title: A Low-Cost iPhone-Assisted Augmented Reality Solution for the Localization of Intracranial Lesions.
abstract: BACKGROUND
  Precise location of intracranial lesions before surgery is important, but occasionally difficult. Modern navigation systems are very helpful, but expensive. A low-cost solution that could locate brain lesions and their surface projections in augmented reality would be beneficial. We used an iPhone to partially achieve this goal, and evaluated its accuracy and feasibility in a clinical neurosurgery setting.
  
  
  RESULTS
  We located brain lesions in 35 patients, and using an iPhone, we depicted the lesion's surface projection onto the skin of the head. To assess the accuracy of this method, we pasted computed tomography (CT) markers surrounding the depicted lesion boundaries on the skin onto 15 patients. CT scans were then performed with or without contrast enhancement. The deviations (D) between the CT markers and the actual lesion boundaries were measured. We found that 97.7% of the markers displayed a high accuracy level (D ≤ 5mm). In the remaining 20 patients, we compared our iPhone-based method with a frameless neuronavigation system. Four check points were chosen on the skin surrounding the depicted lesion boundaries, to assess the deviations between the two methods. The integrated offset was calculated according to the deviations at the four check points. We found that for the supratentorial lesions, the medial offset between these two methods was 2.90 mm and the maximum offset was 4.2 mm.
  
  
  CONCLUSIONS
  This low-cost, image-based, iPhone-assisted, augmented reality solution is technically feasible, and helpful for the localization of some intracranial lesions, especially shallow supratentorial intracranial lesions of moderate size.
authors.affiliation: Department of Neurosurgery, PLA General Hospital Hainan Branch, Sanya, Hainan, China.
authors.forename: YuanZheng
authors.initials: Y
authors.lastname: Hou
authors.affiliation: Department of Geriatric Endocrinology, PLA General Hospital, Beijing, China.
authors.forename: LiChao
authors.initials: L
authors.lastname: Ma
authors.affiliation: Department of Neurosurgery, PLA General Hospital, Beijng, China.
authors.forename: RuYuan
authors.initials: R
authors.lastname: Zhu
authors.affiliation: Department of Neurosurgery, PLA General Hospital, Beijng, China.
authors.forename: XiaoLei
authors.initials: X
authors.lastname: Chen
authors.affiliation: Department of Neurosurgery, PLA General Hospital, Beijng, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Zhang
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D001921:Brain / Q000473:pathology*; D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D038361:Neuronavigation / Q000379:methods*; D019635:Neurosurgical Procedures; D015203:Reproducibility of Results; D000068997:Smartphone; D014057:Tomography, X-Ray Computed; D055815:Young Adult