improved workflow in the emergency department. However, due to substantial implementation costs of such technologies, hospital administrators show reluctance in RTLS adoption. Our previous preliminary studies with RFID data in the emergency department (ED) demonstrated for the first time the quantification of `patient alone time' and its relationship to outcomes such as 30-day hospitalization. In this study, we use ED RTLS data to analyze patient-care team contact time (PCTCT) and its relationship to the total treatment length of stay (LOS) in ED. An observational cohort study was performed in the ED using RTLS data from Jan 17 - Sep 17, 2017, which included a total of 51,697 patients. PCTCT within the first hour of a patient's placement in a treatment bed was calculated and its relationship to treatment LOS was analyzed while controlling for confounding factors affecting treatment LOS. Results show that treatment LOS is highly correlated with the ED crowding captured by the patient-perprovider ratio, negatively correlated to the physician and resident visit frequency, and positively correlated to nurse visit frequency. The results can inform designing new guidelines for ideal patient-care team interactions and be used to determine optimal ED staffing levels and care team composition for effective care delivery.

Intrinsic Mode Function Complexity Index Using Empirical Mode Decomposition discriminates Normal Sinus Rhythm and Atrial Fibrillation on a Single Lead ECG

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia affecting approximately 3 million Americans, and is a prognostic marker for stroke, heart failure and even death. Current techniques to discriminate normal sinus rhythm (NSR) and AF from single lead ECG suffer several limitations in terms of sensitivity and specificity using short time ECG data which distorts ECG and many are not suitable for real-time implementation. The purpose of this research was to test the feasibility of discriminating single lead ECG's with normal sinus rhythm (NSR) and AF using intrinsic mode function (IMF) complexity index. 15 sets of ECG's with NSR and AF were obtained from Physionet database. Custom MATLAB® software was written to compute IMF index for each of the data set and compared for statistical significance. The mean IMF index for NSR across 15 data sets was 0.37 ± 0.08, and the mean IMF index for ECG with AF was 0.21 ± 0.07 showing robust discrimination with statistical significance (p<0.01). IMF complexity robustly discriminates single lead ECG with normal sinus rhythm and AF. Further validation of this result is required on a larger dataset. The results also motivate the use of this technique for analysis of other complex cardiac arrhythmias such as ventricular tachycardia (VT) or ventricular fibrillation (VF).

Waveguide effects and implications for cardiac magnetic resonance elastography: A finite element study

Magnetic resonance elastography (MRE) is increasingly being applied to thin or small structures in which wave propagation is dominated by waveguide effects, which can substantially bias stiffness results with common processing approaches. The purpose of this work was to investigate the importance of such biases and artifacts on MRE inversion results in: (i) various idealized 2D and 3D geometries with one or more dimensions that are small relative to the shear wavelength; and (ii) a realistic cardiac geometry. Finite element models were created using simple 2D geometries as well as a simplified and a realistic 3D cardiac geometry, and simulated displacements acquired by MRE from harmonic excitations from 60 to 220 Hz across a range of frequencies. The displacement wave fields were inverted with direct inversion of the Helmholtz equation with and without the application of bandpass filtering and/or the curl operator to the displacement field. In all geometries considered, and at all frequencies considered, strong biases and artifacts were present in inversion results when the curl operator was not applied. Bandpass filtering without the curl was not sufficient to yield accurate recovery. In the 3D geometries, strong biases and artifacts were present in 2D inversions even when the curl was applied, while only 3D inversions with application of the curl yielded accurate recovery of the complex shear modulus. These results establish that taking the curl of the wave field and performing a full 3D inversion are both necessary steps for accurate estimation of the shear modulus both in simple thin-walled or small structures and in a realistic cardiac geometry when using simple inversions that neglect the hydrostatic pressure term. In practice, sufficient wave amplitude, signal-to-noise ratio, and resolution will be required to achieve accurate results.

MRI feature tracking strain is prognostic for all-cause mortality in AL amyloidosis

OBJECTIVES

Cardiac involvement is a major determinate of mortality in light chain (AL) amyloidosis. Cardiac magnetic resonance imaging (MRI) feature tracking (FT) strain is a new method for measuring myocardial strain. This study retrospectively evaluated the association of MRI FT strain with all-cause mortality in AL amyloidosis.

