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Objective: This paper presents preliminary methods of incorporating the pathological conditions of cardiac arrhythmias and valvular stenosis in hybrid mock circulation loop (hMCL) operation for the enhanced verification and validation of mechanical circulatory support devices such as VADs. Methods: The MGH/MF Waveform datasets from PhysioNet database (including both nominal and clinically diagnosed arrhythmic ECG measurements) as well as cardiovascular system model updates are used to recreate arrhythmic events and valvular stenosis in vitro. Results: Preliminary results show the hMCL can recreate each tested cardiac event within 2% and 4% mean error for reference pressure tracking in the aortic and left ventricular pressure chambers, respectively. Further, frequency spectrum analysis comparisons using the magnitude-squared coherence analysis shows close alignment between measured arrhythmic and hMCL realized pressure frequency content. Conclusion: The generation of cardiac arrhythmias and valvular stenosis around a VAD via both model and acute measurement based methods was achieved. Significance: Pathological conditions such as cardiac arrhythmias and valvular stenosis are limited in documentation despite the large percentage of patients who experience these events. This paper provides a means to begin incorporating these events into hardware-in-the-loop mock circulatory systems for next generation VAD validation and verification. [ABSTRACT FROM AUTHOR]

Tensor fields are useful for modeling the structure of biological tissues. The challenge to measure tensor fields involves acquiring sufficient data of scalar measurements that are physically achievable and reconstructing tensors from as few projections as possible for efficient applications in medical imaging. In this paper, we present a filtered back-projection algorithm for the reconstruction of a symmetric second-rank tensor field from directional X-ray projections about three axes. The tensor field is decomposed into a solenoidal and irrotational component, each of three unknowns. Using the Fourier projection theorem, a filtered back-projection algorithm is derived to reconstruct the solenoidal and irrotational components from projections acquired around three axes. A simple illustrative phantom consisting of two spherical shells and a 3D digital cardiac diffusion image obtained from diffusion tensor MRI of an excised human heart are used to simulate directional X-ray projections. The simulations validate the mathematical derivations and demonstrate reasonable noise properties of the algorithm. The decomposition of the tensor field into solenoidal and irrotational components provides insight into the development of algorithms for reconstructing tensor fields with sufficient samples in terms of the type of directional projections and the necessary orbits for the acquisition of the projections of the tensor field. [ABSTRACT FROM AUTHOR]

Objective: Traditional lead-based cardiac pacemakers suffer from lead-related complications including lead fracture, lead dislodgement, and venous obstruction. Modern leadless pacemakers mitigate the complications, but since they are implanted inside the heart with a small battery, their limited battery lifetime necessities device replacement within the patient's lifetime. This paper presents a leadless and batteryless, wirelessly powered intravenous cardiac pacemaker that can potentially mitigate both problems. Methods: Wireless power is transferred at 13.56 MHz in bursts between the pacemaker modules to achieve sufficient power over the required distance for wireless pacing. The pacemaker stimulation module is designed to fit within the anatomical constraints of a cardiac vein, consume low power, apply greater than 5 V stimulation and comply with FCC SAR regulations. The module is primarily implemented in CMOS technology to achieve extreme system miniaturization. Results: Ex-vivo pacing capability was demonstrated with a system that can apply 5 V stimulation, consume 1 mW power, and operate up to 2.5 cm TX and RX separation. An in-vivo experiment verified the pacemaker functionality by increasing the heartbeat of Yorkshire pig from 64 bpm to 100 bpm. Conclusion: This work establishes that intravascular cardiac pacing can be achieved that can mitigate lead and battery-related complications. Significance: This study has a potential to advance leadless and wirelessly powered pacemaker technology. [ABSTRACT FROM AUTHOR]

Objective: Radar technology promises to be a touchless and thereby burden-free method for continuous heart sound monitoring, which can be used to detect cardiovascular diseases. However, the first and most crucial step is to differentiate between high- and low-quality segments in a recording to assess their suitability for a subsequent automated analysis. This paper gives a comprehensive study on this task and first addresses the specific characteristics of radar-recorded heart sound signals. Methods: To gather heart sound signals recorded from radar, a bistatic radar system was built and installed at the university hospital. Under medical supervision, heart sound data were recorded from 30 healthy test subjects. The signals were segmented and labeled as high- or low-quality by a medical expert. Different state-of-the-art pattern classification algorithms were evaluated for the task of automated signal quality determination and the most promising one was optimized and evaluated using leave-one-subject-out cross validation. Results: The proposed classifier is able to achieve an accuracy of up to 96.36% and demonstrates a superior classification performance compared with the state-of-the-art classifier with a maximum accuracy of 76.00%. Conclusion: This paper introduces an ensemble classifier that is able to perform automated signal quality determination of radar-recorded heart sound signals with a high accuracy. Significance: Besides achieving a higher performance compared with state-of-the-art classifiers, this study is the first one to deal with the quality determination of heart sounds that are recorded by radar systems. The proposed method enables contactless and continuous heart sound monitoring for the detection of cardiovascular diseases. [ABSTRACT FROM AUTHOR]

Encoding data visually is at the heart of visualization. We usually assume that encodings are read as specified (i.e., if a bar chart is drawn by the length of the bars based on the data, that is also how we read them). In this paper, we question this assumption and demonstrate that observed encodings often differ from the ones used to specify the visualization. The value of a chart also often comes from higher level derived encodings, and which encodings end up getting used also depends on the user's task. [ABSTRACT FROM AUTHOR]

Objective: Catheters and wires are used extensively in cardiac catheterization procedures. Detecting their positions in fluoroscopic X-ray images is important for several clinical applications such as motion compensation and co-registration between 2D and 3D imaging modalities. Detecting the complete length of a catheter or wire object as well as electrode positions on the catheter or wire is a challenging task. Method: In this paper, an automatic detection framework for catheters and wires is developed. It is based on path reconstruction from image tensors, which are eigen direction vectors generated from a multiscale vessel enhancement filter. A catheter or a wire object is detected as the smooth path along those eigen direction vectors. Furthermore, a real-time tracking method based on a template generated from the detection method was developed. Results: The proposed framework was tested on a total of 7,754 X-ray images. Detection errors for catheters and guidewires are 0.56 ± 0.28 mm and 0.68 ± 0.33 mm, respectively. The proposed framework was also tested and validated in two clinical applications. For motion compensation using catheter tracking, the 2D target registration errors (TRE) of 1.8 mm ± 0.9 mm was achieved. For co-registration between 2D X-ray images and 3D models from MRI images, a TRE of 2.3 ± 0.9 mm was achieved. Conclusion: A novel and fully automatic detection framework and its clinical applications are developed. Significance: The proposed framework can be applied to improve the accuracy of image-guidance systems for cardiac catheterization procedures. [ABSTRACT FROM AUTHOR]

