Daniel Weller Head ShotDaniel S. Weller, PhD

Overview

Conventional Reconstruction
conventional heart image recon
PSER = 29.3 dB, MSSIM = 0.864
Content-Aware Reconstruction
content-aware heart image recon
PSER = 29.5 dB, MSSIM = 0.876
By automatically tracking image content, the content-aware reconstruction of magnetic resonance images from highly accelerated cardiac imaging data reduces distortions in reconstructed image structures. These methods will facilitate faster and more robust imaging of higher resolution images for earlier diagnosis of cardiovascular and other disease. More info

My 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) and light microscopy. For example, I recently integrated a robust means for tracking image content during image reconstruction, so that MRI reconstruction algorithms can suppress noise and artifacts in image backgrounds more effectively while preserving important image structures. Ongoing imaging research is organized into multiple related thrusts:

More information about current and future research can be found on the Virginia Imaging Theory and Algorithms Laboratory (VITAL) projects web site.

Image Reconstruction Methods

deep learning reconstruction of cardiac cine video (slice 8)deep learning reconstruction of cardiac cine video (slice 10)deep learning reconstruction of cardiac cine video (slice 12)
Data-driven reconstructions enabled by recent advances in deep learning permit high quality images to be rapidly obtained from highly undersampled imaging data. The reconstructions shown are nearly indistinguishable from fully sampled ground-truth data.

Advanced reconstructions including physical and/or data-driven models can produce high quality images from incomplete or noisy data. Effective use of these reconstructions introduce a number of new challenges, however, including dealing with variable image quality in training data, measuring image content complexity or image quality automatically, and effective reconstruction in the presence of motion or other perturbations. Collaborating with physicians including Dr. Christopher Kramer and Dr. Michael Salerno, this research will lead to faster and more robust methods for noninvasive cardiac imaging, e.g., for heart failure patients. This research is supported by the Thomas F.  and Kate Miller Jeffress Memorial Trust, Bank of America, Trustee, and the UVA Center for Engineering in Medicine.

Selected Publications:

  1. Weller, Daniel S., Salerno, Michael, and Meyer, Craig H. "Content-Aware Compressive Magnetic Resonance Image Reconstruction." Magnetic Resonance Imaging, vol. 52, pp. 118-130, October 2018. DOI: 10.1016/j.mri.2018.06.008
  2. Jeelani, Haris, Martin, Jonathan, Vasquez, Francis, Salerno, Michael, and Weller, Daniel S. "Image Quality Affects Deep Learning Reconstruction of MRI." Proc. IEEE International Symposium on Biomedical Imaging. Washington, DC, USA, April 2018, pp. 357-360.
  3. Zhou, Ruixi, Huang, Wei, Yang, Yang, Chen, Xiao, Weller, Daniel S., Kramer, Christopher M., Kozerke, Sebastian, and Salerno, Michael. "Simple motion correction strategy reduces respiratory-induced motion artifacts for k-t accelerated and compressed-sensing CMR perfusion imaging." J Cardiovascular Magnetic Resonance, vol. 20, no. 1, p. 6, February 2018. DOI: 10.1186/s12968-018-0427-1
  4. Weller, Daniel S. "Reconstruction with Dictionary Learning for Accelerated Parallel Magnetic Resonance Imaging." 2016 IEEE Southwest Symposium on Image Analysis and Interpretation. Santa Fe, NM, USA, March 2016, pp. 105-108.

Reconstruction Motion and Artifact Correction

Conventional motion correction
body image with conventional motion correction
Multiscale motion correction
body image with multiscale motion correction
Breathing motion and patient movements both can produce significant artifacts during image reconstruction. Conventional methods (left) are mostly ineffective in suppressing these artifacts, but recently developed multiscale methods promise to reduce these artifacts significantly.

One drawback of MRI is its suspectibility to a range of artifact-inducing phenomena, many of which can distort images to render them no longer useful. A frequent cause of such artifacts is subject motion, either body movements while lying in the scanner, or motion of the internal organs while breathing. Another such challenge, nonuniform magnetic fields can cause unacceptable signal loss or geometric distortions when imaging rapidly. Earlier work on head motion correction (in collaboration with Jeffrey Fessler and Douglas Noll at the University of Michigan supported by NIH F32 EB015914) investigated new ways for learning and correcting for head motion and nonuniform fields during the reconstruction. Current efforts (with Craig Meyer, John Mugler, and Michael Salerno at the University of Virginia) address motion in MRI's in young children, who tend to move in the scanner, and in freely-breathing MRI's of cardiac patients, where the heart moves during the breathing cycle. Current work is supported by NIH R21 EB022309.