MATERIALS AND METHODS

Seventy-six patients with newly diagnosed AL amyloidosis underwent cardiac MRI. 75 had images suitable for MRI FT strain analysis. MRI delayed enhancement, morphologic and functional evaluation, cardiac biomarker staging and transthoracic echocardiography were also performed. Subjects' charts were reviewed for all-cause mortality. Cox proportional hazards analysis was used to evaluate survival in univariate and multivariate analysis.

RESULTS

There were 52 deaths. Median follow-up of surviving patients was 1.7 years. In univariate analysis, global radial (Hazard Ratio (HR) = 0.95, p <.01), circumferential (HR = 1.09, p < .01) and longitudinal (HR = 1.08, p < .01) strain were associated with all-cause mortality. In separate multivariate models, radial (HR = 0.96, p = .02), circumferential (HR = 1.09, p = .03) and longitudinal strain (HR = 1.07, p = .04) remained prognostic when combined with presence of biomarker stage 3.

CONCLUSIONS

MRI FT strain is associated with all-cause mortality in patients with AL amyloidosis.

Feasibility of visualizing higher regions of Shannon entropy in atrial fibrillation patients

Catheter ablation is associated with limited success rates in patients with persistent atrial fibrillation (AF), which is mostly maintained by rotors that are located outside of pulmonary veins (PV) region. None of the currently available commercial mapping systems can accurately predict the rotor location outside of PV in patients with persistent AF. Experimental verification of the feasibility of using Shannon entropy (SE) based mapping technique based on symbolic dynamics approach to identify pivotal points of rotors in isolated rabbit hearts was performed. This mapping approach was applied to clinical intracardiac electrograms from a patient with persistent AF to construct a 3-dimensional (3D) SE map. Our results demonstrate that SE can correctly predict the pivot point of the rotor in animal model. We also demonstrated the feasibility of generating 3D SE maps using current catheter mapping methods to identify active rotor sites that may maintain AF. Higher SE areas are noted at the base of right atrial appendage. Further clinical studies are needed to validate ablation of areas with high SE with outcomes.

Cardiac MR elastography using reduced-FOV, single-shot, spin-echo EPI

Purpose

To implement a reduced field of view (rFOV) technique for cardiac MR elastography (MRE) and to demonstrate the improvement in image quality of both magnitude images and post‐processed MRE stiffness maps compared to the conventional full field of view (full‐FOV) acquisition.

Methods

With Institutional Review Board approval, 17 healthy volunteers underwent both full‐FOV and rFOV cardiac MRE scans using 140‐Hz vibrations. Two cardiac radiologists blindly compared the magnitude images and stiffness maps and graded the images based on several image quality attributes using a 5‐point ordinal scale. Fisher's combined probability test was performed to assess the overall evaluation. The octahedral shear strain‐based signal‐to‐noise ratio (OSS‐SNR) and median stiffness over the left ventricular myocardium were also compared.

Results

One volunteer was excluded because of an inconsistent imaging resolution during the exam. In the remaining 16 volunteers (9 males, 7 females), the rFOV scans outperformed the full‐FOV scans in terms of subjective image quality and ghosting artifacts in the magnitude images and stiffness maps, as well as the overall preference. The quantitative measurements showed that rFOV had significantly higher OSS‐SNR (median: 1.4 [95% confidence interval (CI): 1.2–1.5] vs. 2.1 [95% CI: 1.8–2.4]), P < 0.05) compared to full‐FOV. Although no significant change was found in the median myocardial stiffness between the 2 scans, we observed a decrease in the stiffness variation within the myocardium from 2.1 kPa (95% CI: [1.9, 2.3]) to 1.9 kPa (95% CI: [1.7, 2.0]) for full‐FOV and rFOV, respectively (P < 0.05) in a subgroup of 7 subjects with ghosting present in the myocardium.

Conclusion

This pilot volunteer study demonstrated that rFOV cardiac MRE has the capability to reduce ghosting and to improve image quality in both MRE magnitude images and stiffness maps. Magn Reson Med 80:231–238, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Kurtosis as a statistical approach to identify the pivot point of the rotor

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia that causes stroke affecting more than 2.3 million people in the US. Catheter ablation to terminate AF is successful for paroxysmal AF but suffers limitations with persistent AF patients as current mapping methods cannot identify AF active substrates outside of pulmonary vein region. In this work, we developed a novel Kurtosis based mapping technique that can accurately identify pivot points of the rotors that were induced in ex-vivo isolated rabbit heart. The results indicate that the chaotic nature of rotor pivot point results in higher Kurtosis compared to the periphery thereby enabling its accurate identification. Our results suggest that Kurtosis technique can be further applied to intra-atrial electrograms from AF patients with rotors to accurately identify the rotor pivot point by generating 3-dimensional (3D) patient-specific Kurtosis maps. Validation of this new Kurtosis based mapping technology is required through clinical studies with both paroxysmal and persistent AF patient data.

