====== Mixed Reality ======

{{rss>https://pubmed.ncbi.nlm.nih.gov/rss/search/1LyCSa7Du4sMC9vpA5nZxRhRyj4EzlOKvQq_jsgHFfcdPBGUCc/?limit=15&utm_campaign=pubmed-2&fc=20230505181733}}

Mixed Reality (MR) [[technology]] has opened new avenues for [[planning]], [[visualization]], and [[education]] in surgery. [[Neurosurgical disease]]s require a very clear understanding of the relationships between [[pathology]] and critical neurovascular structures. The decline in [[cadaveric dissection]]s and resource constraints has pushed [[educator]]s to find newer ways of rendering the same [[knowledge]]. The aim of the study was to determine the feasibility of employing an MR device in a high-volume center for neurosurgical [[teaching]]. The study also evaluated the results of the [[trainee]] [[experience]] in using the MR platform.

3 neurosurgical [[consultant]]s who are part of the teaching [[faculty]] were asked to facilitate the session. No prior training on utilizing the MR device was given to the [[trainee]]s. [[HoloLens]] 2 was used as the MR device. 2 [[questionnaire]]s were used to understand the [[experience]] of the trainees.

Results: 8 active neurosurgical trainees who are currently training at our institution were recruited for the purposes of this study. Despite having no prior training on an MR platform, the learning curve was short for most of the trainees. Whether MR replaces current traditional methods of teaching neuroanatomy, the response was divided among the trainees. The results of the User Experience Questionnaire (UEQ) were positive with the trainees finding the device attractive, dependable, novel, and user-friendly.

Conclusion: This study demonstrates the feasibility of using the MR platform in neurosurgery training without significant preparation requirements. This data is required to justify future investment in this technology for training institutions
((Jain S, Timofeev I, Kirollos RW, Helmy A. Use of Mixed Reality in Neurosurgery Training: A Single Centre Experience. World Neurosurg. 2023 May 2:S1878-8750(23)00591-0. doi: 10.1016/j.wneu.2023.04.107. Epub ahead of print. PMID: 37141939.)).
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In neurosurgery, it is important to inspect the [[spatial]] correspondence between the [[preoperative]] medical image (virtual space), and the [[intraoperative]] findings (real space) to improve the safety of the surgery. [[Navigation system]]s and related modalities have been reported as methods for matching this correspondence. However, because of the influence of the [[brain shift]] accompanying [[craniotomy]], registration [[accuracy]] is reduced. In a study, to overcome these issues, Koike et al. developed a spatially accurate [[registration]] method of medical fusion 3-dimensional [[computer graphics]] and the intraoperative [[brain surface]] photograph, and its registration accuracy was measured.

The subjects included 16 patients with [[glioma]]. Nonrigid registration using the [[landmark]]s and thin-plate spline methods was performed for the fusion 3-dimensional computer graphics and the intraoperative brain surface photograph, termed mixed-reality computer graphics. Regarding the registration accuracy measurement, the target registration error was measured by two neurosurgeons, with 10 points for each case at the midpoint of the landmarks.

The number of target registration error measurement points was 160 in the 16 cases. The target registration error was 0.72 ± 0.04 mm. Aligning the intraoperative brain surface photograph and the fusion 3-dimensional computer graphics required ∼10 minutes on average. The average number of landmarks used for alignment was 24.6.

Mixed-reality computer graphics enabled highly precise spatial alignment between the real space and virtual space. Mixed-reality computer graphics have the potential to improve the safety of the surgery by allowing complementary observation of brain surface photographs and fusion 3-dimensional computer graphics
((Koike T, Kin T, Tanaka S, Takeda Y, Uchikawa H, Shiode T, Saito T, Takami H, Takayanagi S, Mukasa A, Oyama H, Saito N. Development of Innovative Neurosurgical Operation Support Method Using Mixed-Reality Computer Graphics. World Neurosurg X. 2021 Mar 13;11:100102. doi: 10.1016/j.wnsx.2021.100102. PMID: 33898969; PMCID: PMC8059082.)).