Medical physics · 2025
Software Article: A generalized cascaded linear system model implementation for x-ray detectors.
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Pacheco Gustavo, Pautasso Juan J, Michielsen Koen, Sechopoulos Ioannis
Аннотация
PURPOSE: Cascaded linear models are widely used for the development and optimization of x-ray imaging systems, yet no publicly available Python implementation currently exists. We introduce CASYMIR, a flexible and open-source Python package capable of modeling direct and indirect-conversion x-ray imaging detectors under various acquisition conditions.
METHODS: We employed a modular software design with generalized frequency-domain expressions for each process in the detection chain, which can be implemented as serial or parallel blocks. The gain factors and other parameters are derived from the detector's characteristics, system geometry, and incident x-ray spectra, all of which can be specified by the user. The signal reaching the detector is propagated throughout the detection stages by applying these process blocks, enabling the computation of the Modulation Transfer Function (MTF) and Noise Power Spectrum (NPS) at any stage of the model.
VALIDATION: Our implementation was experimentally validated using two commercial x-ray detectors: a flat-panel a-Se detector for digital mammography and digital breast tomosynthesis, and a flat-panel scintillator (CsI) detector for dedicated breast CT. The modeled MTF had root-mean-square (RMS) percent errors below 6% for the a-Se detector, while the normalized RMS error for the NNPS was below 3%. For the CsI detector, the RMS percent error in the MTF was 5.4%, and the normalized RMS error for the NNPS was 5.8%.
USAGE NOTES: The CASYMIR Python package can be downloaded from https://github.com/radboud-axti/casymir_public, and it includes a standalone executable script suitable for modeling common commercial systems, along with an extensive README file and example files.
POTENTIAL APPLICATIONS: CASYMIR is available as an open-source Python package under the MIT license. Given its modular and flexible structure, it can be easily modified and integrated into other simulation/virtual clinical trial pipelines where information about the detector's spatial resolution and noise performance is needed. The standalone version of CASYMIR may be particularly useful for running batch simulations with varying acquisition and system parameters, making it ideal for optimizing system design and acquisition techniques. Furthermore, given the package's modular structure, new processes can be implemented to simulate other detector and system designs.
METHODS: We employed a modular software design with generalized frequency-domain expressions for each process in the detection chain, which can be implemented as serial or parallel blocks. The gain factors and other parameters are derived from the detector's characteristics, system geometry, and incident x-ray spectra, all of which can be specified by the user. The signal reaching the detector is propagated throughout the detection stages by applying these process blocks, enabling the computation of the Modulation Transfer Function (MTF) and Noise Power Spectrum (NPS) at any stage of the model.
VALIDATION: Our implementation was experimentally validated using two commercial x-ray detectors: a flat-panel a-Se detector for digital mammography and digital breast tomosynthesis, and a flat-panel scintillator (CsI) detector for dedicated breast CT. The modeled MTF had root-mean-square (RMS) percent errors below 6% for the a-Se detector, while the normalized RMS error for the NNPS was below 3%. For the CsI detector, the RMS percent error in the MTF was 5.4%, and the normalized RMS error for the NNPS was 5.8%.
USAGE NOTES: The CASYMIR Python package can be downloaded from https://github.com/radboud-axti/casymir_public, and it includes a standalone executable script suitable for modeling common commercial systems, along with an extensive README file and example files.
POTENTIAL APPLICATIONS: CASYMIR is available as an open-source Python package under the MIT license. Given its modular and flexible structure, it can be easily modified and integrated into other simulation/virtual clinical trial pipelines where information about the detector's spatial resolution and noise performance is needed. The standalone version of CASYMIR may be particularly useful for running batch simulations with varying acquisition and system parameters, making it ideal for optimizing system design and acquisition techniques. Furthermore, given the package's modular structure, new processes can be implemented to simulate other detector and system designs.