New imaging technologies have allowed us to see things at a new level of clarity and detail, or even see things that we were previously unable to visualize. However, a significant challenge faced by many new imaging technologies that limits widespread use for particular applications is long acquisition times. For example, despite the advantages associated with magnetic resonance imaging (MRI) for cancer screening such as higher tissue sensitivity and no exposure to ionizing radiation, the long acquisition times associated with MRI can signficantly limit the number of screenings that can be done as well as contribute to patient discomfort. Recent application-oriented developments in compressed sensing theory have shown that certain types of medical images are inherently sparse in particular transform domains, and as such can be reconstructed with a high level of accuracy from highly undersampled data below Nyquist sampling rates, which holds great potential for significantly reducing acquisition time.
Here in the VIP lab, researchers have been working on new sampling methods as well as reconstruction methods for improving compressed sensing performance for a variety of different applications in medical imaging and remote sensing. An example of a breast MRI reconstructed using one of the algorithms we have developed is shown below. As can be seen, the reconstructed MRI using the method we have developed (right) is sharp, high contrast, have minimal artifacts, provides good preservation of fine tissue details and variations, and does not exhibit staircase artifacts.
Kazemzadeh, F., and A. Wong, A System, Method and Apparatus for Ultra-resolved Ultra-wide Field-of-view Multispectral and Hyperspectral Holographic Microscopy, , vol. 62155416, USA, April 30, 2015.