Quantification of LV Volumes with 4D Real-Time Echocardiography

This paper presents a new 4D (3D+Time) expansion of echocardiographic volumes on complex exponential wavelet-like basis functions called Brushlets. Brushlet functions offer good localization in time and frequency and decompose a signal into distinct patterns of oriented textures, invariant to intensity and contrast range. Automatic left ventricle (LV) endocardial border detection is carried out in the transform domain where speckle noise is attenuated while cardiac structure location is preserved. Quantitative validation and clinical applications of this new spatio-temporal analysis tool are reported with results on phantoms and clinical data sets to quantify LV volumes and ejection fraction

Spatio-temporal directional analysis of 4D echocardiography

Speckle noise corrupts ultrasonic data by introducing sharp changes in an echocardiographic image intensity profile, while attenuation alters the intensity of equally significant cardiac structures. These properties introduce inhomogeneity in the spatial domain and suggests that measures based on phase information rather than intensity are more appropriate for denoising and cardiac border detection. The present analysis method relies on the expansion of temporal ultrasonic volume data on complex exponential wavelet-like basis functions called Brushlets. These basis functions decompose a signal into distinct patterns of oriented textures. Projected coefficients are associated with distinct 'brush strokes' of a particular size and orientation. 4D overcomplete brushlet analysis is applied to temporal echocardiographic values. We show that adding the time dimension in the analysis dramatically improves the quality and robustness of the method without adding complexity in the design of a segmentation tool. We have investigated mathematical and empirical methods for identifying the most 'efficient' brush stroke sizes and orientations for decomposition and reconstruction on both phantom and clinical data. In order to determine the 'best tiling' or equivalently, the 'best brushlet basis', we use an entropy-based information cost metric function. Quantitative validation and clinical applications of this new spatio-temporal analysis tool are reported for balloon phantoms and clinical data sets

LV Volume Quantification via Spatiotemporal Analysis of Real-Time 3-D Echocardiography

This paper presents a method of four-dimensional (4-D) (3-D+Time) space-frequency analysis for directional denoising and enhancement of real-time three-dimensional (RT3D) ultrasound and quantitative measures in diagnostic cardiac ultrasound. Expansion of echocardiographic volumes is performed with complex exponential wavelet-like basis functions called brushlets. These functions offer good localization in time and frequency and decompose a signal into distinct patterns of oriented harmonics, which are invariant to intensity and contrast range. Deformable-model segmentation is carried out on denoised data after thresholding of transform coefficients. This process attenuates speckle noise while preserving cardiac structure location. The superiority of 4-D over 3-D analysis for decorrelating additive white noise and multiplicative speckle noise on a 4-D phantom volume expanding in time is demonstrated. Quantitative validation, computed for contours and volumes, is performed on in vitro balloon phantoms. Clinical applications of this spatiotemporal analysis tool are reported for six patient cases providing measures of left ventricular volumes and ejection fraction

Lv volume quantification via spatiotemporal analysis of real-time 3-d echocardiography

Abstract—This paper presents a method of four-dimensional (4-D) (3-D + Time) space–frequency analysis for directional denoising and enhancement of real-time three-dimensional (RT3D) ultrasound and quantitative measures in diagnostic cardiac ultrasound. Expansion of echocardiographic volumes is performed with complex exponential wavelet-like basis functions called brushlets. These functions offer good localization in time and frequency and decompose a signal into distinct patterns of oriented harmonics, which are invariant to intensity and contrast range. Deformable-model segmentation is carried out on denoised data after thresholding of transform coefficients. This process attenuates speckle noise while preserving cardiac structure location. The superiority of 4-D over 3-D analysis for decorrelating additive white noise and multiplicative speckle noise on a 4-D phantom volume expanding in time is demonstrated. Quantitative validation, computed for contours and volumes, is performed on in vitro balloon phantoms. Clinical applications of this spaciotemporal analysis tool are reported for six patient cases providing measures of left ventricular volumes and ejection fraction. Index Terms—Echocardiography, LV volume, spaciotemporal analysis, speckle denoising. I

Directional representations of 4D echocardiography for temporal quantification of LV volume

Abstract. Real-time acquisition via four-dimensional (3D plus time) ultrasound obviates the need for slice registration and reconstruction, leaving segmentation as the only barrier to an automated, rapid, and clinically applicable calculation of accurate left ventricular cavity volumes and ejection fraction. Speckle noise corrupts ultrasound data by introducing sharp changes in an image intensity profile, while attenuation alters the intensity of equally significant cardiac structures, depending on orientation with respect to the position of the ultrasound beam. These properties suggest that measures based on phase information rather than intensity are appropriate for denoising and boundary (surface) detection. Our method relies on the expansion of temporal volume data on a family of basis functions called Brushlets. These basis functions decompose a signal into distinct patterns of oriented textures. Projected coefficients are associated with distinct “brush strokes ” of a particular size (width) and orientation (direction). Brushlet decompositions are invariant to intensity (contrast range) but depend on the spatial frequency content of a signal. Preliminary results of this directional spacefrequency analysis applied to both phantoms and clinical data are presented. The method will be used to clinically evaluate 4D data and to extract and quantify heart LV volumes.

ROC curve of hemoglobin content.

<p>A ROC curve was constructed to evaluate the diagnostic performance of hemoglobin content in differentiating between amyloid positive patients and amyloid negative patients. The AUC for hemoglobin content was 0.848 [95% CI: 0.661–1.000] (p = 0.021).</p