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Enhancing the shear-wave axial-velocity estimation using a convolutional neural network
Xufei Chen, Nishith Chennakeshava, Rogier Wildeboer, Ruud Van Sloun, Massimo Mischi
Session: Poster session 2 (Odd numbers)
Session starts: Friday 27 January, 10:00
Presentation starts: 10:00
Xufei Chen (Eindhoven University of Technology)
Nishith Chennakeshava (Eindhoven University of Technology)
Rogier Wildeboer (Philips Research Eindhoven)
Ruud Van Sloun (Eindhoven University of Technology)
Massimo Mischi (Eindhoven University of Technology)
Abstract:
The Signal-to-Noise Ratio (SNR) of the particle motion can directly impact the accuracy of the downstream shear wave (SW) elastography estimates. This SNR decreases when the push pulse energy is lower. However, minimizing the push energy is of clinical relevance, as lower mechanical and thermal indices ensure clinical safety. Here, we propose a 3-D multi-resolution convolutional neural network (MRCNN) to perform improved particle velocity Vz estimation, to minimize the push energy while preserving high SNR. We present a novel approach to generate training data from real acquisitions, providing high SNR targets, one-to-one paired to inputs that are corrupted with real-world noise and disturbances.