Intra- and inter-patient tissue variability are seldom implemented in digital phantoms for imaging simulations, which can lead to issues when developing and evaluating material differentiation methods. In this work, we evaluated two methods for generating variability in tissue attenuation properties based on measured properties of human tissue. Our goal is to find a sampling method that generates attenuation curves within measured distributions. The first approach parameterizes tissue attenuation curves as a linear combination of aluminum and PMMA. The second approach is based on the Midgley decomposition model, where the attenuation curve is expressed in terms of five coefficients. Attenuation curves were generated by sampling the two- and five-parameter spaces, and they were compared to previous measurements in ex-vivo adipose tissue acquired at 8 , 11, 15, 20 and 30keV. The average differences of the sampled curves relative to the measurements were 1.68% (2-parameter) and 1.31% (5-parameter), and the absolute differences in coefficients of variation were under 2% for both methods. These results indicate that both methods captured the variability present in measured attenuation curves. This study provides preliminary insights into the effectiveness of two methods for adding tissue variability to imaging simulations.
The fine alignment of X-ray nano-focusing optics, such as Kirkpatrick-Baez (KB) mirrors, depends strongly on the ability to diagnose the X-ray beam at the focus position. Despite conventional diagnostics techniques (e.g. knife-edge) allowing the measurement of the beam profile with sub-micrometer resolution, they may yield poor accuracy for beams with sizes under 100 nm. With nanometer-resolution phase-recovering techniques like ptychography, information about optical aberrations can be obtained experimentally in the complex-valued wavefront. In this work, we use wave-propagation simulations with Synchrotron Radiation Workshop (SRW) to model the CARNAÚBA beamline at Sirius. The beam phase at the KB mirrors exit pupil is decomposed in terms of Zernike rectangular polynomials. The relevant degrees of freedom (DOF) of the mirrors are scanned, allowing the correlation of the Zernike coefficients with the beam profile at focus. Therefore, the aberrations are classified and quantified for each mirror’s DOF, and alignment tolerances are obtained. We find that each DOF can be described by a unique combination of only three Zernike terms. Additionally, a database with the first 15 Zernike coefficients is created by simulating random alignment states and used to train a simple fully-connected neural network. The neural network was able to determine the alignment states of unknown samples with errors below 3%. The combination of Zernike polynomials and neural networks could potentially lead to single-iteration alignment of KB mirrors using wavefront sensing techniques as a diagnostic tool.
KEYWORDS: Sensors, Photon counting, Monte Carlo methods, Luminescence, Dispersion, Point spread functions, Energy efficiency, Semiconductors, Photon transport, Spatial resolution
X-ray imaging techniques widely employ semiconductor detectors. Energy integrating (EI) detectors are used in digital radiography and photon counting (PC) in CT. This work aims to implement a detailed Monte Carlo modeling of these sensors. The model was divided into radiation interaction and electron-hole pairs (EHP) creation and dispersion. The PENELOPE code simulated the radiation transport. In each electron interaction, the absorbed energy was converted into EHP considering the pair creation energy and the Fano factor. The detection position was sampled using a Gaussian distribution, where the standard deviation was from the Einstein diffusion equation. The Hetch equation models the charge trapping. In the PC mode, the photon was counted if the energy deposited was higher than a threshold (ethr ). Monoenergetic pencil beams between 10 and 100 keV were simulated, with 107 histories. The detector material was cadmium tellurite, with 50 μm pixel size, whose thicknesses, applied electric field, and ethr vary, respectively from 250 to 1000 μm, 0.01 to 1 V/μm, and from 1 to 50 keV. The results show a wider detector response as the beam energy increases. For energies above 32 keV the fluorescence is greatly responsible for this spread. The detector’s efficiency increases with the sensor thickness and decreases with the photon energy. Charge trapping decreases the efficiency up to 43,53%. For the PC mode, an ethr increase yields a narrower detector response and increases the image noise. This study provides a detailed detector modeling and, consequently, insight into the imaging system’s fundamental limitations.
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