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多中心评估用于影像引导介入的增强现实注册方法。

Multicenter assessment of augmented reality registration methods for image-guided interventions.

机构信息

Dotter Department of Interventional Radiology, Oregon Health & Science University School of Medicine, 3181 SW Sam Jackson Park Rd, Portland, OR, 97239, USA.

Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA, 19104, USA.

出版信息

Radiol Med. 2022 Aug;127(8):857-865. doi: 10.1007/s11547-022-01515-3. Epub 2022 Jun 23.

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.

摘要

目的

评估 HoloLens 2 在将 3D CT 体模与 CT 网格对齐方面的手动和自动配准时间和配准精度,这是在 CT 引导介入中进行直观 3D 导航的关键步骤;比较 HoloLens 1 和 2 之间的配准时间。

方法

来自两个学术中心不同临床培训阶段的 18 名参与者使用 HoloLens 2 将 3D CT 体模与 CT 网格进行配准。比较了不同配准方法、临床经验水平和连续尝试之间的配准时间和精度。还回顾性地比较了配准时间与之前的 HoloLens 1 结果。

结果

单手手势、双手手势和 Xbox 控制器的平均总手动配准时间分别为 27.7s、24.3s 和 72.8s;平均自动配准时间为 5.3s(ANOVA p<0.0001)。主治医生、住院医师和研究员以及医学生之间的配准时间无显著差异(p>0.05)。通过手势进行连续尝试时,配准时间显著提高(p<0.01)。与之前的 HoloLens 1 数据相比,使用 HoloLens 2 进行手势注册的速度快了 81.7%(p<0.05)。手动配准方法之间的配准精度无显著差异,分别为 5.9mm、9.5mm 和 8.6mm,单手手势、双手手势和 Xbox 控制器(p>0.05)。

结论

HoloLens 2 上的手动配准时间显著减少,接近自动配准时间,并优于 Xbox 控制器注册。快速、自适应和准确地注册横截面成像的全息模型对于在 CT 引导介入中实施增强现实辅助 3D 导航至关重要。

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