Daniel Weller

Daniel S. Weller, Ph.D.

Assistant Professor of Electrical and Computer Engineering

Ph.D., Electrical Engineering, MIT, 2012
S.M., Electrical Engineering, MIT, 2008
B.S., Electrical and Computer Engineering, Carnegie Mellon University, 2006

P.O. Box 400743
Rice Hall, Room 309
Charlottesville, VA 22904-4743

Telephone: 434-924-4271
Fax: 434-924-8818
email: dweller@virginia.edu

website: www.ece.virginia.edu/vital/

Research Interests

Increasing quality and resolution requirements in scientific and medical imaging continue to grow rapidly. Meanwhile, consumers demand smaller, longer-lasting imaging sensors for next generation smartphones and wearable devices, at a lower cost. Both trends drive the development of novel signal and image processing algorithms that are faster, more robust, and yield higher quality reconstructions than ever before. Mathematical models for images and measurements and computationally efficient reconstruction algorithms that can exploit these models, are essential to meet this need. Recent application areas of interest include parallel-receive MRI and head motion correction in functional MRI. Exciting research topics in these areas include sparse, structured, and parametric signal models, automatic tuning/regularization parameter selection, iterative algorithms for inverse problems, and synergies between acquisition and reconstruction in image formation. Although solutions are created to address a specific need, the emphasis of the research is on ideas and techniques that can be generalized to address similar signal processing problems found in a variety of other applications.

Regularized iterative methods, such as those my group develops, are revolutionizing the capabilities of image formation and reconstruction in many applications. Many of these reconstructions depend on one or more parameters that can greatly impact the quality of the reproduced images. Unfortunately, selecting appropriate values for these parameters is often nontrivial and can vary from acquisition to acquisition. Automatic parameter selection techniques promise to simplify this process, but parameter tuning still necessitates running many reconstructions in order to evaluate them and settle on the best one. We are investigating reducing the total computational cost of parameter selection to make it practical, via identifying poor parameter choices early in the reconstruction process and using lower-dimensional surrogate problems explicitly for parameter tuning.


1. D.S. Weller, S. Ramani, J.-F. Nielsen, and J.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. DOI: 10.1002/mrm.24840 PMCID: PMC3858446

2. D.S. Weller, S. Ramani, and J.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. DOI: 10.1109/TMI.2013.2285046 PMCID: PMC3981959

3. Ramani, Sathish, Weller, Daniel S., et al. "Non-Cartesian MRI Reconstruction With Automatic Regularization Via Monte-Carlo SURE." IEEE Trans. Med. Imaging, vol. 32, no. 8, pp. 1411-1422, August 2013. DOI: 10.1109/TMI.2013.2257829 PMCID: PMC3735835

4. D.S. Weller, J.R. Polimeni, L. Grady, L.L. Wald, E. Adalsteinsson, and V.K Goyal. “Sparsity-Promoting Calibration for GRAPPA Accelerated Parallel MRI Reconstruction.” IEEE Trans. Med. Imaging, vol. 32, no. 7, pp. 1325-1335, July 2013. DOI: 10.1109/TMI.2013.2256923 PMCID: PMC3696426

5. D.S. Weller, J.R. Polimeni, L. Grady, L.L. Wald, E. Adalsteinsson, and V.K Goyal. “Denoising Sparse Images from GRAPPA Using the Nullspace Method.” Magn. Reson. Med., vol. 68, no. 4, pp. 1176-1189, October 2012. DOI: 10.1002/mrm.24116 PMCID: PMC3323741

6. D.S. Weller and V.K Goyal. “Bayesian post-processing methods for jitter mitigation in sampling.” IEEE Trans. Signal Processing, vol. 59, no. 5, pp. 2112-2123, May 2011. DOI: 10.1109/TSP.2011.2108289

7. D.S. Weller and V.K Goyal. “On the estimation of nonrandom signal coefficients from jittered samples.” IEEE Trans. Signal Processing, vol. 59, no. 2, pp. 587-597, February 2011. DOI: 10.1109/TSP.2010.2090347

Conference papers, software, and other publications may be found on my website.