Compressed Sensing

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.

Comparison of breast MRI with reconstructed breast MRI

Related people

Directors

David A. Clausi

Students

Farnoud Kazemzadeh, Edward Li, Bi Hongbo

Alumni

Shimon Schwartz

Related publications

Journal articles

Schwartz, S.A. Wong, and D. A. Clausi, "Optimized sampling distribution based on nonparametric learning for improved compressive sensing performance", Journal of Visual Communication and Image Representation, vol. 31, pp. 26-40, May, 2015. Details

Kazemzadeh, F.S. HaiderC. ScharfenbergerA. Wong, and D. A. Clausi, "Multispectral Stereoscopic Imaging Device: Simultaneous Multiview Imaging from the Visible to the Near-Infraredpdf", IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, vol. 63, issue 7: IEEE Instrumentation and Measurement Society, pp. 1871-1873, July 2014. Details

Schwartz, S.C. LiuA. WongD. A. ClausiP. Fieguth, and K. Bizheva, "Energy-guided learning approach to compressive FD-OCT", Optics Express, vol. 21, issue 1, pp. 329-344, 2013. Details

Schwartz, S.A. Wong, and D. A. Clausi, "Saliency-guided compressive sensing approach to efficient laser range measurement", Journal of Visual Communication and Image Representation, vol. 24, no. 2, pp. 160-170, March, 2012. Details

Schwartz, S.A. Wong, and D. A. Clausi, "Compressive fluorescence microscopy using saliency-guided sparse reconstruction ensemble fusion", Optics Express, vol. 20, issue 16, pp. 17281–17296, July, 2012. Details

Conference papers

Li, E., M. J. Shafiee, F. Kazemzadeh, and A. Wong, "Sparse Reconstruction of Compressed Sensing Multi-spectral Data using Cross-Spectral Multi-layered Conditional Random Field Model", SPIE optics and photonics 2015, San Diego, SPIE , May, Accepted. Details

Li, E.M. J. ShafieeA. Chung, F. Khalvati, A. Wong, and M. A. Haider, "Enhanced Reconstruction of Compressive Sensing MRI via Cross-Domain Stochastically Fully-Connected Random Field Modelpdf", International Society for Magnetic Resonance in Medicine, February, 2014. Details


Kazemzadeh, F.M. J. Shafiee, A. Wong, and D. A. Clausi, "Reconstruction of Compressive Multispectral Sensing Data Using a Multi-layered Conditional Random Field Approach", SPIE: Optics + Photonics, vol. 9217, San diego, USA, SPIE Proceedings, 2014. Details

Schwartz, S.A. Wong, and D. A. Clausi, "Multi-scale saliency-guided compressive sensing approach to efficient robotic laser range measurements", 2012 Ninth Conference on Computer and Robot Vision (CRV), pp. 1-8, May, 2012.Details
 
Schwartz, S.A. Wong, and D. A. Clausi, "Saliency-guided compressive fluorescence microscopy", 34th Annual International Conference of the IEEE Engineering in Medicine and Biology, , San Diego, USA., pp. 4365 - 4368 , 2012. Details

Liu, C.A. Wong, K. Bizheva, P. Fieguth, and H. Bie, "Non-local sparse reconstruction of OCT images", SPIE Photonics West (BiOS), 2012. Details


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. Details

Kazemzadeh, F., A. Wong, and S. Haider, Imaging System and Method for Concurrent Multiview Multispectral Polarimetric Light-field High Dynamic Range Imaging, USA, 2014. Details