Diffusion-Tensor Imaging Versus Digitization in Reconstructing the Masseter Architecture
Bibliographic record
Abstract
Accurate characterization of the craniomaxillofacial (CMF) skeleton using finite element (FE) modeling requires representation of complex geometries, heterogeneous material distributions, and physiological loading. Musculature in CMF FE models are often modeled with simple link elements that do not account for fiber bundles (FBs) and their differential activation. Magnetic resonance (MR) diffusion-tensor imaging (DTI) enables reconstruction of the three-dimensional (3D) FB arrangement within a muscle. However, 3D quantitative validation of DTI-generated FBs is limited. This study compares 3D FB arrangement in terms of pennation angle (PA) and fiber bundle length (FBL) generated through DTI in a human masseter to manual digitization. CT, MR-proton density, and MR-DTI images were acquired from a single cadaveric specimen. Bone and masseter surfaces were reconstructed from CT and MR-proton density images, respectively. PA and FBL were estimated from FBs reconstructed from MR-DTI images using a streamline tracking (STT) algorithm (n = 193) and FBs identified through manual digitization (n = 181) and compared using the Mann-Whitney test. DTI-derived PAs did not differ from the digitized data (p = 0.411), suggesting that MR-DTI can be used to simulate FB orientation and the directionality of transmitted forces. Conversely, a significant difference was observed in FBL (p < 0.01) which may have resulted due to the tractography stopping criterion leading to early tract termination and greater length variability. Overall, this study demonstrated that DTI can yield muscle FB orientation data suitable to representative directionality of physiologic muscle loading in patient-specific CMF FE modeling.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".