Air embolism: diagnosis and management

Air embolism is an uncommon, but potentially life-threatening event for which prompt diagnosis and management can result in significantly improved patient outcomes. Most air emboli are iatrogenic. Arterial air emboli may occur as a complication from lung biopsy, arterial catheterization or cardiopulmonary bypass. Immediate management includes placing the patient on high-flow oxygen and in the right lateral decubitus position. Venous air emboli may occur during pressurized venous infusions, or catheter manipulation. Immediate management includes placement of the patient on high-flow oxygen and in the left lateral decubitus and/or Trendelenburg position. Hyperbaric oxygen therapy is the definitive treatment which may decrease the size of air emboli by facilitating gas reabsorption, while also improving tissue oxygenation and reducing ischemic reperfusion injury.

Novel approaches for quantitative electrogram analysis for intraprocedural guidance for catheter ablation: A case of a patient with persistent atrial fibrillation

PURPOSE

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia that causes stroke affecting more than 2.3 million people in the US and is increasing in prevalence due to ageing population causing a new global epidemic. Catheter ablation with pulmonary vein isolation (PVI) to terminate AF is successful for paroxysmal AF but suffers limitations with persistent AF patients as current mapping methods cannot identify AF active substrates outside of PVI region. Recent evidences in the mechanistic understating of AF pathophysiology suggest that ectopic activity, localized re-entrant circuit with fibrillatory propagation and multiple circuit re-entries may all be involved in human AF. The authors developed novel electrogram analysis methods and validated using optical mapping data from isolated rabbit hearts to accurately identify rotor pivot points. The purpose of this study was to assess the feasibility of generating patient-specific 3D maps for intraprocedural guidance for catheter ablation using intracardiac electrograms from a persistent AF patient using novel electrogram analysis methods.

METHODS

A persistent AF patient with clinical appointment for AF ablation was recruited for this study with IRB approval. 1055 electrograms throughout the left and right atrium were obtained for offline analysis with the novel approaches such as multiscale entropy, multiscale frequency, recurrence period density entropy, kurtosis and empirical mode decomposition to generate patient specific 3D maps. 3D Shannon Entropy, Renyi Entropy and Dominant frequency maps were also generated for comparison purposes along with local activation time and complex fractionated electrogram analysis maps.

RESULTS

Patient specific 3D maps were obtained for each of the different approach. The 3D maps indicate potential active sites outside the PVI region. However, presence of rotors cannot be confirmed and validation of these approaches is required on a larger dataset.

CONCLUSIONS

Conventional catheter mapping system can be used for generating patient specific 3D maps with short time series analysis using the novel approaches.

Regional assessment of in vivo myocardial stiffness using 3D magnetic resonance elastography in a porcine model of myocardial infarction

PURPOSE

The stiffness of a myocardial infarct affects the left ventricular pump function and remodeling. Magnetic resonance elastography (MRE) is a noninvasive imaging technique for measuring soft-tissue stiffness in vivo. The purpose of this study was to investigate the feasibility of assessing in vivo regional myocardial stiffness with high-frequency 3D cardiac MRE in a porcine model of myocardial infarction, and compare the results with ex vivo uniaxial tensile testing.

METHODS

Myocardial infarct was induced in a porcine model by embolizing the left circumflex artery. Fourteen days postinfarction, MRE imaging was performed in diastole using an echocardiogram-gated spin-echo echo-planar-imaging sequence with 140-Hz vibrations and 3D MRE processing. The MRE stiffness and tensile modulus from uniaxial testing were compared between the remote and infarcted myocardium.

RESULTS

Myocardial infarcts showed increased in vivo MRE stiffness compared with remote myocardium (4.6 ± 0.7 kPa versus 3.0 ± 0.6 kPa, P = 0.02) within the same pig. Ex vivo uniaxial mechanical testing confirmed the in vivo MRE results, showing that myocardial infarcts were stiffer than remote myocardium (650 ± 80 kPa versus 110 ± 20 kPa, P = 0.01).

CONCLUSIONS

These results demonstrate the feasibility of assessing in vivo regional myocardial stiffness with high-frequency 3D cardiac MRE. Magn Reson Med 79:361-369, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Cardiac MR elastography for quantitative assessment of elevated myocardial stiffness in cardiac amyloidosis

Purpose

To evaluate if cardiac magnetic resonance elastography (MRE) can measure increased stiffness in patients with cardiac amyloidosis. Myocardial tissue stiffness plays an important role in cardiac function. A noninvasive quantitative imaging technique capable of measuring myocardial stiffness could aid in disease diagnosis, therapy monitoring, and disease prognostic strategies. We recently developed a high‐frequency cardiac MRE technique capable of making noninvasive stiffness measurements.

Materials and Methods

In all, 16 volunteers and 22 patients with cardiac amyloidosis were enrolled in this study after Institutional Review Board approval and obtaining formal written consent. All subjects were imaged head‐first in the supine position in a 1.5T closed‐bore MR imager. 3D MRE was performed using 5 mm isotropic resolution oblique short‐axis slices and a vibration frequency of 140 Hz to obtain global quantitative in vivo left ventricular stiffness measurements. The median stiffness was compared between the two cohorts. An octahedral shear strain signal‐to‐noise ratio (OSS‐SNR) threshold of 1.17 was used to exclude exams with insufficient motion amplitude.

Results

Five volunteers and six patients had to be excluded from the study because they fell below the 1.17 OSS‐SNR threshold. The myocardial stiffness of cardiac amyloid patients (median: 11.4 kPa, min: 9.2, max: 15.7) was significantly higher (P = 0.0008) than normal controls (median: 8.2 kPa, min: 7.2, max: 11.8).

Conclusion

This study demonstrates the feasibility of 3D high‐frequency cardiac MRE as a contrast‐agent‐free diagnostic imaging technique for cardiac amyloidosis.

Level of Evidence: 2

Technical Efficacy: Stage 2

J. Magn. Reson. Imaging 2017;46:1361–1367.

Quantitative 3D magnetic resonance elastography: Comparison with dynamic mechanical analysis

Purpose

Magnetic resonance elastography (MRE) is a rapidly growing noninvasive imaging technique for measuring tissue mechanical properties in vivo. Previous studies have compared two‐dimensional MRE measurements with material properties from dynamic mechanical analysis (DMA) devices that were limited in frequency range. Advanced DMA technology now allows broad frequency range testing, and three‐dimensional (3D) MRE is increasingly common. The purpose of this study was to compare 3D MRE stiffness measurements with those of DMA over a wide range of frequencies and shear stiffnesses.

Methods

3D MRE and DMA were performed on eight different polyvinyl chloride samples over 20–205 Hz with stiffness between 3 and 23 kPa. Driving frequencies were chosen to create 1.1, 2.2, 3.3, 4.4, 5.5, and 6.6 effective wavelengths across the diameter of the cylindrical phantoms. Wave images were analyzed using direct inversion and local frequency estimation algorithm with the curl operator and compared with DMA measurements at each corresponding frequency. Samples with sufficient spatial resolution and with an octahedral shear strain signal‐to‐noise ratio > 3 were compared.

Results

Consistency between the two techniques was measured with the intraclass correlation coefficient (ICC) and was excellent with an overall ICC of 0.99.

Conclusions

3D MRE and DMA showed excellent consistency over a wide range of frequencies and stiffnesses. Magn Reson Med 77:1184–1192, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

In vivo, high-frequency three-dimensional cardiac MR elastography: Feasibility in normal volunteers

PURPOSE

Noninvasive stiffness imaging techniques (elastography) can image myocardial tissue biomechanics in vivo. For cardiac MR elastography (MRE) techniques, the optimal vibration frequency for in vivo experiments is unknown. Furthermore, the accuracy of cardiac MRE has never been evaluated in a geometrically accurate phantom. Therefore, the purpose of this study was to determine the necessary driving frequency to obtain accurate three-dimensional (3D) cardiac MRE stiffness estimates in a geometrically accurate diastolic cardiac phantom and to determine the optimal vibration frequency that can be introduced in healthy volunteers.

METHODS

The 3D cardiac MRE was performed on eight healthy volunteers using 80 Hz, 100 Hz, 140 Hz, 180 Hz, and 220 Hz vibration frequencies. These frequencies were tested in a geometrically accurate diastolic heart phantom and compared with dynamic mechanical analysis (DMA).

RESULTS

The 3D Cardiac MRE was shown to be feasible in volunteers at frequencies as high as 180 Hz. MRE and DMA agreed within 5% at frequencies greater than 180 Hz in the cardiac phantom. However, octahedral shear strain signal to noise ratios and myocardial coverage was shown to be highest at a frequency of 140 Hz across all subjects.