Selected Publications:

  1. Weller, Daniel S., Noll, Douglas C., and Fessler, Jeffrey A. "Real Time Filtering with Sparse Variations for Head Motion in Magnetic Resonance Imaging." Signal Processing, in press. DOI: 10.1016/j.sigpro.2018.12.001
  2. Weller, Daniel S., Wang, Luonan, Mugler III, John P., and Meyer, Craig H. "Motion-compensated reconstruction of magnetic resonance images from undersampled data." Magnetic Resonance Imaging, vol. 55, pp. 36-45, January 2019. DOI: 10.1016/j.mri.2018.09.008
  3. Zhou, Ruixi, Huang, Wei, Yang, Yang, Chen, Xiao, Weller, Daniel S., Kramer, Christopher M., Kozerke, Sebastian, and Salerno, Michael. "Simple motion correction strategy reduces respiratory-induced motion artifacts for k-t accelerated and compressed-sensing CMR perfusion imaging." J Cardiovascular Magnetic Resonance, vol. 20, no. 1, p. 6, February 2018. DOI: 10.1186/s12968-018-0427-1
  4. Jeelani, Haris, Yang, Yang, Salerno, Michael, and Weller, Daniel S. "Evaluation of k-Space and Image-Space Motion Correction Schemes for CMR Perfusion." 20th Annual SCMR Scientific Sessions, Washington, DC, USA, February 2017, abstract.
  5. Luonan Wang and Weller, Daniel S. "Joint Motion Estimation and Image Reconstruction Using Alternating Minimization." ISMRM 24nd Scientific Meeting. Singapore, May 2016, p. 1800.
  6. Weller, Daniel S. and Fessler, Jeffrey A. "Fast non-Cartesian L1-SPIRiT with Field Inhomogeneity Correction." ISMRM 22nd Scientific Meeting. Milan, Italy, May 2014, p. 84. Summa cum laude award.
  7. Weller, Daniel S., Noll, Douglas C., and Fessler, Jeffrey A. "Prospective Motion Correction for Functional MRI Using Sparsity and Kalman Filtering." Proc. SPIE Wavelets and Sparsity XV, vol. 8858, pp. 885823-1-10, Aug. 2013. DOI: 10.1117/12.2023074

Automatic Enhancement, Reconstruction for Light Microscopy

3D brain reconstruction with conventional processing 3D brain reconstruction with flattening and structure propagation
Three-dimensional brain reconstruction from multilayered tissue sections requires careful alignment and normalization to produce a consistent volume. This work introduces section flattening and structurally-aware propagation of features to guide the realignment and produce an improved appearance (right) versus the conventional method (left).

Automatic methods are needed to deal with the high volumes of light microscope imaging data obtained by neuroscientists studying the behavior and interactions among individual neurons and glia in the brain. This research has two thrusts: enhancing and analyzing high-resolution video of the behavior of microglia and similar cells in two-photon microscope videos in different states (e.g., healthy versus during infection), and enhancing and reconstructing three-dimensional volumes describing targeted neurons activated during epileptic seizures in the dentate gyrus, dorsal hippocampus, and other interconnected brain regions. The first of these is supported by NSF Grant 1759802, in collaboration with Dr. Scott Acton and Dr. Gustavo Rohde. More information can be found on the Neuroglia Image Toolkit project website. The second project is in collaboration with Dr. Jaideep Kapur and Dr. Cedric Williams. Both of these projects build on the development of automatic image quality comparison and parameter selection tools and their integration with new image enhancement and reconstruction methods.

