Analysis of Radiographic Contrast Markers for X-ray Digital Image Correlation of Tissue-Simulants Under Dynamic Load
Bibliographic record
Abstract
The study of traumatic brain injury is critical to the improvement of protective equipment.Numerical models of brain deformation require real-world data for validation.In preparation for upcoming cadaver studies, a novel method of measuring displacement and strain fields of optically inaccessible internal planes using high-speed X-ray, embedded contrast markers and digital image correlation (DIC) is presented herein.An uncoupled scintillator and optimally-selected highspeed camera enable continuous X-ray imaging through a human head at 10,000 fps.As varying composition creates radiographic contrast, contrast within a human brain is limited, therefore, artificial contrast markers are required.Markers must be dynamically coupled to the bulk material and provide sufficient X-ray contrast.An analytical tool was developed to design of contrast markers.The impact of contrast-to-noise ratio and out-of-plane motion on DIC accuracy were quantified.Finally, a feasibility study using a biofidelic headform subjected to a NOCSAE drop test is presented.iiiAs I turn the page on this chapter of my life, I cannot help but to reflect on the enormous contributions of an incredible team of collaborators that made it all possible.The completion of this work is due in no small part to my advisor, Professor Oren Petel.His support and passion for my research, made this experience enjoyable.You have provided me with unique opportunities to explore my field over the last two years, encouraging personal growth, and leading me to ask the important question.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".