Detection of quiescent cardiac phases in echocardiography data using nonlinear filtering and boundary detection techniques.

We describe an algorithm to detect cardiac quiescence within a heartbeat using nonlinear filtering and boundary detection techniques in echocardiography images. The motivation for detection of these quiescent phases is to provide improved cardiac gating to obtain motion-artifact-free images of the heart at cardiac computed tomography (CT). Currently, cardiac gating is provided through electrocardiography (ECG), which does not provide information about the instantaneous mechanical state of the heart. Our goal is to test if information about the actual mechanical motion of the heart obtained from B-mode echocardiographic data could potentially be used for gating purposes. The nonlinear filtering algorithm presented involves anisotropic diffusion to smoothen the homogeneous regions of the B-mode images while preserving image edges that represent myocardial boundaries. Following this, we detect the boundary of a particular region of interest (ROI) using a thresholding step. The positional changes of this ROI are then observed for quiescent phases over multiple cardiac cycles using the ECG's R-R interval. In a pilot study, seven subjects were imaged in the apical, four-chamber view, and quiescence of the interventricular septum was primarily observed in the diastolic region of the ECG signal. However, the position and length of quiescence vary across multiple heartbeats for the same individual and for different individuals as well. The center of quiescence for the seven patients ranged from 51 to 84 % and did not show a trend with heart rates, which ranged from 54 to 83 beats per minute. The gating intervals based on such analysis of echocardiographic signals could potentially optimize cardiac CT gating.

Seismocardiography-Based Detection of Cardiac Quiescence.

Cardiac-computed tomography angiography (CTA) is a minimally invasive imaging technology for characterizing coronary arteries. A fundamental limitation of CTA imaging is cardiac movement, which can cause artifacts and reduce the quality of the obtained images. To mitigate this problem, current approaches involve gating the image based on the electrocardiogram (ECG) to predict the timing of quiescent periods of the cardiac cycle. This paper focuses on developing a foundation for using a mechanical alternative to the ECG for finding these quiescent periods: the seismocardiogram (SCG). SCG was used to determine beat-by-beat systolic and diastolic quiescent periods of the cardiac cycle for nine healthy subjects, and 11 subjects with various cardiovascular diseases. To reduce noise in the SCG, and quantify these quiescent periods, a Kalman filter was designed to extract the velocity of chest wall movement from the recorded SCG signals. The average systolic and diastolic quiescent periods were centered at 29% and 76% for the healthy subjects, and 33% and 79% for subjects with cardiovascular disease. Both inter and intrasubject variability in the quiescent phases were observed compared to ECG-predicted phases, suggesting that the ECG may be a suboptimal modality for predicting quiescence, and that the SCG provides complementary data to the ECG.

Characterization of cardiac quiescence from retrospective cardiac computed tomography using a correlation-based phase-to-phase deviation measure.

PURPOSE: Accurate knowledge of cardiac quiescence is crucial to the performance of many cardiac imaging modalities, including computed tomography coronary angiography (CTCA). To accurately quantify quiescence, a method for detecting the quiescent periods of the heart from retrospective cardiac computed tomography (CT) using a correlation-based, phase-to-phase deviation measure was developed. METHODS: Retrospective cardiac CT data were obtained from 20 patients (11 male, 9 female, 33-74 yr) and the left main, left anterior descending, left circumflex, right coronary artery (RCA), and interventricular septum (IVS) were segmented for each phase using a semiautomated technique. Cardiac motion of individual coronary vessels as well as the IVS was calculated using phase-to-phase deviation. As an easily identifiable feature, the IVS was analyzed to assess how well it predicts vessel quiescence. Finally, the diagnostic quality of the reconstructed volumes from the quiescent phases determined using the deviation measure from the vessels in aggregate and the IVS was compared to that from quiescent phases calculated by the CT scanner. Three board-certified radiologists, fellowship-trained in cardiothoracic imaging, graded the diagnostic quality of the reconstructions using a Likert response format: 1 = excellent, 2 = good, 3 = adequate, 4 = nondiagnostic. RESULTS: Systolic and diastolic quiescent periods were identified for each subject from the vessel motion calculated using the phase-to-phase deviation measure. The motion of the IVS was found to be similar to the aggregate vessel (AGG) motion. The diagnostic quality of the coronary vessels for the quiescent phases calculated from the aggregate vessel (PAGG) and IVS (PIV S) deviation signal using the proposed methods was comparable to the quiescent phases calculated by the CT scanner (PCT). The one exception was the RCA, which improved for PAGG for 18 of the 20 subjects when compared to PCT (PCT = 2.48; PAGG = 2.07, p = 0.001). CONCLUSIONS: A method for quantifying the motion of specific coronary vessels using a correlation-based, phase-to-phase deviation measure was developed and tested on 20 patients receiving cardiac CT exams. The IVS was found to be a suitable predictor of vessel quiescence. The diagnostic quality of the quiescent phases detected by the proposed methods was comparable to those calculated by the CT scanner. The ability to quantify coronary vessel quiescence from the motion of the IVS can be used to develop new CTCA gating techniques and quantify the resulting potential improvement in CTCA image quality.

