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.
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Related publications
Li E., Khalvati F., Shafiee M.J., Haider M.A., and Wong A, "Sparse reconstruction of compressive sensing MRI using cross-domain stochastically fully connected conditional random fields", BMC Medical Imaging, vol. 16, 2016. Get it here. Li E., Shafiee M.J., Kazemzadeh F., and Wong A, "Sparse Reconstruction of Compressive Sensing Multi-Spectral Data Using an Inter-Spectral Multi-Layered Conditional Random Field ", IEEE Access, vol. 4, pp. 5540-5554, 2016. Get it here. Schwartz S., Wong A., and Clausi D.A, "Optimized sampling distribution based on nonparametric learning for improved compressive sensing performance", Journal of Visual Communication and Image Representation, vol. 31, pp. 26-40, 2015. Get it here. Kazemzadeh F., Haider S.A., Scharfenberger C., Wong A., and Clausi D.A, "Multispectral stereoscopic imaging device: Simultaneous multiview imaging from the visible to the near-infrared", IEEE Transactions on Instrumentation and Measurement, vol. 63, pp. 1871-1873, 2014. Get it here. Schwartz S., Liu C., Wong A., Clausi D.A., Fieguth P., and Bizheva K, "Energy-guided learning approach to compressive FD-OCT", Optics Express, vol. 21, pp. 329-344, 2013. Get it here. Schwartz S., Wong A., and Clausi D.A, "Saliency-guided compressive sensing approach to efficient laser range measurement", Journal of Visual Communication and Image Representation, vol. 24, pp. 160-170, 2013. Get it here. Schwartz S., Wong A., and Clausi D.A, "Compressive fluorescence microscopy using saliency-guided sparse reconstruction ensemble fusion", Optics Express, vol. 20, pp. 17281-17296, 2012. Get it here.
Conference PapersLi E., Shafiee M.J., Kazemzadeh F., and Wong A, "Sparse reconstruction of compressed sensing multispectral data using a cross-spectral multilayered conditional random field mode", Proceedings of SPIE - The International Society for Optical Engineering, 2015. Get it here. Schwartz S., Wong A., and Clausi D.A, "Saliency-guided compressive fluorescence microscopy", Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2012. Get it here. Schwartz S., Wong A., and Clausi D.A, "Multi-scale saliency-guided compressive sensing approach to efficient robotic laser range measurements", Proceedings of the 2012 9th Conference on Computer and Robot Vision, CRV 2012, 2012. Get it here.
Patents
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.
Kazemzadeh, F., A. Wong, and S. Haider, Imaging System and Method for Concurrent Multiview Multispectral Polarimetric Light-field High Dynamic Range Imaging, , USA, 2014.