Selected Publications:

  1. Haoyi Liang, Dabrowska, Natalia, Kapur, Jaideep, and Weller, Daniel S. "Structure-based Intensity Propagation for 3D Brain Reconstruction with Multilayer Section Microscopy." IEEE Trans. Med. Imaging, in press. DOI: 10.1109/TMI.2018.2878488
  2. Haoyi Liang, Dabrowska, Natalia, Kapur, Jaideep, and Weller, Daniel. "Structure Correction for 3D Mouse Brain Reconstruction." Proc. IEEE International Symposium on Biomedical Imaging. Washington, DC, USA, April 2018, abstract.
  3. Haoyi Liang, Dabrowska, Natalia, Kapur, Jaideep, and Weller, Daniel. "Whole Brain Reconstruction from Multilayered Sections of a Mouse Model of Status Epilepticus." 2017 IEEE Asilomar Conf. on Signals, Systems, and Computers. Pacific Grove, CA, USA, October 2017, pp. 1260-1263.
  4. Haoyi Liang, Acton, Scott T., and Weller, Daniel S. "Content-Aware Neuron Image Enhancement." Proc. IEEE Int. Conf. on Image Processing. Beijing, China, September 2017, pp. 3510-3514.
  5. Haoyi Liang and Weller, Daniel S. "Comparison-based Image Quality Assessment for Selecting Image Restoration Parameters." IEEE Trans. Image Process., vol. 25, no. 11, pp. 5118-5130, Nov. 2016. DOI: 10.1109/TIP.2016.2601783
  6. Haoyi Liang and Weller, Daniel S. "Denoising method selection by comparison-based image quality assessment." 2016 IEEE Int. Conf. on Image Processing. Phoenix, AZ, USA, Sep. 2016, pp. 3106-3110.
  7. Haoyi Liang and Weller, Daniel S. "Regularization Parameter Trimming for Iterative Image Reconstruction." 2015 IEEE Asilomar Conf. on Signals, Systems, and Computers. Pacific Grove, CA, USA, Nov. 2015, pp. 755-759.

Previous Research

Fast Reconstruction Algorithms

Joint work with Sathish Ramani, Jon-Fredrik Nielsen, and Jeffrey Fessler at University of Michigan, and Yonina Eldar at Technion, supported by NIH F32 EB015914

High quality model-based reconstructions from incomplete measurements are necessary to enable faster, more robust MRI scans. However, such processing generally requires time-consuming iterative algorithms, which limits the image quality readily available to MRI users during a scan. For physicians and other users to integrate new advanced image reconstructions into their scanning protocols, faster implementations are needed. The algorithmic improvements made possible in this research, coupled with parallel and cloud computing capababilities, enable rapid reconstruction of images and time series on the fly. This research investigated new combinations of optimization techniques like variable-splitting and majorization-minimization in order to solve these complicated reconstruction problems more quickly.

Selected Publications:

  1. Weller, Daniel S. "Robust Phase Retrieval with Sparsity under Nonnegativity Constraints." 2016 IEEE Asilomar Conf. on Signals, Systems, and Computers. Pacific Grove, CA, USA, Nov. 2016, pp. 1043-1047.
  2. Weller, Daniel S. "Analysis-Form Sparse Phase Retrieval Using Variable-Splitting." 2016 IEEE Southwest Symposium on Image Analysis and Interpretation. Santa Fe, NM, USA, March 2016, pp. 61-64.
  3. Weller, Daniel S., Pnueli, Ayelet, Divon, Gilad, Radzyner, Ori, Eldar, Yonina C., and Fessler, Jeffrey A. "Undersampled Phase Retrieval with Outliers." IEEE Trans. Comput. Imaging, vol. 1, no. 4, pp. 247-258, December 2015. DOI: 10.1109/TCI.2015.2498402
  4. Weller, Daniel S., Pnueli, Ayelet, Radzyner, Ori, Divon, Gilad, Eldar, Yonina C., and Fessler, Jeffrey A. "Phase retrieval of sparse signals using optimization transfer and ADMM." 2014 IEEE Int. Conf. on Image Processing. Paris, France, Oct. 2014, pp. 1342-6.
  5. Weller, Daniel S., Ramani, Sathish, and Fessler, Jeffrey A. "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

Reconstruction Parameter Selection

Joint work with Sathish Ramani, Jon-Fredrik Nielsen, and Jeffrey Fessler at University of Michigan, supported by NIH F32 EB015914

Another hurdle to overcome with advanced reconstruction techniques for MRI and other applications is the selection of the numerical parameters that control the signal models used in the reconstruction. Using too large or too small a value can over-emphasize or under-regularize a reconstruction, yielding unacceptable image quality. Manual parameter tuning is too time-consuming and unreliable for widespread practical application, so automatic methods aim to adjust these parameters to optimize some measure of reconstructed image quality. We applied an automatic squared-error measure of image quality to choose regularization parameter values for advanced MRI reconstruction algorithms.

