Effective magnetic susceptibility of 3D‐printed porous metal scaffolds
Bibliographic record
Abstract
Abstract Purpose 3D‐printed porous metal scaffolds are a promising emerging technology in orthopedic implant design. Compared to solid metal implants, porous metal implants have lower magnetic susceptibility values, which have a direct impact on imaging time and image quality. The purpose of this study is to determine the relationship between porosity and effective susceptibility through quantitative estimates informed by comparing coregistered scanned and simulated field maps. Methods Five porous scaffold cylinders were designed and 3D‐printed in titanium alloy (Ti‐6Al‐4V) with nominal porosities ranging from 60% to 90% using a cellular sheet‐based gyroid design. The effective susceptibility of each cylinder was estimated by comparing acquired B0 field maps against simulations of a solid cylinder of varying assigned magnetic susceptibility, where the orientation and volume of interest of the simulations was informed by a custom alignment phantom. Results Magnitude images and field maps showed obvious decreases in artifact size and field inhomogeneity with increasing porosity. The effective susceptibility was found to be linearly correlated with porosity (R2 = 0.9993). The extrapolated 100% porous (no metal) magnetic susceptibility was −9.9 ppm, closely matching the expected value of pure water (−9 ppm), indicating a reliable estimation of susceptibility. Conclusion Effective susceptibility of porous metal scaffolds is linearly correlated with porosity. Highly porous implants have sufficiently low effective susceptibilities to be more amenable to routine imaging with MRI.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".