Deep Learning-Based Universal Beamformer for Ultrasound Imaging
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In ultrasound (US) imaging, individual channel RF measurements are back-propagated and accumulated to form an image after applying specific delays. While this time reversal is usually implemented using a hardware- or software-based delay-and-sum (DAS) beamformer, the performance of DAS decreases rapidly in situations where data acquisition is not ideal. Herein, for the first time, we demonstrate that a single data-driven adaptive beamformer designed as a deep neural network can generate high quality images robustly for various detector channel configurations and subsampling rates. The proposed deep beamformer is evaluated for two distinct acquisition schemes: focused ultrasound imaging and planewave imaging. Experimental results showed that the proposed deep beamformer exhibit significant performance gain for both focused and planar imaging schemes, in terms of contrast-to-noise ratio and structural similarity.
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Deep Learning-Based Universal Beamformer for Ultrasound Imaging
- 1. Deep Learning-based Universal Beamformer for Ultrasound Imaging Shujaat Khan, Jaeyoung Huh and Jong Chul Ye BISPL - BioImaging, Signal Processing, and Learning lab. Dept. Bio & Brain Engineering KAIST, Korea
- 2. Motivation Application Needs Reduce the number of channel - Ultra-fast US - Portable US - 3 dimensional US
- 3. Acquisition Modes Focused Imaging Planewave Imaging
- 4. Conventional approach Conventional delay-and-sum (DAS) Beamforming Pipeline Rx probe Beam focusing Signal Adder Envelope detection Log compression B-mode image Hilbert Transform IQ data (signal before envelope detection)
- 5. Full-sampling pattern Our contribution Beam focusing Envelope detection Log compression B-mode image IQ data (signal before envelope detection) Convolutional neural network (CNN) Universal Deep Beamformer (DeepBF) probe Rx Sub-sampling pattern 2x Sub-sampling pattern 4x
- 6. Review : Adaptive Beamforming It is difficult to calculate the inverse of covariance matrix R
- 7. Review : Deconvolution y : RF vector at the depth n H : Deconvolution filter matrices W : Beamformer weight U : [𝒚𝒚 𝒏𝒏 + 𝑸𝑸 𝑻𝑻 … 𝒚𝒚 𝒏𝒏 − 𝑸𝑸 𝑻𝑻 ] 𝑻𝑻 : Deconvolution filter Input dependent non-linear mapping Input dependent Point Spread Function (PSF)
- 8. CNN as a combinatorial representation Encoder-decoder CNN analysis basis synthesis basis Encoder Decoder Similarity between two equation implies that the deconvolution beamforming can be learned using an encoder-decoder CNN. <J. C. Ye and W. K. Sung, “Understanding geometry of encoder-decoder CNNs,” in Proceedings of the 36th International Conference on Machine Learning, >
- 9. Application to Planar Wave Imaging Proposed Method Data Generation Rx Depth Planewave1 Time of flight Planewave2 Planewave3 Planewave
- 10. Results (Planewave / in-vivo) <Full data> 31 plane waves and 64 channels 3 plane waves and 64 channels 31 plane waves and 32 channels 31 plane waves and 8 channels DAS DeepBF 255 128 0 Lateral length(mm) 0 40 30 20 10 depth(mm) 50 0 20 30 40 10
- 11. Results (Planewave / phantom) <Full data> 31 plane waves and 64 channels 11 plane waves and 64 channels 3 plane waves and 64 channels 31 plane waves and 32 channels 31 plane waves and 16 channels 31 plane waves and 8 channels 255 128 0 DAS DeepBF depth(mm) 60 0 24 36 48 12 Lateral length(mm) 0 40 30 20 10 Note that those images are generated from one network.
- 12. Application to Focused B-Mode Imaging … … … … Focusing Rx Depth Scanline Proposed Method Data Generation
- 13. Results (focused / in-vivo) <Full Data> 64 channels 32 channels 16 channels 8 channels 4 channels 0 -30 -60 1 0.5 0 depth(mm) 45 0 18 27 36 9 Lateral length(mm) 0 40 30 20 10 DAS BF 0 -30 -60 1 0.5 0 dB depth(mm) 45 0 18 27 36 9 Lateral length(mm) 0 40 30 20 10 DeepBF dB
- 14. Results (focused / phantom) 32 channels <Full Data> 64 channels 16 channels 4 channels depth(mm) 45 0 18 27 36 9 depth(mm) 45 0 18 27 36 9 Lateral length(mm) 0 40 30 20 10 Lateral length(mm) 0 40 30 20 10 0 -30 -60 1 0.5 0 0 -30 -60 1 0.5 0 dB dB DAS BF DeepBF
- 15. Conclusion • The proposed method exploits the redundancies in the raw RF data. • The proposed method can be applied to low-power US, hand-held systems, etc • The proposed universal DeepBF exhibits superior image quality for all sub-sampling rates.
- 16. Thank You