Ultrasound tomography with learned dictionaries

We propose a new method for imaging sound speed in breast tissue from measurements obtained by ultrasound tomography (UST) scanners. Given the measurements, our algorithm finds a sparse image representation in an overcomplete dictionary that is adapted to the properties of UST images. This dictionary is learned from high resolution MRI breast scans using an unsupervised maximum likelihood dictionary learning method. The proposed dictionary-based regularization method significantly improves the quality of reconstructed breast UST images. It outperforms the wavelet-based reconstruction and the least squares minimization with lowpass constraints, 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 conditions.