Image texture aided spectral CT material decomposition

J. C. Rodriguez Luna; Diego Andrade; Mini Das

doi:10.1117/12.3008896

3 April 2024 Image texture aided spectral CT material decomposition

J. C. Rodriguez Luna, Diego Andrade, Mini Das

Proceedings Volume 12925, Medical Imaging 2024: Physics of Medical Imaging; 129252K (2024) https://doi.org/10.1117/12.3008896
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Spectral capabilities of photon counting detectors (PCDs) can allow material decomposition. We recently showed multi-material decomposition using high resolution photon counting detectors (Medipix3). High-resolution photon counting spectral detectors have unique advantages and challenges. Some of the challenges arise from noise properties as well as spectral distortions. We show benefits of an empirical correction method to obtain accurate attenuation values in a spectral CT even with high resolution detectors and a combination of spectral distortions. Aided with accurate spectral correction, we show that a Gaussian mixture model assisted iterative decomposition can separate multiple materials at once. Our group has been investigating the role of image texture features in signal detection performance in tomographic images. Here we will explore utilizing variations in image texture features in spectral CT material decomposition. Along with attenuation variations for each energy bin, second order statistical texture feature variations associated with spectral data will be used to reduce the number of energy bins and imaging dose to perform multi-material decomposition. With promising preliminary results, we will show a more thorough investigation of image texture variations in spectral data to assist efficient and low dose material decomposition in spectral CT.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

J. C. Rodriguez Luna, Diego Andrade, and Mini Das "Image texture aided spectral CT material decomposition", Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging, 129252K (3 April 2024); https://doi.org/10.1117/12.3008896

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KEYWORDS

Windows

Cooccurrence matrices

Computed tomography

Sensors

Attenuation correction

Polymethylmethacrylate

Attenuation

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