Quasi‐static magnetic resonance elastography at 7 T to measure the effect of pathology before and after fixation on tissue biomechanical properties
Bibliographic record
Abstract
Evaluation of imaging for cancer detection and localization can be achieved by correlation of gold-standard histopathology with imaging data. Usage of a 3D biomechanical-based deformable registration for correlation of the histopathology of whole-tissue specimens with ex vivo imaging necessitates measurement of the distribution of biomechanical properties in the ex vivo tissue specimen and changes that occur during pathology fixation. To measure high-resolution 3D distributions of Young's modulus (E) prefixation and postfixation, a quasi-static magnetic resonance elastography method was developed at 7 T. Use of echo-planar imaging allowed for shorter imaging times, in line with limited time frames allowable for pathology specimens. The finite element modeling algorithm produced voxel-wise E measures, and mechanical indentation was used for comparison. An initial preclinical evaluation with canine prostate specimens (n = 5) demonstrated a consistent increase in E with fixation (P < 0.002) by a factor of 4 (± 1). Increases were a function of distance from the tissue edge and correlated with fixation time (ρ = 1, P < 0.02). The technique will be used to generate population-averaged data of E from clinical ex vivo specimens prefixation and postfixation to inform registration of whole-mount histopathology with in vivo imaging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".