This paper proposes a semi-automatic Fontan surgery planning method for designing and manufacturing hemodynamically optimized patient-specific grafts. Fontan surgery is a palliative procedure for patients with a single ventricle heart defect by creating a new path using a vascular graft for the deoxygenated blood to be directed to the lungs, bypassing the heart. However, designing patient-specific grafts with optimized hemodynamic performance is a complex task due to the variety of patient-specific anatomies, confined surgical planning space, and the requirement of simultaneously considering multiple design criteria for vascular graft optimization. To address these challenges, we used parameterized Fontan pathways to explore patient-specific vascular graft design spaces and search for optimal solutions by formulating a nonlinear constrained optimization problem, which minimizes indexed power loss (iPL) of the Fontan model by constraining hepatic flow distribution (HFD), percentage of abnormal wall shear stress (%WSS) and geometric interference between Fontan pathways and the heart models (InDep) within clinically acceptable thresholds. Gaussian process regression was employed to build surrogate models of the hemodynamic parameters as well as InDep and $\text{N}_\text{v}$ (conduit model smoothness indicator) for optimization by pattern search. We tested the proposed method on two patient-specific models (n=2). The results showed the automatically optimized (AutoOpt) Fontan models hemodynamically outperformed or at least are comparable to manually optimized Fontan models with significantly reduced surgical planning time (15 hours versus over 2 weeks). We also demonstrated feasibility of manufacturing the AutoOpt Fontan conduits by using electrospun nanofibers. [ABSTRACT FROM AUTHOR]

Quantitative assessment of myocardial stiffness is crucial to understand and evaluate cardiac biomechanics and function. Despite the recent progresses of ultrasonic shear wave elastography, quantitative evaluation of myocardial stiffness still remains a challenge because of strong elastic anisotropy. In this paper we introduce a smart ultrasound approach for non-invasive real-time quantification of shear wave velocity (SWV) and elastic fractional anisotropy (FA) in locally transverse isotropic elastic medium such as the myocardium. The approach relies on a simultaneous multidirectional evaluation of the SWV without a prior knowledge of the fiber orientation. We demonstrated that it can quantify accurately SWV in the range of 1.5 to 6 m/s in transverse isotropic medium (FA < 0.7) using numerical simulations. Experimental validation was performed on calibrated phantoms and anisotropic ex vivo tissues. A mean absolute error of 0.22 m/s was found when compared to gold standard measurements. Finally, in vivo feasibility of myocardial anisotropic stiffness assessment was evaluated in four healthy volunteers on the antero-septo basal segment and on anterior free wall of the right ventricle (RV) in end-diastole. A mean longitudinal SWV of 1.08 ± 0.20 m/s was measured on the RV wall and 1.74 ± 0.51 m/s on the septal wall with a good intra-volunteer reproducibility (±0.18 m/s). This approach has the potential to become a clinical tool for the quantitative evaluation of myocardial stiffness and diastolic function. [ABSTRACT FROM AUTHOR]

The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field. [ABSTRACT FROM AUTHOR]

Automatic and accurate 3D cardiac image segmentation plays a crucial role in cardiac disease diagnosis and treatment. Even though CNN based techniques have achieved great success in medical image segmentation, the expensive annotation, large memory consumption, and insufficient generalization ability still pose challenges to their application in clinical practice, especially in the case of 3D segmentation from high-resolution and large-dimension volumetric imaging. In this paper, we propose a few-shot learning framework by combining ideas of semi-supervised learning and self-training for whole heart segmentation and achieve promising accuracy with a Dice score of 0.890 and a Hausdorff distance of 18.539 mm with only four labeled data for training. When more labeled data provided, the model can generalize better across institutions. The key to success lies in the selection and evolution of high-quality pseudo labels in cascaded learning. A shape-constrained network is built to assess the quality of pseudo labels, and the self-training stages with alternative global-local perspectives are employed to improve the pseudo labels. We evaluate our method on the CTA dataset of the MM-WHS 2017 Challenge and a larger multi-center dataset. In the experiments, our method outperforms the state-of-the-art methods significantly and has great generalization ability on the unseen data. We also demonstrate, by a study of two 4D (3D+T) CTA data, the potential of our method to be applied in clinical practice. [ABSTRACT FROM AUTHOR]

Objective: To develop quantitative methods for the clinical interpretation of the ballistocardiogram (BCG). Methods: A closed-loop mathematical model of the cardiovascular system is proposed to theoretically simulate the mechanisms generating the BCG signal, which is then compared with the signal acquired via accelerometry on a suspended bed. Results: Simulated arterial pressure waveforms and ventricular functions are in good qualitative and quantitative agreement with those reported in the clinical literature. Simulated BCG signals exhibit the typical I, J, K, L, M, and N peaks and show good qualitative and quantitative agreement with experimental measurements. Simulated BCG signals associated with reduced contractility and increased stiffness of the left ventricle exhibit different changes that are characteristic of the specific pathological condition. Conclusion: The proposed closed-loop model captures the predominant features of BCG signals and can predict pathological changes on the basis of fundamental mechanisms in cardiovascular physiology. Significance: This paper provides a quantitative framework for the clinical interpretation of BCG signals and the optimization of BCG sensing devices. The present paper considers an average human body and can potentially be extended to include variability among individuals. [ABSTRACT FROM AUTHOR]

This paper proposes a new technique for the identification of fundamental heart sounds (HSs), namely, $S_{1}$ and $S_{2}$ from the cardiac cycle, the first and foremost step in the automated HSs analysis for the detection of pathological events, without incorporating time-interval informations between $S_{1}$ and $S_{2}$ or electrocardiogram signal as reference. The motive of this paper is to demonstrate that the reliable $S_{1}$ and $S_{2}$ classification performances based on the combinatory feature (CF) derived from higher order moments and cepstral-based domain can still be achieved, under the circumstances where the timing interval information might not be easily understood due to cardiac abnormalities. Using deep neural networks approach, a stacked autoencoder (SAE) based on the CF is proposed for the classification of fundamental HSs. Experiments are conducted on both publicly available and recorded HSs signals for the validation of the proposed method. The SAE using the proposed CF achieves better classification results in recognizing $S_{1}$ and $S_{2}$ in comparison to well-known classifiers such as deep belief neural network, support vector machine, Naive Bayes, linear discriminant analysis, and boosting ensemble. The proposed method shows higher classification rate in terms of accuracy, sensitivity, and specificity by considering CF, which uses Mel-frequency cepstral coefficients and its derivative features. A second approach for addressing the problem of $S_{1}$ and $S_{2}$ identifications is carried out by employing 1-D convolutional neural network that uses the signals directly to learn the relevant features by its own for the recognition. [ABSTRACT FROM AUTHOR]

Ultrafast acoustoelectric imaging (UAI) is a novel method for the mapping of biological current densities, which may improve the diagnosis and monitoring of cardiac activation diseases such as arrhythmias. This paper evaluates the feasibility of performing UAI in beating rat hearts. A previously described system based on a 256-channel ultrasound research platform fitted with a 5-MHz linear array was used for simultaneous UAI, ultrafast B-mode, and electrocardiogram (ECG) recordings. In this paper, rat hearts (n = 4) were retroperfused within a Langendorff isolated heart system. A pair of Ag/Cl electrodes were positioned on the epicardium to simultaneously record ECG and UAI signals for imaging frame rates of up to 1000 Hz and a mechanical index of 1.3. To account for the potential effect of motion on the UAI maps, acquisitions for n = 3 hearts were performed with and without suppression of the mechanical contraction using 2,3-butanedione monoxime. Current densities were detected for all four rats in the region of the atrio-ventricular node, with an average contrast-to-noise ratios of 12. The UAI signals’ frequency matched the sinus rhythm, even without mechanical contraction, suggesting that the signals measured correspond to physiological electrical activation. UAI signals appeared at the apex and within the ventricular walls with a delay estimated at 29 ms. Finally, the signals from different electrode positions along the myocardium wall showed the possibility of mapping the electrical activation throughout the heart. These results show the potential of UAI for cardiac activation mapping in vivo and in real time. [ABSTRACT FROM AUTHOR]

