Multiple Measurement Vector Model for Sparsity-Based Vascular Ultrasound Imaging

Abstract

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.

Four-dimensional computational ultrasound imaging of brain hemodynamics

“…Here, we have shown that with a simple, quick, reconstruction method, we are still able to form high-resolution 3D images of the vasculature. In the future, there is scope to apply more advanced reconstruction methods that exploit statistical independence of the signals in neighboring voxels (62,63).…”