Multiple image radiography (MIR) is an X-ray phase-contrast technique that enhances soft-tissuevisibility by rejecting Compton scatter and capturing absorption, refraction, and ultra-smallangle x-ray scattering (USAXS) signals. Conventional MIR workflows depend on normalizationbetween object and reference datasets and precise angular alignment, making them sensitive todrift and prone to artifacts such as banding. We present an improved analysis framework, MIR2,which eliminates normalization and alignment by independently analyzing object and referencedata and applying angular calibration based on the dynamical theory of diffraction. Like MIR, itemploys 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 wasimplemented 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, layeredpaper) and in vivo imaging of a live anesthetized mouse lung. Across both studies, MIR2 producedmore stable and artifact-reduced images than MIR. The method simplifies analysis workflows andsupports streamlined application of MIR in biomedical and material imaging under dose-limitedconditions.