A python framework for single‐image characterization of X‐ray focal spot distribution and detector point spread function

Purpose Accurate 2D characterization of X‐ray tube focal spot dimensions (FS) and detector Point Spread Function (PSF) is essential for radiographic quality assurance, yet traditional methods (pinhole, slit cameras) are either limited to 1D characterization or require impractical setups. This article introduces SCOPE‐XR, an open‐source Python framework that implements and generalizes a previously established reconstruction technique, providing fully automated 2D estimation of FS distributions and detector PSF from a single radiograph of a basic test object. The software is targeted at medical physicists, researchers, and clinical quality control personnel to streamline and enhance routine acceptance testing. Development and Validation Methods SCOPE‐XR processes radiographic images to estimate the shape and dimensions of FS and PSF distributions. The underlying algorithm utilizes automatic circle detection, derivation, pseudo‐CT reconstruction and incorporates an oversampling strategy to improve PSF reconstruction accuracy at limited sampling densities. The software was validated against both virtually simulated datasets and experimental clinical acquisitions, demonstrating high fidelity in characterizing source morphology and detector responses. Data Format and Usage Notes SCOPE‐XR is implemented in Python and is cross‐platform compatible (Windows, macOS, Linux), requiring minimal computational resources. The software accepts standard radiographic image formats (e.g., [DICOM, TIFF, RAW]) as input and outputs 2D emission profiles, quantitative dimensional metrics, and performance plots. A small dataset of virtual and experimental acquisitions is included as an example for benchmarking and reproducibility. The source code, datasets, and comprehensive documentation are publicly accessible via its public repository: https://doi.org/10.15161/oar.it/hrrqs‐cn059 . Potential Applications SCOPE‐XR provides a practical, fully automated alternative to traditional measurement techniques. Its primary clinical and scientific applications include the streamlined evaluation of imaging system performance during acceptance testing, routine quality control, and system design characterization.