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This study presents an innovative approach to constructing a representative system matrix in x-ray imaging forward models. The approach leverages the combination of machine learning algorithms and fundamental physical principles through the use of physics-informed machine learning (PIML). The main goal is to seamlessly integrate machine learning algorithms with core physical principles to provide a nuanced perspective on the development of an interpretable and adaptive system matrix. In contrast to traditional data-intensive methods, this research intentionally prioritizes the incorporation of physics-based constraints into the machine learning framework. The methodology involves carefully extracting relevant features from x-ray imaging data to capture essential object characteristics, which are then integrated into a machine learning model. By including physics-based constraints, the model aligns with the underlying principles that govern x-ray interactions. Through rigorous mathematical validation and preliminary experimentation, the approach demonstrates its feasibility, particularly in situations where acquiring extensive datasets is challenging. From a technical standpoint, the strength of this methodology lies in the inherent adaptability and interpretability of the system matrix, which are crucial for accurate image reconstruction and measurement prediction. The implications of this research span diverse domains and highlight the potential transformative effects on x-ray imaging applications in electronics, medical imaging, and material inspection. In the realm of electronics, the adaptable system matrix improves non-destructive testing by aiding in defect detection and ensuring the reliability of electronic components. In medical imaging, enhanced interpretability leads to improved diagnostic accuracy while reducing radiation exposure. In material inspection, this approach facilitates the identification of structural anomalies and material composition, thereby advancing quality control practices. While recognizing the preliminary nature of the framework, this study lays the groundwork for future research at the intersection of machine learning and physics in x-ray imaging, representing a progressive step towards unlocking transformative possibilities for enhanced accuracy and adaptability across various domains.
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