Real-Time Echocardiography Guidance for Optimized Apical Standard Views

A Very Large Cardiac Channel Data Database (VLCD) Used to Evaluate Global Image Coherence (GIC) as an In Vivo Image Quality Metric

Ultrasound image quality is of utmost importance for a clinician to reach a correct diagnosis. Conventionally, image quality is evaluated using metrics to determine the contrast and resolution. These metrics require localization of specific regions and targets in the image such as a region of interest (ROI), a background region, and/or a point scatterer. Such objects can all be difficult to identify in in-vivo images, especially for automatic evaluation of image quality in large amounts of data. Using a matrix array probe, we have recorded a Very Large cardiac Channel data Database (VLCD) to evaluate coherence as an in vivo image quality metric. The VLCD consists of 33280 individual image frames from 538 recordings of 106 patients. We also introduce a global image coherence (GIC), an in vivo image quality metric that does not require any identified ROI since it is defined as an average coherence value calculated from all the data pixels used to form the image, below a preselected range. The GIC is shown to be a quantitative metric for in vivo image quality when applied to the VLCD. We demonstrate, on a subset of the dataset, that the GIC correlates well with the conventional metrics contrast ratio (CR) and the generalized contrast-to-noise ratio (gCNR) with R = 0.74 ( ) and R = 0.62 ( ), respectively. There exist multiple methods to estimate the coherence of the received signal across the ultrasound array. We further show that all coherence measures investigated in this study are highly correlated ( 0.9 and ) when applied to the VLCD. Thus, even though there are differences in the implementation of coherence measures, all quantify the similarity of the signal across the array and can be averaged into a GIC to evaluate image quality automatically and quantitatively.

Clinical Influence of Handheld Ultrasound, Supported by Automatic Quantification and Telemedicine, in Suspected Heart Failure

Early and correct heart failure (HF) diagnosis is essential to improvement of patient care. We aimed to evaluate the clinical influence of handheld ultrasound device (HUD) examinations by general practitioners (GPs) in patients with suspected HF with or without the use of automatic measurement of left ventricular (LV) ejection fraction (autoEF), mitral annular plane systolic excursion (autoMAPSE) and telemedical support. Five GPs with limited ultrasound experience examined 166 patients with suspected HF (median interquartile range = 70 (63-78) y; mean ± SD EF = 53 ± 10%). They first performed a clinical examination. Second, they added an examination with HUD, automatic quantification tools and, finally, telemedical support by an external cardiologist. At all stages, the GPs considered whether the patients had HF. The final diagnosis was made by one of five cardiologists using medical history and clinical evaluation including a standard echocardiography. Compared with the cardiologists' decision, the GPs correctly classified 54% by clinical evaluation. The proportion increased to 71% after adding HUDs, and to 74 % after telemedical evaluation. Net reclassification improvement was highest for HUD with telemedicine. There was no significant benefit of the automatic tools (p ≥ 0.58). Addition of HUD and telemedicine improved the GPs' diagnostic precision in suspected HF. Automatic LV quantification added no benefit. Refined algorithms and more training may be needed before inexperienced users benefit from automatic quantification of cardiac function by HUDs.