Abstract 295: Magnetic Resonance Imaging Characterization of Peripheral Arterial Chronic Total Occlusions With MicroCT and Histologic Validation
Bibliographic record
Abstract
Objectives: Guidelines recommend surgical bypass for peripheral chronic total occlusions (CTOs). Endovascular revascularization, however, offers improved morbidity and shorter length of hospitalization. Not all lesions are amenable to this technique but predicting crossability is difficult due to limitations in characterizing CTOs with current imaging techniques. This study demonstrates the ability of MRI to characterize peripheral CTO components with microCT and histologic validation. Methods: MRI was performed on 15 excised human peripheral arterial CTO segments from 4 patients. Each sample was imaged at 7 Tesla at high resolution (75μm3 voxels) to produce T2- and T2*-maps using ultrashort echo (UTE) sequences with echo times: {20μs, 500μs, 1ms}. A T2* difference image was produced by subtracting the UTE images and a phase map was constructed. The T2, UTE 20μs and T2* difference images were used together to differentiate CTO components. MicroCT and histology were used to validate regions of interest (ROIs). Results: 3 independent reviewers identified 47 ROIs. There was excellent agreement between MRI and microCT for calcium (sensitivity 87%, specificity 99%). There was also good agreement between MRI and histology for adipose tissue (100%, 100%), soft tissue (97%, 97%), thrombus (78%, 100%), collagen (83%, 94%) and open lumen (95%, 98%). Conclusions: These results demonstrate the potential of high-resolution T2 and T2* imaging using UTE, to characterize lesion components in human peripheral CTOs. Further work is required to better differentiate thrombus from collagen. This study provides the foundation for future studies in determining the lesion crossability in CTOs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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".