Registration and fusion of multimodal vascular images: a phantom study
Bibliographic record
Abstract
The aim of this work was to compare the geometric accuracy of X-ray angiography, MRI, X-ray computed tomography (XCT), and ultrasound imaging (B-mode and IVUS) for measuring the lumen diameters of blood vessels. An image fusion method also was developed to improve these measurements. The images were acquired from a realistic phantom mimicking normal vessels of known internal diameters. After acquisition, the multimodal images were coregistered, by manual alignment of fiducial markers and then by automatic maximization of mutual information. The fusion method was performed by means of a fuzzy logic modeling approach followed by a combination process based on possibilistic logic. The data showed (i) the good geometric accuracy of XCT compared to the other methods for all studied diameters; and (ii) the good results of fused images compared to single modalities alone. For XCT, the error varied from 1.1% to 9.7%, depending on the vessel diameter that ranged from 0.93 to 6.24 mm. MRI-IVUS fusion allowed variability of measurements to be reduced up to 78%. To conclude, this work underlined both the usefulness of the vascular phantom as a validation tool and the utility of image fusion in the vascular context. Future work will consist of studying pathological vessel shapes, image artifacts and partial volume effect correction.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".