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Daniel S. Weller

Assistant Professor

Contact
Rice Hall 309
351 McCormick Road
PO Box 400743
Charlottesville, VA 22904-1000

Phone: (434) 924-4271
FAX: (434) 924-8818
Email: dsw8c@virginia.edu

Home Page: http://people.virginia.edu/~dsw8c/
Lab Home Page: http://www.ece.virginia.edu/vital/


Summary:
Daniel Weller's research focuses on combining mathematical modeling tools, signal processing theory, and estimation techniques into novel image processing and reconstruction methods, for applications like magnetic resonance imaging (MRI). His lab will devise new reconstruction, post-processing, and analysis methods for dynamic/time-series imaging problems. These methods will combine new models and optimization algorithms to enable faster, more robust imaging, with fewer artifacts, enhanced resolution, and other application-specific benefits. These new methods will work with the next generation of hardware and magnetic resonance imaging sequences. At the same time, his group will investigate how these solutions can be generalized to address similar signal processing problems found in other applications, beyond MRI.

Background:
Daniel Weller received his Ph.D. and S.M. in electrical engineering from the Massachusetts Institute of Technology, in June 2012 and June 2008, respectively. Previously, he received his B.S. in electrical and computer engineering, with honors, from Carnegie Mellon University, in May 2006. He spent two years at the University of Michigan (Ann Arbor) as a post-doctoral research fellow, supported by the National Institutes of Health via a Ruth L. Kirschstein National Research Service Award (F32). As a graduate student, he was a finalist in the Student Paper Competition at the 2011 IEEE International Symposium on Biomedical Imaging. He is a member of IEEE (including the Signal Processing and Engineering in Medicine & Biology societies), of the International Society for Magnetic Resonance in Medicine (ISMRM), and of both Tau Beta Pi and Eta Kappa Nu.

Research Interests

  • Medical Imaging, especially Magnetic Resonance Imaging (MRI)
  • Signal Processing and Estimation
  • Non-Ideal Sampling and Reconstruction

Most Recent Publications:

  • Daniel S. Weller, Sathish Ramani, Jon-Fredrik Nielsen, and Jeffrey A. Fessler. "Monte Carlo SURE-Based Parameter Selection for Parallel Magnetic Resonance Imaging Reconstruction." Magn. Reson. Med., vol. 71, no. 5, pp. 1760-1770, May 2014.
  • Daniel S. Weller, Sathish Ramani, and Jeffrey A. Fessler. "Augmented Lagrangian with Variable Splitting for Faster Non-Cartesian L1-SPIRiT MR Image Reconstruction." IEEE Trans. Med. Imaging, vol. 33, no. 2, pp. 351-361, February 2014.
  • Sathish Ramani, Daniel S. Weller, Jon-Fredrik Nielsen, and Jeffrey A. Fessler. "Non-Cartesian MRI Reconstruction With Automatic Regularization Via Monte-Carlo SURE." IEEE Trans. Med. Imag., vol. 32, no. 8, pp. 1411-1422, August 2013.
  • Daniel S. Weller, Jonathan R. Polimeni, Leo Grady, Lawrence L. Wald, Elfar Adalsteinsson, and Vivek K Goyal. "Sparsity-Promoting Calibration for GRAPPA Accelerated Parallel MRI Reconstruction." IEEE Trans. Med. Imag., vol. 32, no. 7, pp. 1325-1335, July 2013.
  • Daniel S. Weller, Jonathan R. Polimeni, Leo Grady, Lawrence L. Wald, Elfar Adalsteinsson, and Vivek K Goyal. "Denoising Sparse Images from GRAPPA Using the Nullspace Method." Magn. Reson. Med., vol. 68, no. 4, pp. 1176-1189, Oct. 2012.
  • Daniel S. Weller and Vivek K Goyal. "Bayesian post-processing methods for jitter mitigation in sampling." IEEE Trans. Signal Process., vol. 59, no. 5, pp. 2112-2123, May 2011.
  • Daniel S. Weller and Vivek K Goyal. "On the estimation of nonrandom signal coefficients from jittered samples." IEEE Trans. Signal Process., vol. 59, no. 2, pp. 587-597, Feb. 2011.