WaveFlow-Towards Integration of Ultrasound Processing with Deep Learning

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Abstract

The ultimate goal of this work is a real-time processing framework for ultrasound image reconstruction augmented with machine learning. To attain this, we have implemented WaveFlow-a set of ultrasound data acquisition and processing tools for TensorFlow. WaveFlow includes: ultrasound Environments (connection points between the input raw ultrasound data source and TensorFlow) and signal processing Operators (ops) library. Raw data can be processed in real-time using algorithms available both in TensorFlow and WaveFlow. Currently, WaveFlow provides ops for B-mode image reconstruction (beamforming), signal processing and quantitative ultrasound. The ops were implemented both for the CPU and GPU, as well as for built-in automated tests and benchmarks. To demonstrate WaveFlow's performance, ultrasound data were acquired from wire and cyst phantoms and elaborated using selected sequences of the ops. We implemented and evaluated: Delay-and-Sum beamformer, synthetic transmit aperture imaging (STAI), planewave imaging (PWI), envelope detection algorithm and dynamic range clipping. The benchmarks were executed on the NVidia@ Titan X GPU integrated in the USPlatform research scanner (us4us Ltd., Poland). We achieved B-mode image reconstruction frame rates of 55 fps, 17 fps for the STAI and the PWI algorithms, respectively. The results showed the feasibility of realtime ultrasound image reconstruction using WaveFlow operators in the TensorFlow framework. WaveFlow source code can be found at !!ithub.com/waveflow-teamlwaveflow.

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