Today's implantable mechanical circulatory support devices, such as left ventricular assist devices, still rely on a percutaneous driveline, which is a frequent cause of severe infections and which reduces the quality of life for the patients. Inductive power transfer (IPT) is therefore a promising technology to replace the driveline and, hence, reducing the likelihood of an infection. This paper focuses on the series–series compensated IPT system and provides an in-depth comparison of two operating modes, i.e., the operation at resonance and the operation above resonance, and highlights the advantages and disadvantages with respect to the requirements set by the application at hand. In addition, the paper presents the design and the realization of a fully functional transcutaneous energy transfer (TET) implant hardware prototype, which includes the IPT front-end, the control circuit, the backup battery and its charging converter, as well as the communication electronics in a boxed volume of only 10.3 cl. The experimental verification shows that overall dc–dc efficiencies of up to 90% can be achieved for both operating modes when transmitting 25–30 W from the external battery to the implant backup battery, each having a nominal voltage of 14.8 V, using TET coils with 70 mm diameter and 10 mm coil separation distance. [ABSTRACT FROM AUTHOR]

Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the “Automatic Cardiac Diagnosis Challenge” dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions. [ABSTRACT FROM AUTHOR]

We present a wafer-scale fabricated, PDMS-based platform for culturing miniaturized engineered heart tissues (EHTs) which allows highly accurate measurements of the contractile properties of these tissues. The design of the platform is an anisometrically downscaled version of the Heart-Dyno system, consisting of two elastic micropillars inside an elliptic microwell with volume ranging from 3 down to $1\mu \text{L}$ which supports EHT formation. Size downscaling facilitates fabrication of the platform and makes it compatible with accurate and highly reproducible batch wafer-scale processing; furthermore, downscaling reduces the cost of cell cultures and increases assay throughput. After fabrication, the devices were characterized by nanoindentation to assess the mechanical properties of the pillars and transferred to 96-well plates for cell seeding. Regardless the size of the platform, cell seeding resulted in successful formation of EHTs and all tissues were functionally active (i.e. showed cyclic contractions). The precise characterization of the stiffness of the micropillars enabled accurate measurements of the contractile forces exerted by the cardiac tissues through optical tracking of micropillar displacement. The miniature EHT platforms described in this paper represent a proper microenvironment for culturing and studying EHTs. [2020-0130] [ABSTRACT FROM AUTHOR]

Objectives: This paper introduces a novel method for the detection and classification of aortic stenosis (AS) using the time-frequency features of chest cardio-mechanical signals collected from wearable sensors, namely seismo-cardiogram (SCG) and gyro-cardiogram (GCG) signals. Such a method could potentially monitor high-risk patients out of the clinic. Methods: Experimental measurements were collected from twenty patients with AS and twenty healthy subjects. Firstly, a digital signal processing framework is proposed to extract time-frequency features. The features are then selected via the analysis of variance test. Different combinations of features are evaluated using the decision tree, random forest, and artificial neural network methods. Two classification tasks are conducted. The first task is a binary classification between normal subjects and AS patients. The second task is a multi-class classification of AS patients with co-existing valvular heart diseases. Results: In the binary classification task, the average accuracies achieved are 96.25% from decision tree, 97.43% from random forest, and 95.56% from neural network. The best performance is from combined SCG and GCG features with random forest classifier. In the multi-class classification, the best performance is 92.99% using the random forest classifier and SCG features. Conclusion: The results suggest that the solution could be a feasible method for classifying aortic stenosis, both in the binary and multi-class tasks. It also indicates that most of the important time-frequency features are below 11 Hz.Significance: The proposed method shows great potential to provide continuous monitoring of valvular heart diseases to prevent patients from sudden critical cardiac situations. [ABSTRACT FROM AUTHOR]

Combining Phonocardiography (PCG) and Electrocardiography (ECG) data has been recognized within the state-of-the-art as of added value for enhanced cardiovascular assessment. However, multiple aspects of ECG data acquisition in a stethoscope form factor remain unstudied, and existing devices typically enforce a substantial change into routine clinical auscultation procedures, with predictably low technology acceptance. As such, in this paper, we present a novel approach to ECG data acquisition throughout the five main cardiac auscultation points, and that intends to be incorporated in a commonly used electronic stethoscope. Therefore, it enables analysis and acquisition of both PCG and ECG signals in a single pass. We describe the development, experimental evaluation, and comparison of the ECG signals obtained using our proposed approach and a gold standard medical device, through metrics that allow the evaluation of morphological similarities. Results point to a high correlation between the two evaluated setups, thus supporting the idea of meaningfully collecting ECG data along medical auscultation points with the proposed form factor. Moreover, this work has led us to conclude that for the studied population, signals acquired on focuses F1, F2, and F3 are usually highly correlated with leads V1 and V2 of the standard ECG medical recording procedure. [ABSTRACT FROM AUTHOR]

Biomedical wearable sensors enable long-term monitoring applications and provide instantaneous diagnostic capabilities. Physiological monitoring can help in both the diagnosis and the ongoing treatment of a vast number of cardiovascular and pulmonary diseases such as hypertension, dysrhythmia, and asthma. In this paper, we present a system capable of monitoring several vital signals and physiological variables that determine the cardiopulmonary activity status. We explore direct measurements of multiple vital parameters with only one sensor and without special constraints. The system employs a PZT-4 piezo transducer stimulated by a suitable analog front end. The system both generates pulsed ultrasound waves at 1 MHz and amplifies reflected echoes to track internal organ motions, mainly that of the heart apex. According to the respiratory motion of the heart, the proposed system provides respiratory and heart cycles information. Promising results were obtained from six subjects with an average accuracy of 96.7% in heartbeats per minute measurement, referenced to a commercial photoplethysmography sensor. It also exhibits 94.5% sensitivity and 94.0% specificity in respiration detection compared to a spirometer signal as a reference. [ABSTRACT FROM AUTHOR]

