Optimization and image quality assessment of the alpha-image reconstruction algorithm: iterative reconstruction with well-defined image quality metrics

Sergej Lebedev; Stefan Sawall; Stefan Kuchenbecker; Sebastian Faby; Michael Knaup; Marc Kachelrieß

doi:10.1117/12.2082043

18 March 2015 Optimization and image quality assessment of the alpha-image reconstruction algorithm: iterative reconstruction with well-defined image quality metrics

Sergej Lebedev, Stefan Sawall, Stefan Kuchenbecker, Sebastian Faby, Michael Knaup, Marc Kachelrieß

Author Affiliations +

Proceedings Volume 9412, Medical Imaging 2015: Physics of Medical Imaging; 94120O (2015) https://doi.org/10.1117/12.2082043
Event: SPIE Medical Imaging, 2015, Orlando, Florida, United States

Abstract

The reconstruction of CT images with low noise and highest spatial resolution is a challenging task. Usually, a trade-off between at least these two demands has to be found or several reconstructions with mutually exclusive properties, i.e. either low noise or high spatial resolution, have to be performed. Iterative reconstruction methods might be suitable tools to overcome these limitations and provide images of highest diagnostic quality with formerly mutually exclusive image properties. While image quality metrics like the modulation transfer function (MTF) or the point spread function (PSF) are well-defined in case of standard reconstructions, e.g. filtered backprojection, the iterative algorithms lack these metrics. To overcome this issue alternate methodologies like the model observers have been proposed recently to allow a quantification of a usually task-dependent image quality metric.¹ As an alternative we recently proposed an iterative reconstruction method, the alpha-image reconstruction (AIR), providing well-defined image quality metrics on a per-voxel basis.² In particular, the AIR algorithm seeks to find weighting images, the alpha-images, that are used to blend between basis images with mutually exclusive image properties. The result is an image with highest diagnostic quality that provides a high spatial resolution and a low noise level. As the estimation of the alpha-images is computationally demanding we herein aim at optimizing this process and highlight the favorable properties of AIR using patient measurements.

Citation Download Citation

Sergej Lebedev, Stefan Sawall, Stefan Kuchenbecker, Sebastian Faby, Michael Knaup, and Marc Kachelrieß "Optimization and image quality assessment of the alpha-image reconstruction algorithm: iterative reconstruction with well-defined image quality metrics", Proc. SPIE 9412, Medical Imaging 2015: Physics of Medical Imaging, 94120O (18 March 2015); https://doi.org/10.1117/12.2082043

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KEYWORDS

Image quality

Reconstruction algorithms

Modulation transfer functions

Spatial resolution

Neodymium

Tissues

Computed tomography

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