Quantitative evaluation of local myocardial blood volume in contrast echocardiography.

Myocardial perfusion is usually assessed by Single Photon Emission Computed Tomography (SPECT) imaging. Information about myocardial perfusion is sometimes deduced from angiography or Computed Tomography (CT) angiography, which detect coronary artery stenosis. Contrast echocardiography can be used for that purpose as well. However, the currently available data acquisition and analysis methods are difficult to manage in the clinical environment. This paper presents a novel contrast echo data acquisition protocol and parameter extraction procedure, providing an automatic quantitative evaluation of the local myocardial blood volume for the entire left ventricular myocardium. This information is indicative of local perfusion. Our method evaluates the myocardial blood volume according to the local gray level intensity, as measured during a single heartbeat, when there is a distinct myocardial opacification (based on visual estimation). The echocardiographic image analysis is based on a new attenuation correction technique, which compensates for the ultrasonic signal attenuation in both the tissue and the contrast agent. In comparison, the existing contrast echo based methods utilize the long-term temporal variability of the gray level to extract information regarding the local myocardial blood flow velocity. Our technique has been tested on 17 cine-loops of 15 different patients. We have found a high correlation between abnormal segments, detected automatically by our technique, and segments that have been clinically diagnosed as ischemic (at rest) or infarcted. For that purpose, we have defined ischemic segments as segments fed by coronary arteries with severe stenosis, as determined by angiography, and infarcted segments as segments after Acute Myocardial Infarction, as detected by electrocardiography. Furthermore, we have found a high correlation between the automatically calculated myocardial blood volume levels and the clinical evaluation of segmental contractility, based on echocardiographic imaging.

Quantitative analysis of echocardiographic cine-loops using a 2D-strain algorithm in correlation with thallium-201 SPECT imaging.

The aim of this study was to investigate the correlation between the regional left ventricular (LV) contractility, assessed by two-dimensional (2D)-strain echocardiographic images, and regional LV perfusion, determined by thallium-201 single photon emission computed tomography (SPECT) imaging. Forty-five hospitalized patients with suspected myocardial ischemia (32 males, mean age 69 +/- 12 years) have undergone both echocardiography and SPECT imaging on the same day. The echocardiographic data has been obtained in three apical views. The data for each view has been processed by the 2D-strain algorithm, to produce six peak systolic longitudinal strain measurements per view, one per segment. The resulting measurements have been compared with the corresponding regions within the SPECT bull's-eye map (at rest). In order to handle inaccuracies in the positioning of the echocardiographic transducer, the two datasets have undergone registration based on local correlation. The mean correlation coefficients between SPECT perfusion and peak systolic longitudinal strain for the apical long-axis, apical two-chamber, and apical four-chamber views were 0.55 +/- 0.36, 0.47 +/- 0.35, and 0.64 +/- 0.30 respectively (negative correlation coefficients have been considered as zero). The overall mean correlation coefficient was 0.56 +/- 0.34. The scatter graphs of the average values of perfusion and the strain values showed a nonlinear relation between the two parameters. The average correlation coefficient is comparable to the values reported for the correlation between the average LV function, based on visual analysis of echocardiographic imaging, and the average LV perfusion, based on SPECT imaging.

Automatic endocardial-boundary detection in low mechanical-index contrast echocardiography.

This paper presents a novel algorithm, aimed at automatic endocardial boundary (inner boundary) detection in myocardial opacification scenarios. The data acquisition protocol uses (on purpose) low mechanical index imaging (i.e., weak ultrasound signal), so that the acquired images are characterized by low signal-to-noise ratios. The proposed algorithm is based on converting the frames, given in Cartesian coordinates, into polar coordinates, and applying a set of filters in order to compute the initial estimation of the endocardial boundary. The final estimation of the endocardial boundary is produced by an error correction process, which uses both spatial and temporal filtering. The estimated boundaries are converted into Cartesian coordinates, for display. Our algorithm has been tested on nine cine-loops. The resulting myocardial outlines have been separately assessed by two clinicians, scoring each segment in each cine-loop on a scale between 5 (excellent) and 1 (completely unacceptable). The mean overall score is 3.8 +/- 0.8, which seems adequate. The same clinicians have also manually drawn the contours of the endocardial boundary for the end-systolic and the end-diastolic frames of each cine-loop. The results show, that the mismatch between the automatically determined outlines and the manually drawn outlines is of the same order of magnitude as the interobserver variability. These results further support the validity of our method.

Stationary clutter rejection in echocardiography.

Clutter is one of the most problematic artifacts in echocardiography. It sometimes blocks substantial portions of the image, making the diagnosis in these areas difficult, if not impossible. This is, to our knowledge, the first study aimed solely at automatic clutter rejection, performed in postprocessing, without changing the data-acquisition method. The procedure is based on the fact that the motion of the organs causing most of the clutter (e.g., the ribcage and the lungs) is much slower than that of the cardiac muscle, so that the clutter shows very small changes during a single cardiac cycle. The algorithm has been successfully tested on a set of 16 cineloops in apical two-chamber and apical four-chamber views, belonging to 16 different patients. The results show a high probability of clutter detection, while maintaining a low probability for erroneous detection of pixels as clutter.

Adaptive brightness transfer functions in echocardiography.

Despite the clear advantages of echocardiography as a diagnostic tool, its images tend to be noisy and unclear. This paper presents an innovative algorithm, called ABTF (adaptive brightness transfer function), designed to optimally adjust the gray-levels used in echocardiography. The algorithm is aimed at aiding in visual tissue classification and texture-based visual tissue tracking in echocardiographic images. The ABTF method is based on fitting the cine-loop's gray-level histogram to a sum of three Gaussian functions, each of which relates to a different region within the image, the left ventricular cavity, the relatively dark regions within the cardiac muscle and the bright regions within the cardiac muscle. The procedure's feasibility has been supported by a test-set, including 23 echocardiographic cine-loops from 10 different patients. The resulting image quality appears to be superior to that of the original images, tending to show better contrast and a higher dynamic range of gray-levels within the cardiac muscle. According to two expert cardiologists, who have blindly ranked the image quality of each cine-loop on a scale from 1 to 10, where 10 corresponds to the highest possible image quality, the mean score of the original cine-loops is 7.1 +/- 1.1, while the mean score of the cine-loops to which ABTF has been applied is 8.0 +/- 1.2.