Application of Extended Reality (XR)-Assisted Computed Tomography (CT)-Guided Localization in Extracranial-intracranial (EC-IC) Bypass Surgery

Application of Extended Reality (XR)-Assisted CT-Guided Localization in Extracranial-intracranial (EC-IC) Bypass Surgery

This study will evaluate the application of a metaverse-based surgical simulation platform in extracranial-intracranial (EC-IC) bypass surgery. The study population will include patients with moyamoya disease or chronic cerebral ischemia who are scheduled to undergo EC-IC bypass surgery.

Before surgery, preoperative brain computed tomography images will be imported into the metaverse surgical simulation platform. During surgery, extended reality technology will be used to provide precise localization of the relevant vessels, including both the donor and recipient arteries.

The primary objective is to assess the localization accuracy of this platform. The study will also investigate whether this technology can facilitate a minimally invasive surgical approach and improve surgical safety.

Inclusion Criteria:

• Adult patients diagnosed with ischemic moyamoya disease, moyamoya syndrome, or chronic intracranial internal carotid artery stenosis or occlusion (chronic ICA stenosis/occlusion).
• Patients scheduled to undergo extracranial-intracranial bypass surgery (EC-IC bypass), including superficial temporal artery-middle cerebral artery bypass (STA-MCA bypass), superficial temporal artery trunk-interposition graft-middle cerebral artery bypass (STA trunk-graft-MCA bypass), or other cerebral revascularization procedures using the superficial temporal artery or another branch of the external carotid artery system (STA/ECA system) as the donor blood supply.

Exclusion Criteria:

• Presence of a contraindication to computed tomography (CT) scanning.
• Presence of a severe systemic illness that would prevent participation in subsequent study assessments, such as poorly controlled diabetes mellitus, active malignancy, or severe cardiac disease.
• The need for emergency surgery without sufficient time to complete image-based modeling and registration.
• Presence of scalp lesions, wounds, or anatomical deformities that prevent reliable surface registration.
• The XR registration result is considered unreliable by the research team and may potentially compromise surgical safety.