Efficient B-Mode Ultrasound Image Reconstruction From Sub-Sampled RF Data Using Deep Learning
In portable, 3-D, and ultra-fast ultrasound imaging systems, there is an increasing demand for the reconstruction of high-quality images from a limited number of radio-frequency (RF) measurements due to receiver (Rx) or transmit (Xmit) event sub-sampling. However, due to the presence of side lobe ar...
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| Veröffentlicht in: | IEEE transactions on medical imaging 2019-02, Vol.38 (2), p.325-336 |
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| creator | Yoon, Yeo Hun Khan, Shujaat Huh, Jaeyoung Ye, Jong Chul |
| description | In portable, 3-D, and ultra-fast ultrasound imaging systems, there is an increasing demand for the reconstruction of high-quality images from a limited number of radio-frequency (RF) measurements due to receiver (Rx) or transmit (Xmit) event sub-sampling. However, due to the presence of side lobe artifacts from RF sub-sampling, the standard beamformer often produces blurry images with less contrast, which are unsuitable for diagnostic purposes. Existing compressed sensing approaches often require either hardware changes or computationally expensive algorithms, but their quality improvements are limited. To address this problem, in this paper, we propose a novel deep learning approach that directly interpolates the missing RF data by utilizing redundancy in the Rx-Xmit plane. Our extensive experimental results using sub-sampled RF data from a multi-line acquisition B-mode system confirm that the proposed method can effectively reduce the data rate without sacrificing the image quality. |
| doi_str_mv | 10.1109/TMI.2018.2864821 |
| format | Article |
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However, due to the presence of side lobe artifacts from RF sub-sampling, the standard beamformer often produces blurry images with less contrast, which are unsuitable for diagnostic purposes. Existing compressed sensing approaches often require either hardware changes or computationally expensive algorithms, but their quality improvements are limited. To address this problem, in this paper, we propose a novel deep learning approach that directly interpolates the missing RF data by utilizing redundancy in the Rx-Xmit plane. 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| subjects | Abdomen - diagnostic imaging Algorithms B-mode Carotid Arteries - diagnostic imaging Deep Learning Diagnostic systems Hankel matrix Humans Image contrast Image processing Image Processing, Computer-Assisted - methods Image quality Image reconstruction Imaging Machine learning multi-line acquisition Neural networks Radio frequency Redundancy Sampling Sidelobes Supply chains Ultrasonic imaging Ultrasonography - methods Ultrasound Ultrasound imaging |
| title | Efficient B-Mode Ultrasound Image Reconstruction From Sub-Sampled RF Data Using Deep Learning |
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