CONCLUSION

This study motivates future evaluation of high-frequency 3D MRE in patient populations. Magn Reson Med 77:351-360, 2017. © 2016 Wiley Periodicals, Inc.

Real-time T-p knot algorithm for baseline wander noise removal from the electrocardiogram - biomed 2009

The electrocardiogram (ECG) is often contaminated with various noises including electromyographic, 60 Hz, respiratory and baseline wander (BW). The BW noise presents challenges in removal from the ECG by conventional filtering approaches because its frequency content overlaps with that of ECG signals. Removal of the BW noise is often preferred as a step before ECG signal processing. In this paper we present an algorithm for estimating and removing BW noise from a single-channel ECG signal, which can be implemented in real-time digital signal processing hardware and software. The algorithm uses the Pan & Tompkins R-wave detection method and places an interpolation point (i.e., a "T-P knot") at each R-R midpoint. It performs a cubic spline interpolation of the four most recently detected T-P knots to estimate the most recent segment of the BW noise. This most recent segment is then subtracted from the ECG signal to produce a "flattened" signal. The algorithm was implemented and tested in a pseudo real-time environment using MATLABTM, and test results are presented for simulated ECG and BW data as well as for actual ECG recordings from the PhysioNet/PhysioBank Fantasia database containing very large BW signal components. Correlations of 0.9959-0.9978 are shown for the estimated versus actual BW signals confirming the accuracy of the T-P knot algorithm.

Real-time estimation of the ECG-derived respiration (EDR) signal using a new algorithm for baseline wander noise removal

Numerous methods have been reported for deriving respiratory information such as respiratory rate from the electrocardiogram (ECG). In this paper the authors present a real-time algorithm for estimation and removal of baseline wander (BW) noise and obtaining the ECG-derived respiration (EDR) signal for estimation of a patient's respiratory rate. This algorithm utilizes a real-time "T-P knot" baseline wander removal technique which is based on the repetitive backward subtraction of the estimated baseline from the ECG signal. The estimated baseline is interpolated from the ECG signal at midpoints between each detected R-wave. As each segment of the estimated baseline signal is subtracted from the ECG, a "flattened" ECG signal is produced for which the amplitude of each R-wave is analyzed. The respiration signal is estimated from the amplitude modulation of R-waves caused by breathing. Testing of the algorithm was conducted in a pseudo real-time environment using MATLAB(TM), and test results are presented for simultaneously recorded ECG and respiration recordings from the PhysioNet/PhysioBank Fantasia database. Test data from patients were chosen with particularly large baseline wander components to ensure the reliability of the algorithm under adverse ECG recording conditions. The algorithm yielded EDR signals with a respiration rate of 4.4 breaths/min. for Fantasia patient record f2y10 and 10.1 breaths/min. for Fantasia patient record f2y06. These were in good agreement with the simultaneously recorded respiration data provided in the Fantasia database thus confirming the efficacy of the algorithm.

Real-time estimation of the ecg-derived respiration (edr) signal - biomed 2009

Respiration rate is a commonly measured physiological parameter for which several methods have been proposed for obtaining from the electrocardiogram (ECG). In this paper the authors present a new real-time algorithm for obtaining the ECG-derived respiration (EDR) signal. This algorithm utilizes a real-time baseline wander removal technique which is based on the repetitive backward subtraction of the estimated baseline from the ECG signal. The estimated baseline is interpolated from the ECG signal at midpoints between each detected R-wave. As each segment of the estimated baseline signal is subtracted from the ECG, a "flattened" ECG signal is produced for which the amplitude of each R-wave is analyzed. The respiration signal is estimated from the amplitude modulation of R-waves caused by breathing. The algorithm depends only on the ECG morphology, interpolation and subtraction and is functional in real-time. Testing of the algorithm was conducted in a pseudo real-time environment using MATLABTM, and test results are presented for simultaneously recorded ECG and respiration recordings from the PhysioNet/PhysioBank Fantasia database. Test data from a patient was chosen with particularly large baseline wander components to ensure the reliability of the algorithm under adverse ECG recording conditions. The algorithm yielded respiration rates of 4.4 breaths/min. for Fantasia patient record f2y10 and 13.3 breaths/min. for Fantasia patient record f2y06. These were in good agreement with the respiration rates of the simultaneously recorded respiration data provided in the Fantasia database thus confirming the efficacy of the algorithm.