Incorporating robustness in diagonally-relaxed orthogonal projections method for proton computed tomography

Authors

Paniz Karbasi, Baylor University
Blake Schultze, Baylor University
Valentina Giacometti, University of WollongongFollow
Tia E. Plautz, University of CaliforniaFollow
Keith Schubert, Baylor University
Reinhard W. Schulte, Loma Linda UniversityFollow
Vladimir Bashkirov, Loma Linda University

RIS ID

110535

Publication Details

Karbasi, P., Schultze, B., Giacometti, V., Plautz, T., Schubert, K. E., Schulte, R. W. & Bashkirov, V. A. (2015). Incorporating robustness in diagonally-relaxed orthogonal projections method for proton computed tomography. 2015 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) (pp. 1-4). United States: IEEE.

Abstract

Iterative algorithms such as ART, DROP, and CARP are commonly used in reconstructing computed tomography images, but only account for errors in the measurements. Errors in the predicted path and intersection lengths, or even blocks of missing measurements can result in degraded image quality. Robust techniques allow for errors in other areas of the model and produce good images that show less sensitivity. In this paper we introduce a robust version of DROP and compare its performance advantages to the standard DROP algorithm on on real data.