Multicenter assessment of augmented reality registration methods for image-guided interventions.
Radiol Med
; 127(8): 857-865, 2022 Aug.
Article
en En
| MEDLINE
| ID: mdl-35737194
ABSTRACT
PURPOSE:
To evaluate manual and automatic registration times and registration accuracies on HoloLens 2 for aligning a 3D CT phantom model onto a CT grid, a crucial step for intuitive 3D navigation during CT-guided interventions; to compare registration times between HoloLens 1 and 2.METHODS:
Eighteen participants in various stages of clinical training across two academic centers performed registration of a 3D CT phantom model onto a CT grid using HoloLens 2. Registration times and accuracies were compared among different registration methods, clinical experience levels, and consecutive attempts. Registration times were also compared retrospectively to prior HoloLens 1 results.RESULTS:
Mean aggregate manual registration times were 27.7 s, 24.3 s, and 72.8 s for one-handed gesture, two-handed gesture, and Xbox controller, respectively; mean automatic registration time was 5.3 s (ANOVA p < 0.0001). No significant difference in registration times was found among attendings, residents and fellows, and medical students (p > 0.05). Significant improvements in registration times were detected across consecutive attempts using hand gestures (p < 0.01). Compared to prior HoloLens 1 data, hand gesture registration was 81.7% faster with HoloLens 2 (p < 0.05). Registration accuracies were not significantly different across manual registration methods, measuring at 5.9 mm, 9.5 mm, and 8.6 mm with one-handed gesture, two-handed gesture, and Xbox controller, respectively (p > 0.05).CONCLUSIONS:
Manual registration times decreased significantly on HoloLens 2, approaching those of automatic registration and outperforming Xbox controller registration. Fast, adaptive, and accurate registration of holographic models of cross-sectional imaging is paramount for the implementation of augmented reality-assisted 3D navigation during CT-guided interventions.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Cirugía Asistida por Computador
/
Realidad Aumentada
Tipo de estudio:
Guideline
/
Observational_studies
Límite:
Humans
Idioma:
En
Revista:
Radiol Med
Año:
2022
Tipo del documento:
Article
País de afiliación:
Estados Unidos