Kinetic analysis and inter-subject registration of brain PET images
Bibliographic record
Abstract
Positron emission tomography (PET) imaging is becoming increasingly popular for understanding brain function.This thesis addresses two problems related to PET images: binding potential (BP) computation and pairwise PET image registration.We first investigate the influence of several computational choices on the calculation of binding potential maps in brain PET.Our work uses simulated data and allows us to provide some benchmarks for the choices to make for BP computation, which is an important step towards fully automated MR independent BP estimation.We then introduce a new method for pairwise dynamic PET image registration that is derived from the 3D diffeomorphic log-demons algorithm, and demonstrate an improvement over existing methods.We also present a highresolution [ 11 C]raclopride PET template built from 35 subjects scanned on the High Resolution Research Tomograph.As this is the highest resolution PET scanner available at the time, to the best of our knowledge, this template is the best quality representation of a PET [ 11 C]raclopride image produced to date.iii ABR ÉG É L'imagerie à émission de positrons est de plus en plus utilisée pour comprendre le fonctionnement du cerveau.Ce mémoire aborde deux sujets liés à ces images: le calcul du potentiel de liaison et l'alignement de deux images.Nous étudions tout d'abord l'influence de certains choix d'implémentation sur les estimations de potentiel de liaison.Ces travaux effectués sur des données simulées nous permettent de donner des points de repère concernant les choix à faire pour calculer le potentiel de liaison, ce qui constitue un pas important vers un calcul du potentiel de liaison entièrement automatisé et indépendant d'images à résonance magnétique.Nous introduisons ensuite une nouvelle méthode pour l'alignement de deux images de tomographie à émission de positrons.Cette méthode est adaptée de l'algorithme des log-démons difféomorphiques 3D.Nous montrons que notre méthode donne de meilleurs résultats que des méthodes existantes.Nous présentons aussi un modèle de haute résolution pour l'imagerie à émission de positrons utilisant la [ 11 C]raclopride.Ce modèle est construit à partir de 35 sujets scannés sur le tomographe de recherche à haute résolution (High Resolution Research Tomograph).Comme il s'agit du tomographe de plus haute résolution disponible à ce jour, à notre connaissance, notre modèle est l'image de raclopride de plus haute résolution jamais produite.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".