A Gaussian fitting-based analysis method for multiple image radiography with integrated angular calibration, MIR2
Bibliographic record
Abstract
Abstract 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 in vivo 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".