We propose a new method for reconstruction of breast images from measurements obtained by ultrasound tomography (UT) scanners. Our solution for this inverse problem is based on sparse image representation in an overcomplete dictionary that is adapted to the properties of UT images. This dictionary is learned from high resolution MRI breast scans using an unsupervised dictionary learning method described in Ref. [1]. The proposed dictionary-based regularization method significantly improves the quality of reconstructed breast UT images. It outperforms the wavelet-based reconstruction and the l2+lowpassminimization algorithm, on both numerical and in vivo data. Our results demonstrate that the use of the learned dictionary improves the image accuracy for up to 4 dB with the exact measurement matrix and for 3.5 dB with the estimated measurement matrix over the wavelet-based reconstruction under the same setup.
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