Accélération d'une approche régularisée de reconstruction en tomographie à rayons X avec réduction des artéfacts métalliques
Bibliographic record
Abstract
Resume Ce travail porte sur l'imagerie par tomographie a rayons X des vaisseaux peripheriques traites par angioplastie avec implantation d'un tuteur endovasculaire metallique. On cherche a detecter le developpement de la restenose en mesurant la lumiere du vaisseau sanguin image. Cette application necessite la reconstruction d'images de haute resolution. De plus, la presence du tuteur metallique cause l'apparition d'artefacts qui nuisent a la precision de la mesure dans les images reconstruites dans les appareils tomographiques utilises en milieu clinique. On propose donc de realiser la reconstruction a l'aide d'un algorithme axe sur la maximisation penalisee de la log-vraisemblance conditionnelle de l'image. Cet algorithme est deduit d'un modele de formation des donnees qui tient compte de la variation non lineaire de l'attenuation des photons X dans l'objet selon leur energie, ainsi que du caractere polychromatique du faisceau X. L'algorithme reduit donc effectivement les artefacts causes specifiquement par le tuteur metallique. De plus, il peut etre configure de maniere a obtenir un compromis satisfaisant entre la resolution de l'image et la variance de l'image reconstruite, selon le niveau de bruit des donnees. Cette methode de reconstruction est reconnue pour donner des images d'excellente qualite. Toutefois, le temps de calcul necessaire a la convergence de cet algorithme est excessivement long. Le but de ce travail est donc de reduire le temps de calcul de cet algorithme de reconstruction iteratif. Cette reduction passe par la critique de la formulation du probleme et de la methode de reconstruction, ainsi que par la mise en oeuvre d'approches alternatives.---------- Abstract This thesis is concerned with X-ray tomography of peripheral vessels that have undergone angioplasty with implantation of an endovascular metal stent. We seek to detect the onset of restenosis by measuring the lumen of the imaged blood vessel. This application requires the reconstruction of high-resolution images. In addition, the presence of a metal stent causes streak artifacts that complicate the lumen measurements in images obtained with the usual algorithms, like those implemented in clinical scanners. A regularized statistical reconstruction algorithm, hinged on the maximization of the conditional log-likelihood of the image, is preferable in this case. We choose a variant deduced from a data formation model that takes into account the nonlinear variation of X~photon attenuation to photon energy, as well as the polychromatic character of the X-ray beam. This algorithm effectively reduces the artifacts specifically caused by the metal structures. Moreover, the algorithm may be set to determine a good compromise between image resolution and variance, according to data noise. This reconstruction method is thus known to yield images of excellent quality. However, the runtime to convergence is excessively long. The goal of this work is to reduce the reconstruction runtime.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".