Selected Publications:

  1. Weller, Daniel S., Ramani, Sathish, Nielsen, Jon-Fredrik, and Fessler, Jeffrey A. "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
  2. Ramani, Sathish, Weller, Daniel S., Nielsen, Jon-Fredrik, and Fessler, Jeffrey A. "Non-Cartesian MRI Reconstruction With Automatic Regularization Via Monte-Carlo SURE." IEEE Trans. Med. Imag., vol. 32, no. 8, pp. 1411-1422, August 2013. DOI: 10.1109/TMI.2013.2257829
  3. Weller, Daniel S., Ramani, Sathish, Nielsen, Jon-Fredrik, and Fessler, Jeffrey A. "Automatic L1-SPIRiT Regularization Parameter Selection Using Monte-Carlo SURE." ISMRM 21st Scientific Meeting. Salt Lake City, USA, April 2013, p. 2602.
  4. Weller, Daniel S., Ramani, Sathish, Nielsen, Jon-Fredrik, and Fessler, Jeffrey A. "SURE-Based Parameter Selection for Parallel MRI Reconstruction using GRAPPA and Sparsity." 2013 IEEE Int. Symp. on Biomedical Imaging. San Francisco, USA, April 2013, pp. 954-957.

Accelerating MRI by Unifying Sparse Models and Multiple Receivers

Joint work with Vivek Goyal (thesis advisor) and Elfar Adalsteinsson at the Massachusetts Institute of Technology, Lawrence Wald and Jonathan Polimeni at the A. A. Martinos Center, and Leo Grady now at HeartFlow, Inc. (formerly with Siemens Corporate Research)

The time required to scan using magnetic resonance (MR) technology is a fundamental limiting factor of image quality and affordability and impedes the development of novel applications of MR imaging in clinical diagnostics. One popular method for accelerating the acquisition process in MR imaging include partial reconstructions from data acquired using multiple receiver coils. Recently, compressed sensing (CS) has been applied successfully to highly undersampled MR data, both of the single and multiple coil varieties. However, despite the intuitive complementary nature of using multiple coils and CS, only limited improvement has been attained by combining these methods, and the most successful methods involve non-standard acquisition parameters or sampling patterns. I am examining the problem of combining sparsity models and accelerated parallel imaging and exploring novel algorithms for their combination suitable for conventional MR imaging using uniformly-spaced undersampling patterns. A significant reduction in scan time will open up whole new possibilities for MR imaging, including real-time high-resolution MR video and rapid scanning for dynamic imaging.

Selected Publications:

