2010/03/04 by Erik Fredenberg, Magnus Aslund, Magnus Åslund +4 · 1 citation
Engineering · Mathematics · Medicine · Physics and Astronomy · #Acoustics #Advanced X-ray and CT Imaging #Artificial intelligence #Attenuation #Computer science #Contrast-to-noise ratio #Detective quantum efficiency #Detector #Digital Radiography and Breast Imaging #Energy (signal processing) #Image quality #Mammography #Mathematics #Medicine #Noise (video) #Observer (physics) #Optics #Physics #Quantum #Quantum noise #Radiation Dose and Imaging #Signal-to-noise ratio (imaging) #Spectral imaging #Subtraction #Weighting #physics.med-ph
paper · pdf · doi:10.1117/12.845480
published as Proc. SPIE 7622, Medical Imaging 2010: Physics of Medical Imaging, 762210 (2010)
openalex publication_date 2010/03/04 · arxiv created 2021/01/23 · arxiv updated 2021/01/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.