Defense Event

Accelerated Optimization Algorithms for Statistical 3D X-ray CT Image Reconstruction

Donghwan Kim

Monday, May 19, 2014
1:30pm - 3:30pm
3316 EECS

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About the Event

X-ray computed tomography (CT) has been widely celebrated for its ability to visualize the patient anatomy, but high radiation exposure is a concern. Statistical image reconstruction algorithms in X-ray CT can provide improved image quality for reduced dose levels in contrast to the conventional filtered back-projection (FBP) methods. However, the statistical approach requires substantial computation time. Therefore, this work develops fast iterative algorithms for statistical reconstruction. Ordered subsets (OS) methods have been used widely in tomography problems, because they reduce the computational cost by using only a subset of the measurement data per iteration. However, OS methods require too long a reconstruction time to be used routinely for every clinical CT scan. In this talk, two approaches are presented for accelerating OS algorithms, one that uses a novel spatially nonuniform optimization transfer approach and one that combines with momentum algorithms (OS-momentum). The initial version of OS-momentum uses well-known Nesterov's momentum methods. To further accelerate OS-momentum algorithms, this work proposes novel momentum methods, called optimized gradient methods, which are twice as fast yet have remarkably simple implementations comparable to Nesterov's methods. Simulated and real patient 3D CT scans are used to examine the acceleration of the proposed algorithms

Additional Information

Sponsor(s): Prof. Jeffrey A. Fessler

Open to: Public