Seismocardiography-based detection of cardiac quiescence for cardiac computed tomography angiography.

As a measure of chest wall acceleration caused by cardiac motion, the seismocardiogram (SCG) has the potential to supplement the electrocardiogram (ECG) to more accurately trigger cardiac computed tomography angiography (CTA) data acquisition during periods of cardiac quiescence. The SCG was used to identify the systolic and diastolic quiescent periods of the cardiac cycle on a beat-by-beat basis and from composite velocity signals for nine healthy subjects. The cardiac velocity transmitted to the chest wall was calculated using a Kalman filter. The average systolic and diastolic quiescent periods were centered at 30% and 76%, respectively. Inter- and intra-subject variability of the quiescent phases with respect to the ECG was observed, suggesting that the ECG may be a suboptimal modality for predicting cardiac quiescence.

Detection of Cardiac Quiescence from B-Mode Echocardiography Using a Correlation-Based Frame-to-Frame Deviation Measure.

Two novel methods for detecting cardiac quiescent phases from B-mode echocardiography using a correlation-based frame-to-frame deviation measure were developed. Accurate knowledge of cardiac quiescence is crucial to the performance of many imaging modalities, including computed tomography coronary angiography (CTCA). Synchronous electrocardiography (ECG) and echocardiography data were obtained from 10 healthy human subjects (four male, six female, 23-45 years) and the interventricular septum (IVS) was observed using the apical four-chamber echocardiographic view. The velocity of the IVS was derived from active contour tracking and verified using tissue Doppler imaging echocardiography methods. In turn, the frame-to-frame deviation methods for identifying quiescence of the IVS were verified using active contour tracking. The timing of the diastolic quiescent phase was found to exhibit both inter- and intra-subject variability, suggesting that the current method of CTCA gating based on the ECG is suboptimal and that gating based on signals derived from cardiac motion are likely more accurate in predicting quiescence for cardiac imaging. Two robust and efficient methods for identifying cardiac quiescent phases from B-mode echocardiographic data were developed and verified. The methods presented in this paper will be used to develop new CTCA gating techniques and quantify the resulting potential improvement in CTCA image quality.

Novel Tool for Complete Digitization of Paper Electrocardiography Data.

OBJECTIVE: We present a Matlab-based tool to convert electrocardiography (ECG) information from paper charts into digital ECG signals. The tool can be used for long-term retrospective studies of cardiac patients to study the evolving features with prognostic value. METHODS AND PROCEDURES: To perform the conversion, we: 1) detect the graphical grid on ECG charts using grayscale thresholding; 2) digitize the ECG signal based on its contour using a column-wise pixel scan; and 3) use template-based optical character recognition to extract patient demographic information from the paper ECG in order to interface the data with the patients' medical record. To validate the digitization technique: 1) correlation between the digital signals and signals digitized from paper ECG are performed and 2) clinically significant ECG parameters are measured and compared from both the paper-based ECG signals and the digitized ECG. RESULTS: The validation demonstrates a correlation value of 0.85-0.9 between the digital ECG signal and the signal digitized from the paper ECG. There is a high correlation in the clinical parameters between the ECG information from the paper charts and digitized signal, with intra-observer and inter-observer correlations of 0.8-0.9 (p < 0.05), and kappa statistics ranging from 0.85 (inter-observer) to 1.00 (intra-observer). CONCLUSION: The important features of the ECG signal, especially the QRST complex and the associated intervals, are preserved by obtaining the contour from the paper ECG. The differences between the measures of clinically important features extracted from the original signal and the reconstructed signal are insignificant, thus highlighting the accuracy of this technique. CLINICAL IMPACT: Using this type of ECG digitization tool to carry out retrospective studies on large databases, which rely on paper ECG records, studies of emerging ECG features can be performed. In addition, this tool can be used to potentially integrate digitized ECG information with digital ECG analysis programs and with the patient's electronic medical record.

