Geometrical accuracy and fusion of multimodal vascular images: A phantom study
Bibliographic record
Abstract
The aim of this work was to compare the geometrical accuracy of x-ray angiography, magnetic resonance imaging (MRI), x-ray computed tomography (XCT), and ultrasound imaging (B-mode and IVUS, or intravascular ultrasound) for measuring the lumen diameters of blood vessels. An image fusion method was also developed to improve these measurements. The images were acquired from a phantom that mimic vessels of known diameters. After acquisition, the multimodal images were coregistered by manual alignment of fiducial markers, and then by maximization of mutual information. The fusion method was performed by means of a fuzzy logic modeling approach followed by a combination process based on a possibilistic theory. The results showed (i) the better geometrical accuracy of XCT and IVUS compared to the other modalities, and (ii) the better accuracy and smaller variability of fused images compared to single modalities, with respect to most diameters investigated. For XCT, the error varied from 0.4% to 5.4%, depending on the vessel diameter that ranged from 0.93 to 6.24 mm. For IVUS, the error ranged from -0.3% to 1.7% but the smallest vessel (0.93 mm) could not be investigated because of the probe size. Compared to others fusion schemes, the XCT-MRI fused images provided the best results for both accuracy (from -1.6% to 0.2% for the three largest vessels) and robustness (mean relative error of 1.9%). To conclude, this work underlined both the usefulness of the multimodality vascular phantom as a validation tool and the utility of image fusion in the vascular context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".