A Gaussian fitting-based analysis method for multiple image radiography with integrated angular calibration, MIR2

Multiple image radiography (MIR) is an X-ray phase-contrast technique that enhances soft-tissue visibility by rejecting Compton scatter and capturing absorption, refraction, and ultra-small-angle x-ray scattering (USAXS) signals. Conventional MIR workflows depend on normalization between object and reference datasets and precise angular alignment, making them sensitive to drift and prone to artifacts such as banding. We present an improved analysis framework, MIR2, which eliminates normalization and alignment by independently analyzing object and reference data and applying angular calibration based on the dynamical theory of diffraction. Like MIR, it employs pixel-wise Gaussian fitting of angular intensity profiles, but the MIR2 pipeline is simpler, less error-prone, and more robust against alignment-related artifacts. Importantly, artifact suppression is achieved intrinsically, without relying on additional correction algorithms. MIR2 was implemented in Python and validated at the BMIT beamline (Canadian Light Source, 33.3 keV, Si(220) double-crystal monochromator) using both test objects (PMMA step wedge, layered paper) and<i>in vivo</i>imaging of a live anesthetized mouse lung. Across both studies, MIR2 produced more stable and artifact-reduced images than MIR. The method simplifies analysis workflows and supports streamlined application of MIR in biomedical and material imaging under dose-limited conditions.