Development of a calibration procedure for integration of dual fluoroscopy and motion analysis
Notice bibliographique
Résumé
INTRODUCTION Accurate quantification of in vivo effects of injury on joint mechanics is essential to identify movement abnormality and related joint pathologies such as osteoarthritis. Typically used Motion Analysis (MA) technologies for studying human gait and injury suffer from soft tissue movement artifact, which may prohibit identification of small but significant changes of joint motion. High-speed dual fluoroscopy (DF) systems such as the one at the Clinical Movement Assessment Laboratory, University of Calgary, provide movement-artifact-free, high-resolution (0.30-0.44°, 0.25-0.33mm) [1], in vivo bone kinematics during dynamic activities. Such systems however, represent a trade-off between high system accuracy and limited field of view (FOV~10 inch) [2] compared to MA systems. DF systems therefore typically provide information only for a single joint while MA systems may capture the whole body. This project worked toward the integration of traditional MA and state-of-the-art DF systems to provide high accuracy joint as well as lower limb kinematics. The aim was to create hardware and software solutions for the calibration of a DF system for integration with MA systems. METHODS A Plexiglas calibration frame (48” x 22”) with an integrated steel bead grid (95 x 41, 0.125″ diameter) was designed and built. The calibration frame pattern spanned the entire frame to allow easy identification of the pattern in the small FOV of the X-ray images. A unique braille design with letters for each row and column was implemented to support simple bead location identification and future automated procedures. Three sets of column identifiers were placed at the left, center, and right regions. This pattern was glued into the calibration frame using 0.125 diameter spherical steel beads. The DF 3D coordinates were determined by imaging a custom calibration cube and using a modified direct linear transform [3]. The calibration frame was placed on top of the treadmill and images were acquired by the DF system. A MATLAB program was developed to process the calibration frame images. A Hough Transform-based circle detection function was used for digitizing the beads in both images. The user then identified the bead ID’s in the X-ray images. Combining the X-ray image bead locations, the DF 3D coordinate system, and the calibration frame’s physical parameters, the planar equation for the treadmill location can be computed. RESULTS Figure 1 shows an image of the calibration device positioned on the treadmill, as well as the resulting X-ray images. The braille pattern was successful in allowing the user to identify the pattern and its beads. DISCUSSION AND CONCLUSIONS The calibration frame developed in provides information of the spatial location of the instrumented treadmill. This is instrumental for integrating the DF and MA systems. Without this calibration device, all joint movements are observed as floating in 3D space and information about the joint’s interaction with the ground is not accessible. Further, without systems integration, no knowledge is available for the interaction of multiple joints of the lower limbs, which contains critical information for biomechanical investigations of injury and disease. Future developments based on these methods will provide the planar equations of the treadmill to provide full systems integration.
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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 | 0,001 | 0,000 |
| 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 | 0,000 |
| 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 ».