Simulating computed tomography (CT) systems offers numerous advantages, including the optimization of scan parameters, training of specialist personnel, and quantification of measurement uncertainties. Current simulation approaches, often referred to as virtual computed tomography (vCT), predominantly rely on analytical models. However, these models require extensive system-specific tuning to produce realistic synthetic measurements, creating a significant barrier to broader adoption and efficiency. To address this challenge, this work explores the potential of implicit neural representation (INR) as an alternative to the analytical models used in vCT. INRs excel at representing complex, high-dimensional data in a continuous and differentiable manner, making them a promising substitute for traditional analytical models. As a first building block, we propose a two-stage approach for simulating the X-ray beam energy and detector signal processing in industrial CT systems. This method is trained and evaluated using real-world data. Results demonstrate that the proposed INR-based architecture can accurately generate synthetic projections for parameter configurations within the training dataset. However, its poor performance on the test dataset highlights limitations in generalization beyond the training data. Potential methods to address these shortcomings are discussed.
This study underscores the potential of INRs as a flexible framework for simulating complex CT systems. By capturing subtle system-specific characteristics and reducing dependence on explicitly defined parameterizations, INRs could pave the way for
more versatile and efficient simulation models.