Keith Even Schubert, Ph.D.

University of California, Santa Barbara

Ph.D.

Electrical and Computer Engineering, Control Systems

2003

Minors: Applied Mathematics and Signals and Communication
Dissertation: A New Look at Estimation

University of California, Los Angeles

M.S.

Electrical Engineering, Control Systems

1992

Minors: Applied Mathematics and Digital Signal Processing
Project: Guidance and Control of Amateur Rockets

University of Redlands

B.S.

General Engineering

1991

Minor: Mathematics
Project: Solar Desalination
Honors: Dean’s List (multiple times), Cum Laude

Entropy of Parallel Execution and Communication

Ernesto Gomez, Zongqi “Ritchie” Cai, Keith Evan Schubert

2017

We propose a definition of parallel state, derive a phase space from this state, and calculate the entropy of states and full executions using combinatorial analysis. A main contribution of this work is the introduction of an experimentally measurable phase space, which we then use to analyze execution states, ensembles of states, and ensembles of complete executions. We show that the entropy analysis reveals both expected and unexpected features of execution, and application of principal component analysis shows capability to extract execution details at the level of individual process states, as well as reveal hardware properties such as network or memory communications.

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Operation of the Preclinical Head Scanner for Proton CT

K.E. Schubert, et al.

2016

We report on the operation and performance tests of a preclinical head scanner developed for proton computed tomography (pCT). After extensive preclinical testing, pCT is intended to be employed in support of proton therapy treatment planning and pre-treatment verification in patients undergoing particle-beam therapy. In order to assess the performance of the scanner, we have performed CT scans with 200 MeV protons from both the synchrotron of the Loma Linda University Medical Center (LLUMC) and the cyclotron of the Northwestern Medicine Chicago Proton Center (NMCPC). The very high sustained rate of data acquisition, exceeding one million protons per second, allowed a full 360° scan to be completed in less than 7 min. The reconstruction of various phantoms verified accurate reconstruction of the proton relative stopping power (RSP) and the spatial resolution in a variety of materials. The dose for an image with better than 1% uncertainty in the RSP is found to be close to 1 mGy.

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Evaluation of Mathematical Algorithms for Automatic Patient Alignment in Radiosurgery

Keith E, Schubert, et al.

2014

Image registration techniques based on anatomical features can serve to automate patient alignment for intracranial radiosurgery procedures in an effort to improve the accuracy and efficiency of the alignment process as well as potentially eliminate the need for implanted fiducial markers. To explore this option, four two-dimensional (2D) image registration algorithms were analyzed: the phase correlation technique, mutual information (MI) maximization, enhanced correlation coefficient (ECC) maximization, and the iterative closest point (ICP) algorithm. Digitally reconstructed radiographs from the treatment planning computed tomography scan of a human skull were used as the reference images, while orthogonal digital x-ray images taken in the treatment room were used as the captured images to be aligned. The accuracy of aligning the skull with each algorithm was compared to the alignment of the currently practiced procedure, which is based on a manual process of selecting common landmarks, including implanted fiducials and anatomical skull features. Of the four algorithms, three (phase correlation, MI maximization, and ECC maximization) demonstrated clinically adequate (ie, comparable to the standard alignment technique) translational accuracy and improvements in speed compared to the interactive, user-guided technique; however, the ICP algorithm failed to give clinically acceptable results. The results of this work suggest that a combination of different algorithms may provide the best registration results. This research serves as the initial groundwork for the translation of automated, anatomy-based 2D algorithms into a real-world system for 2D-to-2D image registration and alignment for intracranial radiosurgery. This may obviate the need for invasive implantation of fiducial markers into the skull and may improve treatment room efficiency and accuracy.

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