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.

Date Published: 18 March 2015
PDF: 7 pages
Proc. SPIE 9412, Medical Imaging 2015: Physics of Medical Imaging, 94120O (18 March 2015); doi: 10.1117/12.2082043

Published in SPIE Proceedings Vol. 9412:
Medical Imaging 2015: Physics of Medical Imaging
Christoph Hoeschen; Despina Kontos, Editor(s)