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

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posted on 2022-12-07, 04:12 authored by Ole Marius Hoel RindalOle Marius Hoel Rindal, Tore Grüner Bjåstad, Torvald Espeland, Erik Andreas Rye Berg, Svein-Erik MåsøySvein-Erik Måsøy

The ultrasound image quality is of utmost importance for a clinician to reach the correct diagnosis. Conventionally, image quality is evaluated using metrics to evaluate the contrast and resolution. These metrics requires localization of specific regions and targets in the image such as a region of interest (ROI), a background region and or a point scatter. 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 33 280 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. The GIC is shown to be a quantitative metric for in-vivo image quality when applied to the VLCD. 

There exists multiple methods to estimate the coherence of the received signal across the ultrasound array. We further demonstrate that all coherence measures investigated in this study is highly correlated (R>0.9, p<0.001) when applied to the VLCD. Thus, even though there are differences in the implementation of coherence measures, they all measure the similarity of the signal across the array and can be averaged into a GIC to evaluate the image quality automatically and quantitatively.


Center for Innovative Ultrasound Solutions (CIUS) and the Research Council of Norway under Project 237887


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University of Oslo

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  • Norway

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