Introduction: Ultrasound is an advanced technology in the field of interventional pain screening. Quality control is indispensable to ensure the quality of the images and the accuracy of the device. In diagnostic ultrasound, various parameters can be measured and controlled, such as signal-to-noise ratio (SNR), used to determine the sensitivity of the imaging system and contrast-to-noise ratio (CNR), which is used to evaluate the contrast decay and is an estimate of the noise present in the image. Purpose: The purpose of this master thesis is to develop an application that can be used to measure the signal-to-noise ratio in ultrasound images. In the second part of the thesis, we will use the application to test how the SNR and CNR parameters change at different measurement depths, different gain and different frequencies. Methods: In the process of making this master thesis, descriptive and experimental methods were used. The application was designed in the Python programming language, version 3.6, using Visual Studio Code. Results: For each of the three phantom types, we took several images with the two probes. With all four Wilcoxon signed-rank tests performed, we found that the measurements of our application are comparable to those of FIJI. The second part of the experiment was divided into three parts, namely: 1. the influence of the frequency on SNR and CNR, 2. the influence of the gain on SNR and CNR, and 3. the influence of the measurement depth on SNR and CNR. Discussion and Conclusion: Comparing the developed application with the well-established FIJI ImageJ medical image analysis software, we found that there are no statistically significant deviations in the calculation of SNR and CNR parameters. We have shown experimentally that when performing quality control, we need to calculate the contrast-to-noise parameter in addition to the signal-to-noise ratio, because even if an image has an excellent SNR, it may still be diagnostically unusable if two adjacent structures in the image are not distinguishable from each other due to poor contrast.
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