Neurosurgical simulation by interactive computer graphics on iPad

  • Keisuke Maruyama
  • Taichi Kin
  • Toki Saito
  • Shinya Suematsu
  • Miho Gomyo
  • Akio Noguchi
  • Motoo Nagane
  • Yoshiaki Shiokawa
Original Article

Abstract

Purpose

Presurgical simulation before complicated neurosurgery is a state-of-the-art technique, and its usefulness has recently become well known. However, simulation requires complex image processing, which hinders its widespread application. We explored handling the results of interactive computer graphics on the iPad tablet, which can easily be controlled anywhere.

Methods

Data from preneurosurgical simulations from 12 patients (4 men, 8 women) who underwent complex brain surgery were loaded onto an iPad. First, DICOM data were loaded using Amira visualization software to create interactive computer graphics, and ParaView, another free visualization software package, was used to convert the results of the simulation to be loaded using the free iPad software KiwiViewer.

Results

The interactive computer graphics created prior to neurosurgery were successfully displayed and smoothly controlled on the iPad in all patients. The number of elements ranged from 3 to 13 (mean 7). The mean original data size was 233 MB, which was reduced to 10.4 MB (4.4 % of original size) after image processing by ParaView. This was increased to 46.6 MB (19.9 %) after decompression in KiwiViewer. Controlling the magnification, transfer, rotation, and selection of translucence in 10 levels of each element were smoothly and easily performed using one or two fingers. The requisite skill to smoothly control the iPad software was acquired within 1.8 trials on average in 12 medical students and 6 neurosurgical residents.

Conclusions

Using an iPad to handle the result of preneurosurgical simulation was extremely useful because it could easily be handled anywhere.

Keywords

Interactive computer graphics iPad Neurosurgery  Simulation 

Supplementary material

Supplementary material 1 (mpg 54672 KB)

References

  1. 1.
    Kamada K, Todo T, Morita A, Masutani Y, Aoki S, Ino K, Kawai K, Kirino T (2005) Functional monitoring for visual pathway using real-time visual evoked potentials and optic-radiation tractography. Neurosurgery 57(1 Suppl):121–127Google Scholar
  2. 2.
    Kamada K, Sawamura Y, Takeuchi F, Kawaguchi H, Kuriki S, Todo T, Morita A, Masutani Y, Aoki S, Kirino T (2005) Functional identification of the primary motor area by corticospinal tractography. Neurosurgery 56(1 Suppl):98–109Google Scholar
  3. 3.
    Kin T, Nakatomi H, Shojima M, Tanaka M, Ino K, Mori H, Kunimatsu A, Oyama H, Saito N (2012) A new strategic neurosurgical planning tool for brainstem cavernous malformations using interactive computer graphics with multimodal fusion images. J Neurosurg 117:78–88PubMedCrossRefGoogle Scholar
  4. 4.
    Kin T, Oyama H, Kamada K, Aoki S, Ohtomo K, Saito N (2009) Prediction of surgical view of neuromuscular decompression using interactive computer graphics. Neurosurgery 65:121–129 Google Scholar
  5. 5.
    Kin T, Shin M, Oyama H, Kamada K, Kunimatsu A, Momose T, Saito N (2011) Impact of multiorgan fusion imaging and interactive 3-dimensional visualization for intraventricular neuroendoscopic surgery. Neurosurgery 69(ONS Suppl 1):40–48Google Scholar
  6. 6.
    Takai K, Kin T, Oyama H, Iijima A, Shojima M, Nishido H, Saito N (2011) The use of 3D computer graphics in the diagnosis and treatment of spinal vascular malformations. J Neurosurg Spine 15:654–659PubMedCrossRefGoogle Scholar
  7. 7.
    George P, Dumenco L, Dollase R, Taylor JS, Wald HS, Reis SP (2013) Introducing technology into medical education: two pilot studies. Patient Educ Couns 93(3):522–524. doi:10.1016/j.pec.2013.04.018 PubMedCrossRefGoogle Scholar
  8. 8.
    Luo N, Chapman CG, Patel BK, Woodruff JN, Arora VM (2013) Expectations of iPad use in an internal medicine residency program: Is it worth the “hype”? J Med Internet Res 15(5):e88. doi:10.2196/jmir.2524 PubMedCrossRefPubMedCentralGoogle Scholar
  9. 9.
    Ruparel RK, Brahmbhatt RD, Dove JC, Hutchinson RC, Stauffer JA, Bowers SP, Richie E, Lannen AM, Thiel DD (2014) “iTrainers”—Novel and inexpensive alternatives to traditional laparoscopic box trainers. Urology 83(1):116–120. doi:10.1016/j.urology.2013.09.030 PubMedCrossRefGoogle Scholar
  10. 10.
    De Oliveira GS Jr, Glassenberg R, Chang R, Fitzgerald P, RJ M (2013) Virtual airway simulation to improve dexterity among novices performing fibreoptic intubation. Anaesthesia 68:1053–1058PubMedCrossRefGoogle Scholar
  11. 11.
    Biddiscombe J, Geveci B, Martin K, Moreland K, Thompson D (2007) Time dependent processing in a parallel pipeline architecture. IEEE Trans Vis Comput Graph 13(6):1376–1383. doi:10.1109/TVCG.2007.70600 PubMedCrossRefGoogle Scholar
  12. 12.
    Biddiscombe J, Soumagne J, Oger G, Guibert D, Piccinali JG (2012) Parallel computational steering for HPC applications using HDF5 files in distributed shared memory. IEEE Trans Vis Comput Graph 18(6):852–864. doi:10.1109/TVCG.2012.63 PubMedCrossRefGoogle Scholar

Copyright information

© CARS 2014

Authors and Affiliations

  • Keisuke Maruyama
    • 1
  • Taichi Kin
    • 2
  • Toki Saito
    • 3
  • Shinya Suematsu
    • 1
  • Miho Gomyo
    • 4
  • Akio Noguchi
    • 1
  • Motoo Nagane
    • 1
  • Yoshiaki Shiokawa
    • 1
  1. 1.Department of NeurosurgeryKyorin University School of MedicineTokyoJapan
  2. 2.Department of NeurosurgeryThe University of Tokyo HospitalTokyoJapan
  3. 3.Department of Clinical Information EngineeringThe University of Tokyo Graduate School of EngineeringTokyoJapan
  4. 4.Department of RadiologyKyorin University School of MedicineTokyoJapan

Personalised recommendations