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Proceedings Paper

Spatial resolution and noise prediction in flat-panel cone-beam CT penalized-likelihood reconstruction
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Paper Abstract

Purpose: Model based iterative reconstruction (MBIR) algorithms such as penalized-likelihood (PL) methods have data-dependent and shift-variant image properties. Predictors of local reconstructed noise and resolution have found application in a number of methods that seek to understand, control, and optimize CT data acquisition and reconstruction parameters in a prospective fashion (as opposed to studies based on exhaustive evaluation). However, previous MBIR prediction methods have relied on idealized system models. In this work, we develop and validate new predictors using accurate physical models specific to flat-panel CT systems.

Methods: Novel predictors for estimation of local spatial resolution and noise properties are developed for PL reconstruction that include a physical model for blur and correlated noise in flat-panel cone-beam CT (CBCT) acquisitions. Prospective predictions (e.g., without reconstruction) of local point spread function and and local noise power spectrum (NPS) model are applied, compared, and validated using a flat-panel CBCT test bench. Imaging conditions investigated include two acquisition strategies (an unmodulated X-ray technique and automatic exposure control) as well as varying regularization strength.

Results: Comparisons between prediction and physical measurements show excellent agreement for both spatial resolution and noise properties. In comparison, traditional prediction methods (that ignore blur/correlation found in flat-panel data) fail to capture important data characteristics and show significant mismatch.

Conclusion: Novel image property predictors permit prospective assessment of flat-panel CBCT using MBIR. Such predictors enable standard and task-based performance assessments, and are well-suited to evaluation, control, and optimization of the CT imaging chain (e.g., x-ray technique, reconstruction parameters, novel data acquisition methods, etc.) for improved imaging performance and/or dose utilization.

Paper Details

Date Published: 9 March 2018
PDF: 6 pages
Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 1057346 (9 March 2018); doi: 10.1117/12.2294546
Show Author Affiliations
W. Wang, Johns Hopkins Univ. (United States)
G. J. Gang, Johns Hopkins Univ. (United States)
J. H. Siewerdsen, Johns Hopkins Univ. (United States)
J. W. Stayman, Johns Hopkins Univ. (United States)

Published in SPIE Proceedings Vol. 10573:
Medical Imaging 2018: Physics of Medical Imaging
Joseph Y. Lo; Taly Gilat Schmidt; Guang-Hong Chen, Editor(s)

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