Objectives: The use of remote sensing technologies such as radar is gaining popularity as a technique for contactless detection of physiological signals and analysis of human motion. This paper presents a methodology for classifying different events in a collection of phase modulated continuous wave radar returns. The primary application of interest is to monitor inmates where the presence of human vital signs amidst different, interferences needs to be identified. Methods: A comprehensive set of features is derived through time and frequency domain analyses of the radar returns. The Bhattacharyya distance is used to preselect the features with highest class separability as the possible candidate features for use in the classification process. The uncorrelated linear discriminant analysis is performed to decorrelate, denoise, and reduce the dimension of the candidate feature set. Linear and quadratic Bayesian classifiers are designed to distinguish breathing, different human motions, and nonhuman motions. The performance of these classifiers is evaluated on a pilot dataset of radar returns that contained different events including breathing, stopped breathing, simple human motions, and movement of fan and water. Results: Our proposed pattern classification system achieved accuracies of up to $93$% in stationary subject detection, $90$% in stop-breathing detection, and $86$% in interference detection. Conclusion: Our proposed radar pattern recognition system was able to accurately distinguish the predefined events amidst interferences. Significance: Besides inmate monitoring and suicide attempt detection, this paper can be extended to other radar applications such as home-based monitoring of elderly people, apnea detection, and home occupancy detection. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this paper is to increase the accuracy of human cardiac diffusion tensor (DT) estimation in diffusion magnetic resonance imaging (dMRI) with a few diffusion gradient directions. Methods: A structure prior constrained (SPC) method is proposed. The method consists in introducing two regularizers in the conventional nonlinear least squares estimator. The two regularizers penalize the dissimilarity between neighboring DTs and the difference between estimated and prior fiber orientations, respectively. A novel numerical solution is presented to ensure the positive definite estimation. Results: Experiments on ex vivo human cardiac data show that the SPC method is able to well estimate DTs at most voxels, and is superior to state-of-the-art methods in terms of the mean errors of principal eigenvector, second eigenvector, helix angle, transverse angle, fractional anisotropy, and mean diffusivity. Conclusion: The SPC method is a practical and reliable alternative to current denoising- or regularization-based methods for the estimation of human cardiac DT. Significance: The SPC method is able to accurately estimate human cardiac DTs in dMRI with a few diffusion gradient directions. [ABSTRACT FROM AUTHOR]

Objective: A novel method is developed to identify ablation targets for the catheter treatment of ventricular tachycardia (VT). Methods: The method is based on pace-mapping, which is a validated technique to determine the catheter ablation targets. Conventionally, it consists of stimulating the heart ventricle from various sites and comparing the resulting activation pathways to that of a clinical VT by the analysis of surface electrocardiograms (ECG). In this paper, a novel pace-mapping method is presented, which does not require a reference ECG recording of the VT. A three-dimensional correlation gradient map is reconstructed by semiautomatic analysis of ECG morphological changes within the network of pace-mapping sites. In these maps, abnormal points are identified by high correlation gradient values (i.e., corresponding to slow propagation of the electric influx, as in the core of the reentrant VT circuit). The relation between the conventional and reference-less method is described theoretically and evaluated in a retrospective study including 24 VT ablation procedures. Results: The “reference-less” method was able to identify normal points with a high accuracy (negative predictive value: NPV = 97%), and to detect more abnormal points, as predicted by the theory. Correlation gradients computed by the proposed method were significantly higher in ablation zones than in other zones of the ventricle (p < 10–12), indicating excellent prediction of the ablation targets. Significance: The reference-less method might either be used in complement of the conventional method or to treat patients in whom VT cannot be induced during the intervention. [ABSTRACT FROM AUTHOR]

Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-refined bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localization tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, a refinement step is designed to explicitly impose shape prior knowledge and improve segmentation quality. This step is effective for overcoming image artifacts (e.g., due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The pipeline is fully automated, due to network’s ability to infer landmarks, which are then used downstream in the pipeline to initialize atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution, and anatomically smooth bi-ventricular 3D models, despite the presence of artifacts in input CMR volumes. [ABSTRACT FROM AUTHOR]

Cardiac magnetic resonance (CMR) images play a growing role in the diagnostic imaging of cardiovascular diseases. Full coverage of the left ventricle (LV), from base to apex, is a basic criterion for CMR image quality and is necessary for accurate measurement of cardiac volume and functional assessment. Incomplete coverage of the LV is identified through visual inspection, which is time consuming and usually done retrospectively in the assessment of large imaging cohorts. This paper proposes a novel automatic method for determining LV coverage from CMR images by using Fisher-discriminative three-dimensional (FD3D) convolutional neural networks (CNNs). In contrast to our previous method employing 2-D CNNs, this approach utilizes spatial contextual information in CMR volumes, extracts more representative high-level features, and enhances the discriminative capacity of the baseline 2-D CNN learning framework, thus, achieving superior detection accuracy. A two-stage framework is proposed to identify missing basal and apical slices in measurements of CMR volume. First, the FD3D CNN extracts high-level features from the CMR stacks. These image representations are then used to detect the missing basal and apical slices. Compared to the traditional 3-D CNN strategy, the proposed FD3D CNN minimizes within-class scatter and maximizes between-class scatter. We performed extensive experiments to validate the proposed method on more than 5000 independent volumetric CMR scans from the UK Biobank study, achieving low error rates for missing basal/apical slice detection (4.9%/4.6%). The proposed method can also be adopted for assessing LV coverage for other types of CMR image data. [ABSTRACT FROM AUTHOR]

Objective: Electrotherapy remains the most effective direct therapy against lethal cardiac arrhythmias. When an arrhythmic event is sensed, either strong electric shocks or controlled rapid pacing is automatically applied directly to the heart via an implanted cardioverter defibrillator (ICDs). Despite their success, ICDs remain a highly non-optimal therapy: the strong shocks required for defibrillation cause significant extra-cardiac stimulation, resulting in pain and long-term tissue damage, and can also limit battery life. When used in anti-tachycardia pacing mode, ICDs are also often ineffective, as the pacing electrode can be far away from the centre of the arrhythmia, making it hard for the paced wave to interrupt and terminate it. Methods: In this paper, we present two conceptual intra-cardiac directional electrode configurations in silico based on novel arrangements of pairs of positive-negative electrodes. Both configurations have the potential to cause preferential excitation on specific regions of the heart. Results: We demonstrate how the properties of the induced field varies spatially around the electrodes and how it depends upon the specific arrangements of dipole electrode pairs. The results show that when tested within anatomically-realistic rabbit ventricular models, both electrode configurations produce strong virtual electrodes on the targeted endocardial surfaces, with weaker virtual electrodes produced elsewhere. Conclusions: The proposed electrode configurations may facilitate targeted far-field anti-tachycardia pacing and/or defibrillation, which may be useful in cases where conventional anti-tachycardia pacing fails. In addition, the conceptual electrode designs intrinsically confine the electric field to the immediate vicinity of the electrodes, and may, thus, minimize pain due to unnecessary extra-cardiac stimulation. [ABSTRACT FROM AUTHOR]

Objective: The aim of this paper is to describe an automated diagnostic pipeline that uses as input only ultrasound (US) data, but is at the same time informed by a training database of multimodal magnetic resonance (MR) and US image data. Methods: We create a multimodal cardiac motion atlas from three-dimensional (3-D) MR and 3-D US data followed by multi-view machine learning algorithms to combine and extract the most meaningful cardiac descriptors for classification of dilated cardiomyopathy (DCM) patients using US data only. More specifically, we propose two algorithms based on multi-view linear discriminant analysis and multi-view Laplacian support vector machines (MvLapSVMs). Furthermore, a novel regional multi-view approach is proposed to exploit the regional relationships between the two modalities. Results: We evaluate our pipeline on the classification task of discriminating between normals and DCM patients. Results show that the use of multi-view classifiers together with a cardiac motion atlas results in a statistically significant improvement in accuracy compared to classification without the multimodal atlas. MvLapSVM was able to achieve the highest accuracy for both the global approach (92.71%) and the regional approach (94.32%). Conclusion: Our work represents an important contribution to the understanding of cardiac motion, which is an important aid in the quantification of the contractility and function of the left ventricular myocardium. Significance: The intended workflow of the developed pipeline is to make use of the prior knowledge from the multimodal atlas to enable robust extraction of indicators from 3-D US images for detecting DCM patients. [ABSTRACT FROM AUTHOR]

