Abstract
We present PredictUS, a novel Quantitative Ultrasound (QUS) parameter estimation technique with improved resolution and precision using augmented ultrasound data. The ultrasound data is generated using a sequence-to-sequence convolutional neural network based on WaveNet. The spectral-based QUS techniques are limited by the well-studied trade-off between the precision of the estimated QUS parameters and the window size used in estimation, limiting the practical utility of the QUS techniques. In this paper, we present a method to increase the window size by predicting the next data points of a given window. The method provides better estimates of local tissue properties with high resolution by virtually extending the property to a larger region. Our proof-of-concept study based on attenuation coefficient estimate (ACE), an important QUS parameter, attains a resolution reduction up to 50% while maintaining comparable estimation precision. This result shows the promise to extend the precision-resolution trade-off, which, in turn, would have implications in small lesion detection or heterogeneous tissue characterization.
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Deeba, F., Rohling, R. (2019). PredictUS: A Method to Extend the Resolution-Precision Trade-Off in Quantitative Ultrasound Image Reconstruction. In: Knoll, F., Maier, A., Rueckert, D., Ye, J. (eds) Machine Learning for Medical Image Reconstruction. MLMIR 2019. Lecture Notes in Computer Science(), vol 11905. Springer, Cham. https://doi.org/10.1007/978-3-030-33843-5_24
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DOI: https://doi.org/10.1007/978-3-030-33843-5_24
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