We consider the scenario of finding the transfer function of an aberrating layer in front of a receiving ultrasound (US) array, assuming a separate non-aberrated transmit source. We propose a method for blindly estimating this transfer function without exact knowledge of the ultrasound sources or acoustic contrast image, and without directly measuring the transfer function using a separate controlled calibration experiment. Instead, the measurement data of many unknown random images is collected, such as from blood flow, and its second-order statistics are exploited. A measurement model is formulated that explicitly defines the layer's transfer function. A covariance domain problem is then defined to eliminate the image variable, and it is solved for the layer's transfer function using manifold-based optimization. The proposed approach and calibration algorithm are evaluated on a range of challenging and realistic simulations using the k-Wave toolbox. Our results show that, given a sufficiently efficient parameterization of the layer's transfer function, and by jointly estimating the transfer function at multiple frequencies, the proposed algorithm is able to obtain an accurate estimate. Subsequent simulated imaging experiments using the obtained transfer function also show increased imaging performance in various aberrating layers, including a skull layer.

Ultrasound imaging of the vasculature has major significance for the detection of cardiovascular diseases and cancer. However, limited spatial resolution or long acquisition times of existing techniques limit the visualization of the microvascular structures. Enforcing sparsity in the underlying vasculature as well as exploiting statistical independence between voxels have become prominent for fast super-resolution imaging. However, such a statistical independence may not be valid for all voxels and may hence lead to a distorted signal model. Here we present an image reconstruction method that exploits the sparsity of the vasculature data without distorting the original signal model. We employ a multiple measurement vector (MMV) model to enforce the joint sparsity over the images at different time instants. To reduce the computational complexity of obtaining the solution, the ℓ1-SVD method is applied to the MMV model. We demonstrate that our method improves spatial resolution and provides a clear separation between blood vessels. Although our method is slightly slower than existing approaches, it outperforms them in terms of image reconstruction quality.