Predicting Generalized Contrast-to-Noise Ratios in Frame-Averaged Photoacoustic Images
Our related journal article presents a theoretical framework to relate the performance of photoacoustic-based computer vision systems to the parameters of the underlying photoacoustic imaging systems. However, software-based adjustments were not previously included. In this paper, we extend the prev...
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Published in | 2022 IEEE International Ultrasonics Symposium (IUS) pp. 1 - 4 |
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Main Authors | , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
10.10.2022
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Subjects | |
Online Access | Get full text |
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Summary: | Our related journal article presents a theoretical framework to relate the performance of photoacoustic-based computer vision systems to the parameters of the underlying photoacoustic imaging systems. However, software-based adjustments were not previously included. In this paper, we extend the previous framework to include software-based noise reduction algorithms (e.g., frame averaging). We derive expressions for generalized contrast-to-noise ratio (gCNR) predictions in frame-averaged photoacoustic images. These gCNR predictions are validated against histogram-based gCNR measurements, with mean absolute errors of 4.4×10 −3 and 7.7×10 −3 in the phantom and ex vivo environments, respectively. This extension reduced the mean absolute prediction error by 7.74% and 27.15% when compared to predictions with the previous photoacoustic framework applied to frame-averaged photoacoustic images from phantom and ex vivo data, respectively. Results demonstrate that our extended framework more accurately predicts target detectability in frame-averaged photoacoustic images, which is beneficial for photoacoustic-based computer vision systems. |
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ISSN: | 1948-5727 |
DOI: | 10.1109/IUS54386.2022.9958666 |