# WaveFlow - Towards Integration of Ultrasound Processing with Deep Learning

arXiv: https://arxiv.org/abs/1811.01566 · HTML: https://mlsanity.com/paper/1811.01566/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2018.

## Built on by (most cited first, 1 of 1)

- [Reconstruction of Optical Coherence Tomography Images from Wavelength-space Using Deep-learning](https://mlsanity.com/paper/2509.18783/) (2025)

## Abstract (arXiv)

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), plane-wave 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 real-time ultrasound image reconstruction using WaveFlow operators in the TensorFlow framework. WaveFlow source code can be found at github.com/waveflow-team/waveflow