Accuracy of Position and Pose Estimates of Ultrasound Probe Relative to Bony Anatomy
Notice bibliographique
Résumé
Many procedures in orthopedic surgery rely on navigation to accurately place instrumentation. These methods include fluoroscopic, stereotactic and robotic approaches. While contemporary systems have been successful at dramatically lowering reoperation rates, they may not always be available due to their significant costs ($850,000-$1,200,000) or appropriate due to their ionizing radiation. There is an interest in developing lower-cost, radiation-free, and accurate navigation. We propose to address this issue using ultrasound measured distances to nearby bone surfaces. Distances can be processed by a state-estimation algorithm to determine the position and pose of the probe relative to a preoperative CT scan. Recent studies have shown that ultrasound can accurately identify bone surfaces, and algorithms based on range measurements are used for accurate localization in mobile robotics. This study evaluates the feasibility of combining these techniques to make sufficient position and pose measurements in an anatomically realistic model. We assessed position and pose estimation accuracy in a simplified 2D space using a linear 2D ultrasound (L-14W/60, Ultrasonix Corp., Canada) to image (1) an adult L4 vertebra model (∼80 mm across), and (2) a set of ‘V’ shapes characterized by their internal angle. The probe was immersed in a water bath at ten different positions and orientations and images were acquired. The US probe position was measured using an NDI Vega optical tracker (Northern Digital Inc., Canada). The images were manually segmented and distance measurements to the model surface computed. We used a multistart interior point optimization algorithm to compute a position and pose that minimized an objective function based on the average squared distances between the predicted and measured distances to the model surface. We then computed the errors relative to the position reported by the optical tracker. The mean localization error of the probe around the anatomically-realistic model was 0.7mm in translation and 2° in rotation. The algorithm obtained similar errors within a range of initial position estimates of up to 12 mm and 20° from the true position. Errors in the parametric study decreased from 1.65 mm and 3.8° for a ‘V’ angle of 90° to 0.3 mm and 0.5° for a ‘V’ angle of 150°. These accuracies suggest that the proposed technique is sufficiently accurate to justify further development. Limitations include the use of a conventional 2D US probe, the 2D plane scenario rather than the real 3D use case, and the use of manual bone segmentation as opposed to an automated algorithm. Future work is planned to address these limitations.
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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 | 0,000 |
| 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 | 0,000 |
| 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 ».