Cardiac computed tomography (CT) is a valuable tool for functional mechanical heart valve (MHV) assessment. An important aspect of bileaflet MHV assessment is evaluation and measurement of leaflet opening and closing angles. Performed manually, however, it is a laborious and time consuming task. In this paper, we propose an automated approach for bileaflet MHV leaflet angle computation. This method consists of four steps. After a one click selection of the MHV region on an axial image, an automatic MHV extraction using thresholding, and a connected component analysis based on voxel intensities is performed. Then, the MHV component (valve ring and two leaflets) positions are identified using random sample consensus and least square fitting. Finally, the angles are automatically computed based on the orientation of the components in each timeframe. Five multiphase CT scans from patients with a bileaflet MHV containing between 14 and 17 timepoints were used for development and another 15 were used for evaluation. The detected MHV components were scored for their overlap with real components as successful or unsuccessful. For successful results, the angles were compared to those measured by a radiologist. Qualitatively evaluated on a data set of 222 images, a total of 398 out of 444 angle computations (89.6%) were rated as successful. Compared to the angles measured by the radiologist, the successful angles showed a mean difference of 0.54° ± 3.63° from the manual calculations. The method provides a high success rate and an accurate computation of leaflet opening angles compared to manual measurements. [ABSTRACT FROM AUTHOR]

Compared to external device based approaches, a data-driven gating technique in PET imaging is advantageous as it does not require additional hardware or procedure. Currently, data-driven cardiac gating is less studied than respiratory gating. The aim of this paper is to develop a robust data-driven cardiac gating approach for clinical application. First, the central location of the heart is obtained from the corresponding CT image. A cylinder-shaped volume of interest (VOI) centered at the central location of the heart is used to confine cardiac signal calculation. The cardiac signal modeling the expansion/contraction of the heart is calculated using the second order moment of the tracer distribution in the VOI in the projection domain. The signal-to-noise ratio (SNR) of the cardiac motion signal is defined as the energy of the cardiac frequency components over the energy of other non-cardiac frequencies. The optimal cardiac signal with maximal SNR is obtained through an iterative optimization of signal extraction parameters. To validate our method, simulations of different scan parameters including tracer uptake and noise level were generated from the 4D XCAT phantom. Quantitative evaluation was achieved by comparing the extracted signal with the truth in the simulation study. Our method was also applied to 19 patients with high myocardium uptake and compared with the conventional center-of-mass based data-driven method. The simulation study suggests that two major limiting factors in the performance of our method are the myocardium/body uptake ratio and the count rate of the whole field of view. High accuracy of the detected signal was observed with myocardium/body uptake ratio > 7 and count rate > 100 counts/ms. Cardiac peak frequencies were successfully detected in all 19 patients using our method, while the conventional method obtained peaks in only 12 data sets. Our method was also visually validated by gated reconstructions. In summary, we have developed a novel dedicated data-driven cardiac gating method for PET by tracking the contraction/expansion of the heart during the scan. Quantitative evaluation using simulations and qualitative validation with clinical datasets both demonstrate that our method is a robust alternative to the device-based method with a high success rate. [ABSTRACT FROM AUTHOR]

The issue of missing values (MVs) has appeared widely in real-world datasets and hindered the use of many statistical or machine learning algorithms for data analytics due to their incompetence in handling incomplete datasets. To address this issue, several MV imputation algorithms have been developed. However, these approaches do not perform well when most of the incomplete tuples are clustered with each other, coined here as theClustered Missing Values Phenomenon, which attributes to the lack of sufficient complete tuples near an MV for imputation. In this paper, we propose theOrder-Sensitive Imputation for Clustered Missing values(OSICM) framework, in which missing values are imputed sequentially such that the values filled earlier in the process are also used for later imputation of other MVs. Obviously, the order of imputations is critical to the effectiveness and efficiency of OSICM framework. We formulate the searching of the optimal imputation order as an optimization problem, and show its NP-hardness. Furthermore, we devise an algorithm to find the exact optimal solution and propose two approximate/heuristic algorithms to trade off effectiveness for efficiency. Finally, we conduct extensive experiments on real and synthetic datasets to demonstrate the superiority of our OSICM framework. [ABSTRACT FROM AUTHOR]

Propagation of radio-frequency signals inside human body is demanding to analyze as it is a highly complex medium consisting of different frequency-dependent lossy materials of varying thickness. Moreover, experimental analyses are also unfeasible because that requires probes to be placed inside a human body to collect the signals. This paper focuses on in-body to in-body implant communication for future multinodal capsule-like leadless cardiac pacemaker technology. The frequency range of 0.3–3 GHz is analyzed using very detailed numerical simulations of digital human models. The results show that the Industrial, Scientific, and Medical radio band of the frequency range of 2.4–2.5 GHz is optimal, having the least attenuation of signals considering the size constraints of the implant antenna. Furthermore, the placement of an additional subcutaneous implant transceiver is studied. The analysis shows that the abdominal wall is the optimal position for the placement of the implant compared to shoulder and lateral side of the body. This result is further validated by anin vivoexperiment on an adult pig. The other novelty of the study is the investigation of the channel behavior based on ventricular blood volume of the heart to find out the appropriate timing of the transmission of signals between the implants. The results show that the attenuation of the signal increases with the increase in blood volume inside the heart. [ABSTRACT FROM AUTHOR]

Goal: The purpose of this paper is to improve the detection of high-frequency sounds from the heart for better identification of turbulent blood flow in partially occluded coronary arteries. This paper also describes a method for the quantitative assessment of data quality. Methods: A very light-weight dual accelerometer has been developed that places a small mechanical load on the chest. When used in conjunction with a novel correlation-based analysis, this dual-signal transducer provides an estimate to the signal-to-noise ratio (SNR) of the acoustic signal. Results: The new transducer has significantly better SNR properties than the traditional cardiac microphones. This improvement is due to increased sensitivity to high-frequency signals not a reduction in noise and is likely the result of reduced mechanical loading on the chest. Conclusion: Substantial improvement in the detection of high-frequency heart sounds is possible as is quantitative assessment of data quality. Significance: The new transducer and analysis will lead to substantial improvements in the acoustic detection of partially occluded arteries associated with coronary artery disease. It is finally possible to obtain a measurement of the quality of heart sound signals as they are being recorded. [ABSTRACT FROM PUBLISHER]

This paper presents an approach to measure the duration and the energy of instantaneous frequencies (EIFs) of the aortic (A2) and pulmonic (P2) valve closure sounds for the second heart sound (S2) based on analytic signal representation. In past studies, concepts were usually surrounded around the measurement of splits (i.e., delays between the A2 and the P2) in medical terms based only on visual inspections of the time-frequency representation of the S2s. The values were empirically estimated, and the two vital issues—A2 and P2 overlaps and low energies of P2s—were ignored. In this paper, such issues are addressed, and the relevant parameters are measured by identifying the starting and the ending positions of A2s and P2s. The diagnosis related to the duration and the EIFs of the A2 and the P2 is also examined. Furthermore, the proposed method explicitly guides to distinguish the normal/abnormal S2s and the types of S2 splits. The proposed method is characterized by the Hilbert transform-based IF estimation as well as the localization technique based on the reassignment of smoothed pseudo-Wigner–Ville distribution. To validate the idea, total 31 S2s collected from six healthy normal subjects were tested. The result computed by the proposed method shows that the mean ± standard deviation (SD) of the duration of A2s and P2s is 46.7 ± 2.5 and 41.8 ± 2.4 ms, respectively. The mean ± SD of the EIFs of A2s and P2s is 13.8 ± 2.4 and 10.5 ± 1.7, respectively. These estimated results accord with the evidence in the literature. Moreover, when the proposed method was applied to the abnormal S2s, collected from the database of the Texas Heart Institute, it could correctly distinguish the types of abnormal splits. The experimental result reveals that the accuracy rate of the proposed method is as high as up to 97%, thereby demonstrating the effectiveness of the proposed idea. [ABSTRACT FROM PUBLISHER]

Incorporation of automated electrocardiogram (ECG) analysis techniques in home monitoring applications can ensure early detection of myocardial infarction (MI), thus reducing the risk of mortality. Most of the published techniques use advanced signal processing tools, a huge number of ECG features, and complex classifiers, which make their hardware implementation difficult. This paper proposes the use of harmonic phase distribution pattern of the ECG data for MI identification. The morphological and temporal changes of the ECG waveform caused by the presence of MI are reflected in the phase distribution pattern of the Fourier harmonics. Two discriminative features, clearly reflecting these variations, are identified for each of the three standard ECG leads (II, III, and V2). Classification of the healthy and MI data is performed using a threshold-based classification rule and logistic regression. The proposed technique has achieved an average detection accuracy of 95.6% with sensitivity and specificity of 96.5% and 92.7%, respectively, for classifying all types of MI data from the Physionet Physikalisch-Technische Bundesanstalt diagnostic ECG database. The robustness of the algorithm is confirmed with real data as well. The algorithm is also implemented and validated on a microcontroller-based Arduino board, which can serve as a prototype ECG analysis device. Apart from providing comparable performance to other reported techniques, the proposed technique provides distinct advantages in terms of computational simplicity of the features, significantly reduced feature dimension, and use of simple linear classifiers which ensure faster and easier MI identification. [ABSTRACT FROM AUTHOR]

By resolving the energy of the incident X-ray photons, spectral X-ray imaging with photon counting detectors offers additional material-specific information compared to conventional X-ray imaging. This additional information can be used to improve clinical diagnosis for various applications. However, spectral imaging still faces several challenges. Amplified noise and a reduced signal-to-noise ratio on the decomposed basis material images remain a major problem, especially for low-dose applications. Furthermore, it is challenging to construct an accurate model of the spectral measurement acquisition process. In this paper, we present a novel algorithm for projection-based material decomposition. It uses an empirical polynomial model that is tuned by calibration measurements. We combine this method with a statistical model of the measured photon counts and a dictionary-based joint regularization approach. We focused on spectral coronary angiography as a potential clinical application of projection-based material decomposition with photon counting detectors. Numerical and real experiments show that spectral angiography with realistic dose levels and gadolinium contrast agent concentrations are feasible using the proposed decomposition algorithm and currently available photon-counting detector technology. [ABSTRACT FROM AUTHOR]

Objective: The purpose of this paper is to accelerate cardiac diffusion tensor imaging (CDTI) by integrating low-rankness and compressed sensing. Methods: Diffusion-weighted images exhibit both transform sparsity and low-rankness. These properties can jointly be exploited to accelerate CDTI, especially when a phase map is applied to correct for the phase inconsistency across diffusion directions, thereby enhancing low-rankness. The proposed method is evaluated both ex vivo and in vivo, and is compared to methods using either a low-rank or sparsity constraint alone. Results: Compared to using a low-rank or sparsity constraint alone, the proposed method preserves more accurate helix angle features, the transmural continuum across the myocardium wall, and mean diffusivity at higher acceleration, while yielding significantly lower bias and higher intraclass correlation coefficient. Conclusion: Low-rankness and compressed sensing together facilitate acceleration for both ex vivo and in vivo CDTI, improving reconstruction accuracy compared to employing either constraint alone. Significance: Compared to previous methods for accelerating CDTI, the proposed method has the potential to reach higher acceleration while preserving myofiber architecture features, which may allow more spatial coverage, higher spatial resolution, and shorter temporal footprint in the future. [ABSTRACT FROM AUTHOR]

Objective: In this paper, we accurately detect the state-sequence first heart sound (S1)–systole–second heart sound (S2)–diastole, i.e., the positions of S1 and S2, in heart sound recordings. We propose an event detection approach without explicitly incorporating a priori information of the state duration. This renders it also applicable to recordings with cardiac arrhythmia and extendable to the detection of extra heart sounds (third and fourth heart sound), heart murmurs, as well as other acoustic events. Methods: We use data from the 2016 PhysioNet/CinC Challenge, containing heart sound recordings and annotations of the heart sound states. From the recordings, we extract spectral and envelope features and investigate the performance of different deep recurrent neural network (DRNN) architectures to detect the state sequence. We use virtual adversarial training, dropout, and data augmentation for regularization. Results: We compare our results with the state-of-the-art method and achieve an average score for the four events of the state sequence of ${\bf F}_{1}\approx 96$ % on an independent test set. Conclusion: Our approach shows state-of-the-art performance carefully evaluated on the 2016 PhysioNet/CinC Challenge dataset. Significance: In this work, we introduce a new methodology for the segmentation of heart sounds, suggesting an event detection approach with DRNNs using spectral or envelope features. [ABSTRACT FROM AUTHOR]

The success of deep convolutional neural networks (NNs) on image classification and recognition tasks has led to new applications in very diversified contexts, including the field of medical imaging. In this paper, we investigate and propose NN architectures for automated multiclass segmentation of anatomical organs in chest radiographs (CXRs), namely for lungs, clavicles, and heart. We address several open challenges including model overfitting, reducing number of parameters, and handling of severely imbalanced data in CXR by fusing recent concepts in convolutional networks and adapting them to the segmentation problem task in CXR. We demonstrate that our architecture combining delayed subsampling, exponential linear units, highly restrictive regularization, and a large number of high-resolution low-level abstract features outperforms state-of-the-art methods on all considered organs, as well as the human observer on lungs and heart. The models use a multiclass configuration with three target classes and are trained and tested on the publicly available Japanese Society of Radiological Technology database, consisting of 247 X-ray images the ground-truth masks for which are available in the segmentation in CXR database. Our best performing model, trained with the loss function based on the Dice coefficient, reached mean Jaccard overlap scores of 95% for lungs, 86.8% for clavicles, and 88.2% for heart. This architecture outperformed the human observer results for lungs and heart. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach for the separation of heart and lung sounds (HLS) is proposed based on the nonlinear decomposition technique, variational mode decomposition (VMD) of phonocardiogram signal. Subjective validation and objective quantification are used to assess the performance of the method. The proposed technique is found to perform better than the conventional algorithms such as empirical mode decomposition (EMD), ensemble EMD, and different variants of the EMD. As the third heart sound, $S_{3}$ is an important clinical sign of cardiac failure in elderly patients, a new technique is proposed for its detection. This method is built on the VMD and the smoothed pseudo Wigner–Ville distribution. In total, 40 sets of cardiac cycles containing $S_{3}$ are obtained from publicly available databases and are used to evaluate the performance of the proposed method in noiseless condition. It is able to detect the $S_{3}$ correctly even when the normalized amplitude of $S_{3}$ is 14.1%, whereas the existing method based on the Hilbert variation decomposition requires at least 16.13% of the normalized amplitude of $S_{3}$ in comparison to the normalized amplitude of the highest peak present in the cardiac cycle. In addition to this, the result shows that the proposed method can detect $S_{3}$ in noisy cases up to SNR level of −5 dB. [ABSTRACT FROM AUTHOR]

