1Electrical
and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana,
IL, United States; 2Beckman Institute, University of Illinois at
Urbana-Champaign, Urbana, IL, United States; 3Center for Imaging
of Neurodegenerative Diseases, Department of Veteran Affairs Medical Center,
San Francisco, CA, United States; 4Department of Radiology and
Biomedical Imaging, University of California, San Francisco, CA, United
States
We propose a new method to jointly denoise a sequence of noisy images typically acquired in quantitative imaging experiments. The proposed method uses a penalized maximum likelihood estimation formalism, integrating two modeling constraints: a low-rank model that captures any correlation in the edge structures from one frame to another and a penalty function that promotes sparse edge structures. A computationally efficient algorithm is developed to solve the associated optimization problem. Representative results from a parametric mapping experiment are presented to demonstrate the performance of the proposed method.