Optimization of a customized Simultaneous Algebraic Reconstruction Technique algorithm for breast CT

Abstract

Iterative CT reconstruction algorithms coupled with edge-preserving filters are attracting a growing interest in the field of biomedical X-ray imaging. In many cases the application of such algorithms results in an improved reconstruction quality when compared with filtered back-projection (FBP). Iterative algorithms commonly entail a decrease of image noise or, equivalently, an increase of contrast-to-noise ratio, while preserving image detail. Conversely, they modify the shape of noise power spectrum, producing a shift towards lower spatial frequencies with respect to FBP. This results in a patchy or waxy appearance of the reconstructed images. Changes in image texture affect radiologists’ perception of image quality, possibly influencing their willingness to use an iterative algorithm in clinical practice. In this work we present a GPU implementation of a simultaneous algebraic reconstruction technique algorithm, combined with a bilateral regularization filter, and we discuss the optimization of the algorithm’s parameters in terms of noise texture, selecting those parameters which preserve its natural appearance. We evaluated the performances of the algorithm, compared to FBP, both on a test phantom and on a surgical mastectomy sample by using contrast-to-noise ratio and spatial resolution metrics. Samples were imaged at the SYRMEP beamline of the Elettra synchrotron facility with a monochromatic beam (32 keV) in propagation-based phase-contrast configuration, delivering a clinically-compatible radiation dose of 5 mGy and using a large-area CdTe photon-counting detector. Results show, in the specific application on breast specimens, that the implemented algorithm can be tuned to preserve the noise texture and spatial resolution observed in FBP reconstructions, while improving contrast-to-noise ratio up to 30%.