The theory of and practical consideration for ultrasound guided interventions: from phantom data to clinical studies
Notice bibliographique
Résumé
Cancer is one of the leading causes of death in Canada. Research in early detection is essential for improving survival rates. The current standards for diagnosis include physical examinations, chemical tests, and biopsy. However, these tests are inaccurate and invasive. There is a need for a more accurate and less invasive diagnostic tool. To address this, Temporal Enhanced Ultrasound (TeUS) was developed. \n \nTeUS is a method of non-invasive imaging based on the temporal response of the tissue to ultrasound irradiation. Previous studies have shown its effectiveness for in vivo and ex vivo classification of prostate cancer. Additional studies investigated the physical phenomenon of TeUS and demonstrated that the tissue response to physiological micro-vibrations recorded as a time series were the basis for tissue classification. This hypothesis was later validated through a series of simulations and tissue-mimicking phantom experiments. \n \nDespite the clinical success of TeUS, one underlying issue is having a controlled imaging environment with standard and repeatable micro-vibrations. Additionally, specialized ultrasound equipment is required for acquisition. This thesis aims at addressing these challenges. \n \nFirst, I introduced a new method of TeUS acquisition by incorporating changes to the imaging focal point in a time-dependent manner. I built 9 tissue-mimicking phantoms that differed in scatterer size and elasticity, collected TeUS, and used machine learning models to classify the phantoms. These results demonstrated the effectiveness of modifying the imaging focal point during acquisition for classification of phantoms. Second, I introduced two new methods of post-processing to further enhance ultrasound time series analysis. The first method is used to accommodate the changes to the imaging focal point, while the second method is a post-acquisition technique to create time series from a single ultrasound frame. These methods were evaluated using phantom experiments. Lastly, I demonstrated the feasibility of creating a time series from a single ultrasound frame using data collected from prostate cancer biopsy. A deep learning model was trained and results were compared to classification using traditional TeUS. The results obtained in this thesis may be useful for further improving the clinical translation of Temporal Enhanced Ultrasound for cancer diagnosis.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».