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231225s2017 xx |||||o 00| ||eng c |
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|a 10.1109/TUFFC.2017.2736890
|2 doi
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|a pubmed24n0915.xml
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|a eng
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| 100 |
1 |
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|a Gasse, Maxime
|e verfasserin
|4 aut
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| 245 |
1 |
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|a High-Quality Plane Wave Compounding Using Convolutional Neural Networks
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|c 2017
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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| 500 |
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|a Date Completed 26.11.2018
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|a Date Revised 26.11.2018
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Single plane wave (PW) imaging produces ultrasound images of poor quality at high frame rates (ultrafast). High-quality PW imaging usually relies on the coherent compounding of several successive steered emissions (typically more than ten), which in turn results in a decreased frame rate. We propose a new strategy to reduce the number of emitted PWs by learning a compounding operation from data, i.e., by training a convolutional neural network to reconstruct high-quality images using a small number of transmissions. We present experimental evidence that this approach is promising, as we were able to produce high-quality images from only three PWs, competing in terms of contrast ratio and lateral resolution with the standard compounding of 31 PWs ( 10× speedup factor)
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| 650 |
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|a Journal Article
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| 650 |
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|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Millioz, Fabien
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Roux, Emmanuel
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Garcia, Damien
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liebgott, Herve
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Friboulet, Denis
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on ultrasonics, ferroelectrics, and frequency control
|d 1986
|g 64(2017), 10 vom: 09. Okt., Seite 1637-1639
|w (DE-627)NLM098181017
|x 1525-8955
|7 nnns
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| 773 |
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|g volume:64
|g year:2017
|g number:10
|g day:09
|g month:10
|g pages:1637-1639
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|u http://dx.doi.org/10.1109/TUFFC.2017.2736890
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