An active contour based method for analyzing cardiac quiescence from echocardiography.

A semi-automated method for analyzing cardiac quiescence of anatomical cardiac features from two-dimensional echocardiographic cine data is presented. The method utilizes both active contour and optical flow techniques for feature identification and tracking. A curvature-based potential surface was used in the active contour calculations to attract the contour to regions of inflection on the image surface rather than the standard gradient-based surface that attracts the contour to strong edges. After identifying the feature in each frame, the frame-to-frame correlation matrix of the feature was calculated with correlation values corresponding to how well the feature matched between frames. Therefore prolonged regions of high correlation correspond to periods of cardiac quiescence. The location and duration of these periods were automatically identified from the correlation matrix by finding the largest region around each time index with a mean correlation above a specified threshold. In parallel, the position of the feature was calculated for each frame by finding the centroid of the pixel locations inside the contour. From this trajectory, the magnitude of the two-dimensional velocity was calculated. These methods were used to analyze the quiescence of the interventricular septum from an apical four-chamber echocardiogram performed on a human subject. Correlation-derived quiescent phases were observed to coincide with periods of the cardiac cycle with minimal velocity magnitude.

A system for seismocardiography-based identification of quiescent heart phases: implications for cardiac imaging.

Seismocardiography (SCG), a representation of mechanical heart motion, may more accurately determine periods of cardiac quiescence within a cardiac cycle than the electrically derived electrocardiogram (EKG) and, thus, may have implications for gating in cardiac computed tomography. We designed and implemented a system to synchronously acquire echocardiography, EKG, and SCG data. The device was used to study the variability between EKG and SCG and characterize the relationship between the mechanical and electrical activity of the heart. For each cardiac cycle, the feature of the SCG indicating Aortic Valve Closure was identified and its time position with respect to the EKG was observed. This position was found to vary for different heart rates and between two human subjects. A color map showing the magnitude of the SCG acceleration and computed velocity was derived, allowing for direct visualization of quiescent phases of the cardiac cycle with respect to heart rate.

A trimodal system for the acquisition of synchronous echocardiography, electrocardiography, and seismocardiography data.

A novel system was developed to acquire synchronous echocardiography, electrocardiography (EKG), and seismocardiography (SCG) data. The system was developed to facilitate the study of the relationship between the mechanical and electrical characteristics of the heart. The system has both a hardware and software component. The hardware component consists of an application-specific device designed and built to acquire both SCG and EKG signals simultaneously. The software component consists of a package developed to record and synchronize data from both the device and a clinical ultrasound machine. A feasibility test was performed by simultaneous acquisition of a synchronous dataset from a human subject.

Spectrum analysis of seismic surface waves and its applications in seismic landmine detection.

In geophysics, spectrum analysis of surface waves (SASW) refers to a noninvasive method for soil characterization. However, the term spectrum analysis can be used in a wider sense to mean a method for determining and identifying various modes of seismic surface waves and their properties such as velocity, polarization, etc. Surface waves travel along the free boundary of a medium and can be easily detected with a transducer placed on the free surface of the boundary. A new method based on vector processing of space-time data obtained from an array of triaxial sensors is proposed to produce high-resolution, multimodal spectra from surface waves. Then individual modes can be identified in the spectrum and reconstructed in the space-time domain; also, reflected waves can be separated easily from forward waves in the spectrum domain. This new SASW method can be used for detecting and locating landmines by analyzing the reflected waves for resonance. Processing examples are presented for numerically generated data, experimental data collected in a laboratory setting, and field data.