{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/132714"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/132714","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Apple vision pro for intraoperative 3D tumor visualization and surgical guidance","abstract":"This thesis investigates the feasibility of using the Apple Vision Pro (AVP) mixed-reality headset for visualizing and localizing brain tumors during neurosurgical procedures. A complete workflow was developed to overlay a CT-derived 3D tumor model onto a patient-specific head surface mesh obtained using LiDAR scanning and machine-learning–based object tracking. A 3D-printed head phantom with an embedded tumor target enabled quantitative evaluation of reconstruction fidelity and spatial anchoring accuracy. Results show that AVP can generate a consistent surface mesh of the head and maintain stable alignment of the tumor overlay under typical motion, with deviations increasing primarily in high-curvature anatomical regions or during sudden movements. Although not yet a replacement for conventional stereotactic navigation, the system demonstrates clinically promising accuracy for superficial tumor visualization and preoperative planning. These findings highlight the potential of mixed reality as an intuitive, hands-free tool for neurosurgical guidance and outline key areas for future improvement, including automated registration and enhanced tracking robustness.","abstract_html":"This thesis investigates the feasibility of using the Apple Vision Pro (AVP) mixed-reality headset for visualizing and localizing brain tumors during neurosurgical procedures. A complete workflow was developed to overlay a CT-derived 3D tumor model onto a patient-specific head surface mesh obtained using LiDAR scanning and machine-learning–based object tracking. A 3D-printed head phantom with an embedded tumor target enabled quantitative evaluation of reconstruction fidelity and spatial anchoring accuracy. Results show that AVP can generate a consistent surface mesh of the head and maintain stable alignment of the tumor overlay under typical motion, with deviations increasing primarily in high-curvature anatomical regions or during sudden movements. Although not yet a replacement for conventional stereotactic navigation, the system demonstrates clinically promising accuracy for superficial tumor visualization and preoperative planning. These findings highlight the potential of mixed reality as an intuitive, hands-free tool for neurosurgical guidance and outline key areas for future improvement, including automated registration and enhanced tracking robustness.","abstract_has_math":false,"creators":["Zhang, Zekai"],"institution":"University of Illinois Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Gruev, Viktor"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-12","date_published":"2025-12","updated_at":"2026-07-22T22:25:07Z","subjects":["Apple Vision Pro, Tumor Imaging"],"languages":["en"],"rights":["Copyright 2025 Zekai Zhang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/132714","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Gruev, Viktor"]},{"key":"dc:creator","label":"Author","values":["Zhang, Zekai"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2025-12","2025-12-12"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Apple Vision Pro, Tumor Imaging"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2025 Zekai Zhang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/132714"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This thesis investigates the feasibility of using the Apple Vision Pro (AVP) mixed-reality headset for visualizing and localizing brain tumors during neurosurgical procedures. A complete workflow was developed to overlay a CT-derived 3D tumor model onto a patient-specific head surface mesh obtained using LiDAR scanning and machine-learning–based object tracking. A 3D-printed head phantom with an embedded tumor target enabled quantitative evaluation of reconstruction fidelity and spatial anchoring accuracy. Results show that AVP can generate a consistent surface mesh of the head and maintain stable alignment of the tumor overlay under typical motion, with deviations increasing primarily in high-curvature anatomical regions or during sudden movements. Although not yet a replacement for conventional stereotactic navigation, the system demonstrates clinically promising accuracy for superficial tumor visualization and preoperative planning. These findings highlight the potential of mixed reality as an intuitive, hands-free tool for neurosurgical guidance and outline key areas for future improvement, including automated registration and enhanced tracking robustness.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-12-01","The student, Zekai Zhang, accepted the attached license on 2025-12-12 at 11:21.","The student, Zekai Zhang, submitted this Thesis for approval on 2025-12-12 at 11:24.","This Thesis was approved for publication on 2025-12-12 at 14:01.","DSpace SAF Submission Ingestion Package generated from Vireo submission #23148 on 2026-02-19 at 18:46:59"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Apple vision pro for intraoperative 3D tumor visualization and surgical guidance"]}]}],"canonical_facts":{"dc:contributor":["Gruev, Viktor"],"dc:creator":["Zhang, Zekai"],"dc:date":["2025-12","2025-12-12"],"dc:description":["This thesis investigates the feasibility of using the Apple Vision Pro (AVP) mixed-reality headset for visualizing and localizing brain tumors during neurosurgical procedures. A complete workflow was developed to overlay a CT-derived 3D tumor model onto a patient-specific head surface mesh obtained using LiDAR scanning and machine-learning–based object tracking. A 3D-printed head phantom with an embedded tumor target enabled quantitative evaluation of reconstruction fidelity and spatial anchoring accuracy. Results show that AVP can generate a consistent surface mesh of the head and maintain stable alignment of the tumor overlay under typical motion, with deviations increasing primarily in high-curvature anatomical regions or during sudden movements. Although not yet a replacement for conventional stereotactic navigation, the system demonstrates clinically promising accuracy for superficial tumor visualization and preoperative planning. These findings highlight the potential of mixed reality as an intuitive, hands-free tool for neurosurgical guidance and outline key areas for future improvement, including automated registration and enhanced tracking robustness.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2027-12-01","The student, Zekai Zhang, accepted the attached license on 2025-12-12 at 11:21.","The student, Zekai Zhang, submitted this Thesis for approval on 2025-12-12 at 11:24.","This Thesis was approved for publication on 2025-12-12 at 14:01.","DSpace SAF Submission Ingestion Package generated from Vireo submission #23148 on 2026-02-19 at 18:46:59"],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/132714"],"dc:language":["en"],"dc:rights":["Copyright 2025 Zekai Zhang"],"dc:subject":["Apple Vision Pro, Tumor Imaging"],"dc:title":["Apple vision pro for intraoperative 3D tumor visualization and surgical guidance"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:07Z"}