Satoru Morita1, Kazufumi Suzuki2, Takahiro Yamamoto2, Motoki Kunihara3, Hiroyuki Hashimoto3, Kayo Ito4, Shuhei Fujii4, Jun Ohya4, Ken Masamune5, Shuji Sakai2. 1. Department of Diagnostic Imaging and Nuclear Medicine, Tokyo Women's Medical University Hospital, Shinjuku-ku, Japan. i@imodey.com. 2. Department of Diagnostic Imaging and Nuclear Medicine, Tokyo Women's Medical University Hospital, Shinjuku-ku, Japan. 3. Department of Radiological Service, Tokyo Women's Medical University Hospital, Shinjuku-ku, Tokyo, Japan. 4. Department of Modern Mechanical Engineering, Waseda University, Shinjuku-ku, Japan. 5. Institute of Advanced BioMedical Engineering and Science, Tokyo Women's Medical University, Shinjuku-ku, Japan.
Abstract
PURPOSE: To develop and assess the accuracy of a mixed reality (MR) needle guidance application on smartglasses. MATERIALS AND METHODS: An MR needle guidance application on HoloLens2, without pre-procedural CT image reconstruction or import by manually matching the spatial and MR coordinate systems, was developed. First, the accuracy of the target locations in the image overlay at 63 points arranged on a 45 × 35 × 21 cm box and needle angles from 0° to 80°, placed using the MR application, was verified. The needle placement errors from 12 different entry points in a phantom by seven operators (four physicians and three non-physicians) were compared using a linear mixed model between the MR guidance and conventional methods using protractors. RESULTS: The average errors of the target locations and needle angles placed using the MR application were 5.9 ± 2.6 mm and 2.3 ± 1.7°, respectively. The average needle insertion error using the MR guidance was slightly smaller compared to that using the conventional method (8.4 ± 4.0 mm vs. 9.6 ± 5.1 mm, p = 0.091), particularly in the out-of-plane approach (9.6 ± 3.5 mm vs. 12.3 ± 4.6 mm, p = 0.003). The procedural time was longer with MR guidance than with the conventional method (412 ± 134 s vs. 219 ± 66 s, p < 0.001). CONCLUSION: MR needle guidance without pre-procedural CT image import is feasible when matching coordinate systems, and the accuracy of needle insertion is slightly better than that of the conventional method.
PURPOSE: To develop and assess the accuracy of a mixed reality (MR) needle guidance application on smartglasses. MATERIALS AND METHODS: An MR needle guidance application on HoloLens2, without pre-procedural CT image reconstruction or import by manually matching the spatial and MR coordinate systems, was developed. First, the accuracy of the target locations in the image overlay at 63 points arranged on a 45 × 35 × 21 cm box and needle angles from 0° to 80°, placed using the MR application, was verified. The needle placement errors from 12 different entry points in a phantom by seven operators (four physicians and three non-physicians) were compared using a linear mixed model between the MR guidance and conventional methods using protractors. RESULTS: The average errors of the target locations and needle angles placed using the MR application were 5.9 ± 2.6 mm and 2.3 ± 1.7°, respectively. The average needle insertion error using the MR guidance was slightly smaller compared to that using the conventional method (8.4 ± 4.0 mm vs. 9.6 ± 5.1 mm, p = 0.091), particularly in the out-of-plane approach (9.6 ± 3.5 mm vs. 12.3 ± 4.6 mm, p = 0.003). The procedural time was longer with MR guidance than with the conventional method (412 ± 134 s vs. 219 ± 66 s, p < 0.001). CONCLUSION: MR needle guidance without pre-procedural CT image import is feasible when matching coordinate systems, and the accuracy of needle insertion is slightly better than that of the conventional method.
Authors: Dilara J Long; Ming Li; Quirina M B De Ruiter; Rachel Hecht; Xiaobai Li; Nicole Varble; Maxime Blain; Michael T Kassin; Karun V Sharma; Shawn Sarin; Venkatesh P Krishnasamy; William F Pritchard; John W Karanian; Bradford J Wood; Sheng Xu Journal: Cardiovasc Intervent Radiol Date: 2021-01-06 Impact factor: 2.740
Authors: Jacob T Gibby; Samuel A Swenson; Steve Cvetko; Raj Rao; Ramin Javan Journal: Int J Comput Assist Radiol Surg Date: 2018-06-22 Impact factor: 2.924
Authors: Rachel Hecht; Ming Li; Quirina M B de Ruiter; William F Pritchard; Xiaobai Li; Venkatesh Krishnasamy; Wael Saad; John W Karanian; Bradford J Wood Journal: Cardiovasc Intervent Radiol Date: 2020-01-08 Impact factor: 2.740