Nonlinear statistical reconstruction for flat-panel cone-beam CT with blur and correlated noise models

Steven Tilley II; Jeffrey H. Siewerdsen; Wojciech Zbijewski; J. Webster Stayman

doi:10.1117/12.2216126

22 March 2016 Nonlinear statistical reconstruction for flat-panel cone-beam CT with blur and correlated noise models

Steven Tilley II, Jeffrey H. Siewerdsen, Wojciech Zbijewski, J. Webster Stayman

Proceedings Volume 9783, Medical Imaging 2016: Physics of Medical Imaging; 97830R (2016) https://doi.org/10.1117/12.2216126
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Flat-panel cone-beam CT (FP-CBCT) is a promising imaging modality, partly due to its potential for high spatial resolution reconstructions in relatively compact scanners. Despite this potential, FP-CBCT can face difficulty resolving important fine scale structures (e.g, trabecular details in dedicated extremities scanners and microcalcifications in dedicated CBCT mammography). Model-based methods offer one opportunity to improve high-resolution performance without any hardware changes. Previous work, based on a linearized forward model, demonstrated improved performance when both system blur and spatial correlations characteristics of FP-CBCT systems are modeled. Unfortunately, the linearized model relies on a staged processing approach that complicates tuning parameter selection and can limit the finest achievable spatial resolution. In this work, we present an alternative scheme that leverages a full nonlinear forward model with both system blur and spatially correlated noise. A likelihood-based objective function is derived from this forward model and we derive an iterative optimization algorithm for its solution. The proposed approach is evaluated in simulation studies using a digital extremities phantom and resolution-noise trade-offs are quantitatively evaluated. The correlated nonlinear model outperformed both the uncorrelated nonlinear model and the staged linearized technique with up to a 86% reduction in variance at matched spatial resolution. Additionally, the nonlinear models could achieve finer spatial resolution (correlated: 0.10 mm, uncorrelated: 0.11 mm) than the linear correlated model (0.15 mm), and traditional FDK (0.40 mm). This suggests the proposed nonlinear approach may be an important tool in improving performance for high-resolution clinical applications.

Citation Download Citation

Steven Tilley II, Jeffrey H. Siewerdsen, Wojciech Zbijewski, and J. Webster Stayman "Nonlinear statistical reconstruction for flat-panel cone-beam CT with blur and correlated noise models", Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 97830R (22 March 2016); https://doi.org/10.1117/12.2216126

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