Phase Aberration Estimation in Synthetic Transmit Aperture Ultrasound Imaging and Its Application to Estimating Sound Speed
Notice bibliographique
Résumé
Despite the broad application of ultrasound imaging in modern diagnostic modalities, it often suffers from suboptimal image quality. Phase aberration is one of the main contributors to image degradation, and it appears as poor contrast, poor lateral resolution, and fill-in into hypoechoic regions due to the degraded beam-focusing quality. Image reconstruction is usually performed under the assumption of a homogeneous medium. Nonetheless, in the presence of spatial sound-speed heterogeneity, this hypothesis is no longer valid and leads to errors in the estimated echo arrival time. This dissertation investigates phase aberration estimation methods in synthetic transmit aperture ultrasound imaging (STA) and its application in estimating the speed map of the medium. STA Radio-frequency (RF) data were simulated and also acquired in experiments. However, the signal-to-noise ratio (SNR) of STA signals was much lower than that in B-mode. Therefore, we first developed a Filtered-Normalized-Cross-Correlation (F-NCC) method to estimate the phase aberration in noisy STA data. A 2D filter was applied in the temporal and spatial frequency domain to reduce the noise and off-axis signals, and its performance was validated with both simulation and experiment data. Then we derived an equation to relate the phase aberration to the average speed at a point in the medium. This equation was applied to estimate the average sound speed in the medium. It was demonstrated with a two-layered phantom that the average speed map can be used to estimate the local speed in layered objects, such as in the presence of subcutaneous fat and connective tissues. The image reconstructed based on the speed map across the medium had an improved quality globally. Furthermore, the local speed could be utilized as a biomarker. Phase aberration estimation and speed map methods were more successful when they were iterated. In the above studies, the focal point was selected in the first iteration, and it was not updated later. An adaptive localization method in which the focal point was updated by choosing the maximum brightness point within the selected area in each iteration was proposed. The results of both the simulation and experimental studies showed that adaptive localization improves the phase aberration estimation by 80% and the average speed estimation by 60%. In the future work, the potential to apply the phase aberration estimation method to estimate the local speed in non-layered objects was discussed.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| 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 source (Gemma direct ou Codex distillé), 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 ».