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.

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.

In order to detect the quasi-stationary states of the heart within a cardiac cycle from echocardiography data, we present an algorithm that uses non-linear filtering and boundary detection. The non-linear filtering algorithm involves anisotropic diffusion to remove the speckle noise from the data and to smoothen the homogeneous regions while preserving the edges. Following this, we perform binary thresholding and boundary detection, and observe the positional changes in the region of interest. From a series of echocardiography images, we derived the regions of cardiac quiescence, which we then plotted on the electrocardiograph (ECG) R-R interval. It is observed that the quiescence occurs in the diastolic region of the ECG signal, but the position and length of quiescence varies across multiple cardiac cycles for the same individual. © 2012 IEEE.

OBJECTIVE The objective of our study was to evaluate whether facial and chest photographs obtained simultaneously with radiographs increase radiologists’ detection rate of labeling errors. MATERIALS AND METHODS We obtained simultaneous portable radiographs and photographs of 34 patients. We generated 88 pairs of chest radiographs (one recent radiograph, one prior radiograph) and compiled a set of 20 pairs for reader review. Two, three, or four mismatched pairs (i.e., pairs containing radiographs of different patients) were introduced into each list. Ten radiologist readers blinded to the presence of mismatches interpreted the 20 radiograph pairs. Readers then reviewed a second set of 20 pairs containing mismatches but photographs of the patients obtained at the time of imaging were attached to the radiographs. Readers were not instructed regarding the purpose of the photographs. The mismatch detection rate and time for interpretation was recorded for both sessions. The two-tailed Fisher exact test was used to evaluate differences in mismatch detection rates between sessions, with a p value of less than 0.05 being considered significant. RESULTS The error detection rates without (3/24 = 12.5%) and with (16/25 = 64%) photographs significantly differed (p = 0.0003). The average interpretation times without and with photographs were 35.73 and 26.51 minutes, respectively (two-tailed Student t test, p = 0.1165). CONCLUSION The use of photographs increased the detection of errors without a concomitant increase in film interpretation time, which may translate into improvements in patient safety without an increase in interpretation time.