Title:
HIGH-CONTRAST MINIMUM VARIANCE IMAGING METHOD BASED ON DEEP LEARNING
Document Type and Number:
WIPO Patent Application WO/2021/007989
Kind Code:
A1
Abstract:
Disclosed is a high-contrast minimum variance imaging method based on deep learning. In the method, the performance of a traditional minimum variance imaging method is improved by using deep learning technology. For the problem of the poor performance of a traditional minimum variance imaging method in terms of ultrasonic image contrast, a deep neural network is applied in order to suppress an off-axis scattering signal in channel data received by an ultrasonic transducer, and after the deep neural network is combined with a minimum variance beamforming method, an ultrasonic image with a higher contrast can be obtained while the resolution performance of the minimum variance imaging method is maintained. In the present method, compared with the traditional minimum variance imaging method, after an apodization weight is calculated, channel data is first processed by using a deep neural network, and weighted stacking of the channel data is then carried out, so that the pixel value of a target imaging point is obtained, thereby forming a complete ultrasonic image. The minimum variance imaging method combined with a deep neural network can improve the image contrast performance of a traditional minimum variance imaging method.
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Inventors:
CHEN JUNYING (CN)
ZHUANG RENXIN (CN)
ZHUANG RENXIN (CN)
Application Number:
PCT/CN2019/113196
Publication Date:
January 21, 2021
Filing Date:
October 25, 2019
Export Citation:
Assignee:
UNIV SOUTH CHINA TECH (CN)
International Classes:
A61B8/00; G01S15/89; G06N3/04
Domestic Patent References:
| WO2018127497A1 | 2018-07-12 |
Foreign References:
| CN105760892A | 2016-07-13 | |||
| CN109965905A | 2019-07-05 |
Other References:
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BEN LUIJTEN ET AL.: "Deep Learning for Fast Adaptive Beamforming", ICASSP 2019 - 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 17 May 2019 (2019-05-17), XP033566029, ISSN: 2379-190X, DOI: 20200412115839
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JOHAN FREDRIK SYNNEVAG ET AL.: "Adaptive Beamforming Applied to Medical Ultrasound Imaging", IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL, vol. 54, no. 8, 20 August 2007 (2007-08-20), XP011190325, DOI: 20200412103603A
BEN LUIJTEN ET AL.: "Deep Learning for Fast Adaptive Beamforming", ICASSP 2019 - 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 17 May 2019 (2019-05-17), XP033566029, ISSN: 2379-190X, DOI: 20200412115839
DONGWOON HYUN ET AL.: "Beamforming and Speckle Reduction Using Neural Networks", IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL, vol. 66,, no. 6, 30 May 2019 (2019-05-30), XP011721143, DOI: 20200412103746A
XU, MENGLING: "Research on Adaptive Beamforming Methods in Medical Ultrasound Imaging", CHINA DOCTORAL DISSERTATIONS FULL-TEXT DATABASE, MEDICINE AND HEALTH SCIENCES, no. 7, 15 July 2015 (2015-07-15), DOI: 20200412103908A
Attorney, Agent or Firm:
YOGO PATENT & TRADEMARK AGENCY LIMITED COMPANY (CN)
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