Echocardiography examination of the blood flow is currently either restricted to 1-D techniques in real-time or experimental offline 2-D methods. This paper presents an implementation of transverse oscillation for real-time 2-D vector flow imaging (VFI) on a commercial BK Ultrasound scanner. A large field-of-view (FOV) sequence for studying flow dynamics at 11 frames per second (fps) and a sequence for studying peak systolic velocities (PSVs) with a narrow FOV at 36 fps were validated. The VFI sequences were validated in a flow rig with continuous laminar parabolic flow and in a pulsating flow pump system before being tested in vivo, where measurements were obtained on two healthy volunteers. Mean PSV from 11 cycles was 155 cms−1 with a precision of ±9.0% for the pulsating flow pump. In vivo, PSV estimated in the ascending aorta was 135 cms−1 ± 16.9% for eight cardiac cycles. Furthermore, in vivo flow dynamics of the left ventricle and in the ascending aorta were visualized. In conclusion, angle independent 2-D VFI on a phased array has been implemented in real time, and it is capable of providing quantitative and qualitative flow evaluations of both the complex and fully transverse flow. [ABSTRACT FROM AUTHOR]

Intravascular polarimetry with polarization sensitive optical frequency domain imaging (PS-OFDI) measures polarization properties of the vessel wall and offers characterization of coronary atherosclerotic lesions beyond the cross-sectional image of arterial microstructure available to conventional OFDI. A previous study of intravascular polarimetry in cadaveric human coronary arteries found that tissue birefringence and depolarization provide valuable insight into key features of atherosclerotic plaques. In addition to various tissue components, catheter and sample motion can also influence the polarization of near infrared light as used by PS-OFDI. This paper aimed to evaluate the robustness and repeatability of imaging tissue birefringence and depolarization in a clinical setting. 30 patients scheduled for percutaneous coronary intervention at the Erasmus Medical Center underwent repeated PS-OFDI pullback imaging, using commercial imaging catheters in combination with a custom-built PS-OFDI console. We identified 274 matching cross sections among the repeat pullbacks to evaluate the reproducibility of the conventional backscatter intensity, the birefringence, and the depolarization signals at each spatial location across the vessel wall. Bland–Altman analysis revealed best agreement for the birefringence measurements, followed by backscatter intensity, and depolarization, when limiting the analysis to areas of meaningful birefringence. Pearson correlation analysis confirmed highest correlation for birefringence (0.86), preceding backscatter intensity (0.83), and depolarization (0.78). Our results demonstrate that intravascular polarimetry generates robust maps of tissue birefringence and depolarization in a clinical setting. This outcome motivates the use of intravascular polarimetry for future clinical studies that investigate polarization properties of arterial atherosclerosis. [ABSTRACT FROM AUTHOR]

Cardiac X-ray computed tomography (CT) imaging is still challenging due to the cardiac motion during CT scanning, which leads to the presence of motion artifacts in the reconstructed image. In response, many cardiac X-ray CT imaging algorithms have been proposed, based on motion estimation (ME) and motion compensation (MC), to improve the image quality by alleviating the motion artifacts in the reconstructed image. However, these ME/MC algorithms are mainly based on an axial scan or a low-pitch helical scan. In this paper, we propose a ME/MC-based cardiac imaging algorithm for the data set acquired from a helical scan with an ordinary pitch of around 1.0 so as to obtain the whole cardiac image within a single scan of short time without ECG gating. In the proposed algorithm, a sequence of partial angle reconstructed (PAR) images is generated by using consecutive parts of the sinogram, each of which has a small angular span. Subsequently, an initial 4-D motion vector field (MVF) is obtained using multiple pairs of conjugate PAR images. The 4-D MVF is then refined based on an image quality metric so as to improve the quality of the motion-compensated image. Finally, a time-resolved cardiac image is obtained by performing motion-compensated image reconstruction by using the refined 4-D MVF. Using digital XCAT phantom data sets and a human data set commonly obtained via a helical scan with a pitch of 1.0, we demonstrate that the proposed algorithm significantly improves the image quality by alleviating motion artifacts. [ABSTRACT FROM AUTHOR]

Objective: This paper presents a framework for temporal shape analysis to capture the shape and changes of anatomical structures from three-dimensional+t(ime) medical scans. Method: We first encode the shape of a structure at each time point with the spectral signature, i.e., the eigenvalues and eigenfunctions of the Laplace operator. We then expand it to capture morphing shapes by tracking the eigenmodes across time according to the similarity of their eigenfunctions. The similarity metric is motivated by the fact that small-shaped deformations lead to minor changes in the eigenfunctions. Following each eigenmode from the beginning to end results in a set of eigenmode curves representing the shape and its changes over time. Results: We apply our encoding to a cardiac dataset consisting of series of segmentations outlining the right and left ventricles over time. We measure the accuracy of our encoding by training classifiers on discriminating healthy adults from patients that received reconstructive surgery for Tetralogy of Fallot (TOF). The classifiers based on our encoding significantly surpass deformation-based encodings of the right ventricle, the structure most impacted by TOF. Conclusion: The strength of our framework lies in its simplicity: It only assumes pose invariance within a time series but does not assume point-to-point correspondence across time series or a (statistical or physical) model. In addition, it is easy to implement and only depends on a single parameter, i.e., the number of curves. [ABSTRACT FROM PUBLISHER]

Electrocardiographic Imaging has become an increasingly used technique for non-invasive diagnosis of cardiac arrhythmias, although the need for medical imaging technology to determine the anatomy hinders its introduction in the clinical practice. This paper explores the ability of a new metric based on the inverse reconstruction quality for the location and orientation of the atrial surface inside the torso. Body surface electrical signals from 31 realistic mathematical models and four AF patients were used to estimate the optimal position of the atria inside the torso. The curvature of the L-curve from the Tikhonov method, which was found to be related to the inverse reconstruction quality, was measured after application of deviations in atrial position and orientation. Independent deviations in the atrial position were solved by finding the maximal L-curve curvature with an error of 1.7 ± 2.4 mm in mathematical models and 9.1 ± 11.5 mm in patients. For the case of independent angular deviations, the error in location by using the L-curve was 5.8±7.1° in mathematical models and 12.4° ± 13.2° in patients. The ability of the L-curve curvature was tested also under superimposed uncertainties in the three axis of translation and in the three axis of rotation, and the error in location was of 2.3 ± 3.2 mm and 6.4° ± 7.1° in mathematical models, and 7.9±10.7 mm and 12.1°±15.5° in patients. The curvature of L-curve is a useful marker for the atrial position and would allow emending the inaccuracies in its location. [ABSTRACT FROM PUBLISHER]

The use of synthetic sequences is one of the most promising tools for advanced in silico evaluation of the quantification of cardiac deformation and strain through 3-D ultrasound (US) and magnetic resonance (MR) imaging. In this paper, we propose the first simulation framework which allows the generation of realistic 3-D synthetic cardiac US and MR (both cine and tagging) image sequences from the same virtual patient. A state-of-the-art electromechanical (E/M) model was exploited for simulating groundtruth cardiac motion fields ranging from healthy to various pathological cases, including both ventricular dyssynchrony and myocardial ischemia. The E/M groundtruth along with template MR/US images and physical simulators were combined in a unified framework for generating synthetic data. We efficiently merged several warping strategies to keep the full control of myocardial deformations while preserving realistic image texture. In total, we generated 18 virtual patients, each with synthetic 3-D US, cine MR, and tagged MR sequences. The simulated images were evaluated both qualitatively by showing realistic textures and quantitatively by observing myocardial intensity distributions similar to real data. In particular, the US simulation showed a smoother myocardium/background interface than the state-of-the-art. We also assessed the mechanical properties. The pathological subjects were discriminated from the healthy ones by both global indexes (ejection fraction and the global circumferential strain) and regional strain curves. The synthetic database is comprehensive in terms of both pathology and modality, and has a level of realism sufficient for validation purposes. All the 90 sequences are made publicly available to the research community via an open-access database. [ABSTRACT FROM PUBLISHER]

Objective: Zebrafish larva heart microinjection is a widely used technique in cardiac disease study. Compared with intensively researched rotation control of spherical or nearly spherical targets with clear structures, such as cells and embryos, 3-D rotation control of zebrafish larva demands new techniques due to its nontransparent structures and irregular outlines. Methods: In this paper, we present a vision–servo system to automate the rotation process of zebrafish larva body. A switched control strategy is adopted to rotate zebrafish larva about the optical axis by using two micropipettes. Precisely rolling about larva body is performed, which involves a custom-designed rotational micromanipulator. A vision detection and online tracking algorithm is also developed to meet the requirement of visual servoing. With designed rotation control strategy, zebrafish larva heart can be adjusted to a desired orientation, which is often towards the injection pipette tip. Results: Experimental results show that the designed system is capable of achieving high success rate of 94% about $z$-axis rotation and 100% about $x$-axis with 50 trails. The system also performs an average speed of 44 s/larva with a satisfied rotation accuracy of 0.5 $^\circ$ in the horizontal plane and 2.5 $^\circ$ about its roll axis. Conclusion: The proposed strategy is effective in flexibly manipulating larvae in 3-D. Significance: The developed 3-D rotation control scheme is able to be applied to injection of various organs in zebrafish larva body for different experimental requirements. [ABSTRACT FROM PUBLISHER]

This paper presents a person identification mechanism using electrocardiogram (ECG) signals with abnormal cardiac conditions in network environments. A total of 164 subjects were used in this paper using three different databases containing various irregular heart states from MIT-BIH arrhythmia database (MITDB), MIT-BIH supraventricular arrhythmia database (SVDB), and Charles Sturt diabetes complication screening initiative (DiSciRi) database. We proposed a simple yet effective biometric sample extraction technique for ECG samples with abnormal cardiac conditions to improve the person identification process. These sample points were then applied to four classifiers to verify the robustness of identification. Varying numbers of enrollment and recognition QRS complexes were used to validate the stability of the proposed method. Our experimentation results show that the biometric technique outperforms existing methods lacking the ability to efficiently extract features for biometric matching. This is evident by obtaining high accuracy results of 96.7% for MITDB, 96.4% for SVDB, and 99.3% for DiSciRi. Moreover, high sensitivity, specificity, positive predictive value, and Youden Index’s values further verifies the reliability of the proposed method. This technique also suggests the possibility of improving the classification performance using ECG recordings with low sampling frequency and increased number of ECG samples. [ABSTRACT FROM PUBLISHER]

Patients with drug-refractory heart failure can greatly benefit from cardiac resynchronization therapy (CRT). A CRT device can resynchronize the contractions of the left ventricle (LV) leading to reduced mortality. Unfortunately, 30%–50% of patients do not respond to treatment when assessed by objective criteria such as cardiac remodeling. A significant contributing factor is the suboptimal placement of the LV lead. It has been shown that placing this lead away from scar and at the point of latest mechanical activation can improve response rates. This paper presents a comprehensive and highly automated system that uses scar and mechanical activation to plan and guide CRT procedures. Standard clinical preoperative magnetic resonance imaging is used to extract scar and mechanical activation information. The data are registered to a single 3-D coordinate system and visualized in novel 2-D and 3-D American Heart Association plots enabling the clinician to select target segments. During the procedure, the planning information is overlaid onto live fluoroscopic images to guide lead deployment. The proposed platform has been used during 14 CRT procedures and validated on synthetic, phantom, volunteer, and patient data. [ABSTRACT FROM AUTHOR]

We seek to use computed tomography (CT) to characterize regional lung parenchymal deformation during high-frequency and multi-frequency oscillatory ventilation. Periodic motion of thoracic structures results in artifacts of CT images obtained by standard reconstruction algorithms, especially for frequencies exceeding that of the X-ray source rotation. In this paper, we propose an acquisition and reconstruction technique for high-resolution imaging of the thorax during periodic motion. Our technique relies on phase-binning projections according to the frequency of subject motion relative to the scanner rotation, prior to volumetric reconstruction. The mathematical theory and limitations of the proposed technique are presented, and then validated in a simulated phantom as well as a living porcine subject during oscillatory ventilation. The 4-D image sequences obtained using this frequency-selective reconstruction technique yielded high-spatio-temporal resolution of the thorax during periodic motion. We conclude that the frequency-based selection of CT projections is ideal for characterizing dynamic deformations of thoracic structures that are ordinarily obscured by motion artifact using conventional reconstruction techniques. [ABSTRACT FROM PUBLISHER]

Traditional passwords are inadequate as cryptographic keys, as they are easy to forge and are vulnerable to guessing. Human biometrics have been proposed as a promising alternative due to their intrinsic nature. Electrocardiogram (ECG) is an emerging biometric that is extremely difficult to forge and circumvent, but has not yet been heavily investigated for cryptographic key generation. ECG has challenges with respect to immunity to noise, abnormalities, etc. In this paper, we propose a novel key generation approach that extracts keys from real-valued ECG features with high reliability and entropy in mind. Our technique, called interval optimized mapping bit allocation (IOMBA), is applied to normal and abnormal ECG signals under multiple session conditions. We also investigate IOMBA in the context of different feature extraction methods, such as wavelet, discrete cosine transform, etc., to find the best method for feature extraction. Experiments of IOMBA show that 217-, 38-, and 100-bit keys with 99.9%, 97.4%, and 95% average reliability and high entropy can be extracted from normal, abnormal, and multiple session ECG signals, respectively. By allowing more errors or lowering entropy, key lengths can be further increased by tunable parameters of IOMBA, which can be useful in other applications. While IOMBA is demonstrated on ECG, it should be useful for other biometrics as well. [ABSTRACT FROM AUTHOR]