A comparison of phase retrieval methods for propagation-based phase contrast X-ray imaging with polychromatic sources
Bibliographic record
Abstract
Propagation-based phase-contrast X-ray imaging is the earliest developed phase-contrast X-ray imaging method, however, it requires mathematically intensive algorithms to retrieve phase information. For quantitative analysis, an algorithm called phase retrieval must be applied to the phase-contrast image to retrieve the phase information because the phase and the attenuation coefficient are encoded in the intensity values obtained by the detector. Phase retrieval is a nonlinear inverse technique used to estimate the object X-ray phase shift, thickness or electron density. Many of these phase retrieval methods have been developed assuming a monochromatic X-ray source, although some have been reported to work for a polychromatic X-ray source with some modifications. In this work, we compare seven reported phase retrieval methods for polychromatic sources using a weighted average to calculate the wavelength dependent parameters. Six of the methods compared are single distance approaches and one is an iterative approach that requires an absorption and phase-contrast image. These seven phase retrieval methods are compared for varying object thicknesses in the presence of a polychromatic source in simulation of different materials. The materials investigated in this work are polytetra uorethylene, polystyrene and Kapton. Overall, Paganin's method performed with the lowest relative error for all materials when a polychromatic source is applied to object thicknesses less than 400 microns.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".