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Efficient B-mode Ultrasound Image Reconstruction Using CNN

version 1.0.1 (54.3 MB) by Shujaat Khan
Efficient B-mode Ultrasound Image Reconstruction from Sub-sampled RF Data using Deep Learning

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Updated 26 Nov 2018

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Paper
Yoon, Yeo Hun, Shujaat Khan, Jaeyoung Huh, and Jong Chul Ye. "Efficient B-mode Ultrasound Image Reconstruction from Sub-sampled RF Data using Deep Learning." IEEE transactions on medical imaging (2018).
Implementation
MatConvNet (matconvnet-1.0-beta24)
Please run the matconvnet-1.0-beta24/matlab/vl_compilenn.m file to compile matconvnet.
There is instruction on "http://www.vlfeat.org/matconvnet/mfiles/vl_compilenn/"
Please run the installation setup (install.m) and run some training examples.
Trained network
Trained network for 'SC2xRX4 (down-sampling) CNN' is uploaded.
Test data
Test data file is placed in 'data\cnn_sparse_view_init_multi_normal_dsr2_input64' folder.
The dimension of data are as follows -- Test_data = 64x384x1x2304 (channel x scanline x frame x depth)
To perform a test using proposed algorithm

-> Use 'DNN4x1_TestVal' as input data

-> Run 'MAIN_RECONSTRUCTION.m

-> You will get the reconstructed RF data in the 'data\cnn_sparse_view_init_multi_normal_dsr2_input64' directory.

-> Using standard delay-and-sum (DAS) beam-forming code construct a B-mode image. For our experiments we used a DAS beam-forming code provided by (Alpinion Co., Korea). A similar code can be downloaded from ('http://www.ultrasoundtoolbox.com/').

Cite As

Yoon, Yeo Hun, et al. “Efficient B-Mode Ultrasound Image Reconstruction from Sub-Sampled {RF} Data Using Deep Learning.” {IEEE} Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers ({IEEE}), 2018, pp. 1–1, doi:10.1109/tmi.2018.2864821.

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Updates

1.0.1

- citation information update

MATLAB Release Compatibility
Created with R2018b
Compatible with any release
Platform Compatibility
Windows macOS Linux