  1. Weller, Daniel S., Polimeni, Jonathan R., Grady, Leo, Wald, Lawrence L., Adalsteinsson, Elfar, and Goyal, Vivek K. "Sparsity-Promoting Calibration for GRAPPA Accelerated Parallel MRI Reconstruction." IEEE Trans. Med. Imag., vol. 32, no. 7, pp. 1325-1335, July 2013. DOI: 10.1109/TMI.2013.2256923
  2. Weller, Daniel S., Polimeni, Jonathan R., Grady, Leo, Wald, Lawrence L., Adalsteinsson, Elfar, and Goyal, Vivek K. "Denoising Sparse Images from GRAPPA Using the Nullspace Method." Magn. Reson. Med., vol. 68, no. 4, pp. 1176-1189, Oct. 2012. DOI: 10.1002/mrm.24116
  3. Weller, Daniel S., Polimeni, Jonathan R., Grady, Leo, Wald, Lawrence L., Adalsteinsson, Elfar, and Goyal, Vivek K. "Accelerated Parallel Magnetic Resonance Imaging Reconstruction Using Joint Estimation with a Sparse Signal Model." 2012 IEEE Statist. Signal Process. Workshop. Ann Arbor, USA, August 2012, pp. 221-224.
  4. Weller, Daniel S., Polimeni, Jonathan R., Grady, Leo, Wald, Lawrence L., Adalsteinsson, Elfar, and Goyal, Vivek K. "Greater Acceleration Through Sparsity-Promoting GRAPPA Kernel Calibration." ISMRM 20th Scientific Meeting. Melbourne, Australia, May 2012, p. 3354.
  5. Weller, Daniel S., Polimeni, Jonathan R., Grady, Leo, Wald, Lawrence L., Adalsteinsson, Elfar, and Goyal, Vivek K. "Regularizing GRAPPA using simultaneous sparsity to recover de-noised images." Proc. SPIE Wavelets and Sparsity XIV, vol. 8138, pp. 81381M-1-9, Aug. 2011. DOI: 10.1117/12.896655
  6. Weller, Daniel S., Polimeni, Jonathan R., Grady, Leo, Wald, Lawrence L., Adalsteinsson, Elfar, and Goyal, Vivek K. "Combined compressed sensing and parallel MRI compared for uniform and random cartesian undersampling of k-Space." 2011 IEEE Int. Conf. on Acoustics, Speech, and Signal Processing. Prague, Czech Republic, May 2011, pp. 553-556. DOI: 10.1109/ICASSP.2011.5946463
  7. Weller, Daniel S., Polimeni, Jonathan R., Grady, Leo, Wald, Lawrence L., Adalsteinsson, Elfar, and Goyal, Vivek K. "SpRING: sparse reconstruction of images using the nullspace method and GRAPPA." ISMRM 19th Scientific Meeting. Montreal, Canada, May 2011, p. 2861.
  8. Weller, Daniel S., Polimeni, Jonathan R., Grady, Leo, Wald, Lawrence L., Adalsteinsson, Elfar, and Goyal, Vivek K. "Evaluating sparsity penalty functions for combined compressed sensing and parallel MRI." 2011 IEEE Int. Symp. on Biomedical Imaging. Chicago, USA, March-April 2011, pp. 1589-1592. DOI: 10.1109/ISBI.2011.5872706 Finalist, Student Paper Competition.
  9. Weller, Daniel S., Polimeni, Jonathan R., Grady, Leo, Wald, Lawrence L., Adalsteinsson, Elfar, and Goyal, Vivek K. "Combining nonconvex compressed sensing and GRAPPA using the nullspace method." ISMRM 18th Scientific Meeting. Stockholm, Sweden, May 2010, p. 4880.

Sampling Jitter in Analog-to-Digital Converters (ADCs)

Joint work with Vivek Goyal (thesis advisor) at the Massachusetts Institute of Technology

The sampling process of acquiring discrete-time (digital) signals from continuous-time (analog) signals used in analog-to-digital converters (ADCs) is highly susceptible to timing noise, or inaccuracy in the phase of the clock controlling the sampling process. Current methods involve designing analog clock circuitry with very nearly exact clock phase, at the expense of component cost, power consumption, and component size. As devices that interact with the analog world get smaller and have more stringent power requirements, ADCs have become a significant limiting factor. I am looking at signal processing alternatives for reducing the effect of timing noise, particularly non-linear algorithms for signal post-processing and am comparing these novel algorithms to those linear algorithms already developed. This work will enable significant advances in sensors, medical equipment, and scientific instrumentation. The sampling jitter problem also has higher-dimensional analogues in image-acquisition and microscopy.

Selected Publications:

  1. Weller, Daniel S. and Goyal, Vivek K. "Bayesian post-processing methods for jitter mitigation in sampling." IEEE Trans. Signal Process., vol. 59, no. 5, pp. 2112-2123, May 2011. DOI: 10.1109/TSP.2011.2108289
  2. Weller, Daniel S. and Goyal, Vivek K. "On the estimation of nonrandom signal coefficients from jittered samples." IEEE Trans. Signal Process., vol. 59, no. 2, pp. 587-597, Feb. 2011. DOI: 10.1109/TSP.2010.2090347
  3. Weller, Daniel S. and Goyal, Vivek K. "Jitter compensation in sampling via polynomial least squares estimation." 2009 IEEE Int. Conf. on Acoustics, Speech, and Signal Processing. Taipei, Taiwan, April 2009, pp. 3341-3344. DOI: 10.1109/ICASSP.2009.4960340