WaveFlow - Towards Integration of Ultrasound Processing with Deep Learning Article Swipe
Piotr Jarosik
,
Michał Byra
,
Marcin Lewandowski
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1811.01566
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1811.01566
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
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://arxiv.org/pdf/1811.01566
- OA Status
- green
- References
- 4
- Related Works
- 20
- OpenAlex ID
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https://openalex.org/W2950113670Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1811.01566Digital Object Identifier
- Title
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WaveFlow - Towards Integration of Ultrasound Processing with Deep LearningWork title
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preprintOpenAlex work type
- Language
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enPrimary language
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2018Year of publication
- Publication date
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2018-11-05Full publication date if available
- Authors
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Piotr Jarosik, Michał Byra, Marcin LewandowskiList of authors in order
- Landing page
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https://arxiv.org/pdf/1811.01566Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/1811.01566Direct OA link when available
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Computer science, Artificial intelligence, Beamforming, Frame rate, Computer vision, Scanner, Synthetic aperture radar, Image processing, Clipping (morphology), Image (mathematics), Telecommunications, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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20Other works algorithmically related by OpenAlex
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| abstract_inverted_index.real-time | 8, 65, 194 |
| abstract_inverted_index.sequences | 124 |
| abstract_inverted_index.synthetic | 134 |
| abstract_inverted_index.Currently, | 74 |
| abstract_inverted_index.TensorFlow | 71, 203 |
| abstract_inverted_index.USPlatform | 162 |
| abstract_inverted_index.WaveFlow's | 109 |
| abstract_inverted_index.algorithms | 67 |
| abstract_inverted_index.benchmarks | 150 |
| abstract_inverted_index.elaborated | 121 |
| abstract_inverted_index.evaluated: | 131 |
| abstract_inverted_index.framework. | 204 |
| abstract_inverted_index.integrated | 159 |
| abstract_inverted_index.plane-wave | 139 |
| abstract_inverted_index.processing | 9, 34, 55, 84 |
| abstract_inverted_index.ultrasound | 12, 30, 40, 48, 111, 195 |
| abstract_inverted_index.(connection | 42 |
| abstract_inverted_index.TensorFlow) | 52 |
| abstract_inverted_index.TensorFlow. | 37 |
| abstract_inverted_index.acquisition | 32 |
| abstract_inverted_index.algorithms, | 186 |
| abstract_inverted_index.beamformer, | 133 |
| abstract_inverted_index.benchmarks. | 106 |
| abstract_inverted_index.demonstrate | 108 |
| abstract_inverted_index.feasibility | 192 |
| abstract_inverted_index.implemented | 24, 91, 129 |
| abstract_inverted_index.ultrasound. | 87 |
| abstract_inverted_index.Environments | 41 |
| abstract_inverted_index.performance, | 110 |
| abstract_inverted_index.quantitative | 86 |
| abstract_inverted_index.Delay-and-Sum | 132 |
| abstract_inverted_index.respectively. | 187 |
| abstract_inverted_index.(beamforming), | 82 |
| abstract_inverted_index.reconstruction | 14, 81, 172, 197 |
| abstract_inverted_index.github.com/waveflow-team/waveflow | 212 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.44999998807907104 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |