Observer model optimization of a spectral mammography system

TL;DR

This study optimizes a spectral mammography system using a theoretical framework that includes anatomical and quantum noise, showing that energy subtraction enhances detection of large tumors, while energy weighting benefits microcalcifications.

Contribution

It introduces a comprehensive optimization approach for spectral mammography that accounts for anatomical noise, revealing different optimal strategies for various imaging tasks.

Findings

Energy subtraction improves detection of large tumors by nearly 50%.

Energy weighting offers slight improvements for microcalcifications.

Performance is robust across different beam qualities and detector resolutions.

Abstract

Spectral imaging is a method in medical x-ray imaging to extract information about the object constituents by the material-specific energy dependence of x-ray attenuation. Contrast-enhanced spectral imaging has been thoroughly investigated, but unenhanced imaging may be more useful because it comes as a bonus to the conventional non-energy-resolved absorption image at screening; there is no additional radiation dose and no need for contrast medium. We have used a previously developed theoretical framework and system model that include quantum and anatomical noise to characterize the performance of a photon-counting spectral mammography system with two energy bins for unenhanced imaging. The theoretical framework was validated with synthesized images. Optimal combination of the energy-resolved images for detecting large unenhanced tumors corresponded closely, but not exactly, to minimization of the anatomical noise, which is commonly referred to as energy subtraction. In that case, an ideal-observer detectability index could be improved close to 50% compared to absorption imaging. Optimization with respect to the signal-to-quantum-noise ratio, commonly referred to as energy weighting, deteriorated detectability. For small microcalcifications or tumors on uniform backgrounds, however, energy subtraction was suboptimal whereas energy weighting provided a minute improvement. The performance was largely independent of beam quality, detector energy resolution, and bin count fraction. It is clear that inclusion of anatomical noise and imaging task in spectral optimization may yield completely different results than an analysis based solely on quantum noise.