Browsing by Autor "Edgar Salazar"

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A Grayscale Coded Illumination for Compressive X-ray Compton Backscattering Imaging
(2025) Abdullah Alrushud; Edgar Salazar; Gonzalo R. Arce
We propose a grayscale coded illumination technique for compressive X-ray compton backscattering imaging. Results show a major improvement over binary illumination patterns in terms of peak signal-to-noise ratio and structural similarity index.
Impact of scatter radiation on spectral quantification performance of first‐ and second‐generation dual‐layer spectral computed tomography
(Wiley, 2024) Edgar Salazar; Leening P. Liu; Amy E. Perkins; Sandra S. Halliburton; Nadav Shapira; Harold Litt; Peter B. Noël
The implementation of a two-dimensional anti-scatter grid in the second-generation DLCT improves the spectral quantification performance. In the clinical routine, this improvement may enable additional clinical benefits, for example, in lung imaging.
Optimizing Transmittance for Enhanced Compressive X-Ray Compton Backscattering Imaging
(2025) Edgar Salazar; Abdullah Alrushud; Gonzalo R. Arce
The Compressive X-ray Compton Backscattering Imager (CXBI) is a method that overcomes the limitations of conventional pixel-by-pixel Compton scanning. It uses coded illumination projected onto the target, combined with relative movement between the mask and the body. Due to CXBI being recently proposed, limited efforts have been made to find optimal mask patterns and their respective parameters. In this paper, we present a data-driven framework to find optimal binary coding patterns for CXBI. This process uses compressive sensing regularizers that are directly related to the CXBI forward model. To find the optimal transmittance value (the percentage of non-blocking pixels in the pattern), the optimization problem was solved for various transmittance values. The results indicate that, on average, 10% and 30% transmittance yield the best Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM) values.