Virtual Reality Neurosurgical Simulation using Computer Graphics Taichi Kin, M.D., Ph.D. 1, Masaaki Shojima, M.D., Ph.D. 1,

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1 Virtual Reality Neurosurgical Simulation using Computer Graphics Taichi Kin, M.D., Ph.D. 1, Masaaki Shojima, M.D., Ph.D. 1, Masanori Yoshino, M.D. 1, Daichi Nakagawa, M.D. 1, Shunya Hanakita, M.D. 1, Akitake Mukasa, M.D., Ph.D. 1, Hideaki Imai, M.D., Ph.D. 1, Masahiro Shin, M.D., Ph.D. 1, Hirofumi Nakatomi, M.D., Ph.D. 1, Hiroshi Oyama M.D., Ph.D. 2, and Nobuhito Saito, M.D., Ph.D. 1 1 Department of Neurosurgery, The University of Tokyo Graduate School of Medicine, 2 Department of Clinical Information Engineering, The University of Tokyo Graduate School of Medicine Medical imaging technology has exhibited rapid progress. Various sequences modalities exist with respect to computed tomography CT, magnetic resonance imaging MRI, cerebral angiography and nuclear medicine examinations. In addition, the development of brain functional imaging, including diffusion tensor tractography, functional MRI and computational fluid dynamics studies, is remarkable. Although these different modalities can be fused to perform three dimensional 3D visualization, image processing methods continue to have many issues that need be solved. In this report, the fundamental knowledge, principles, construction methods, usefulness, problems and future trends of imaging are reviewed based on the experience of 450 examples of neurosurgical simulation using 3D fusion images in our facility. Received February 15, 2013 accepted February 20, 2013 Key words virtual reality, computer graphics, simulation, fusion image Jpn J Neurosurg Tokyo , 2013 Computed tomography CT magnetic resonance imaging MRI MRI functional MRI fmri Address reprint requests to Taichi Kin, M.D., Ph.D., Department of Neurosurgery, The University of Tokyo, 7 3 1, Hongo, Bunkyo ku, Tokyo , Japan

2 thin slice 2 2 Avizo Visualization Science Group Bordeaux France fmri 1 Fig. 1A CT Jpn J Neurosurg VOL. 22 NO

DICOM data Registration Segmentation Rendering Other image analysis data Other CG techniques A Fusion 3D image B C D E F Fig. 1 A A schematic drawing of the fusion 3D image construction process.

3 DICOM data Registration Segmentation Rendering Other image analysis data Other CG techniques A Fusion 3D image B C D E F Fig. 1 A A schematic drawing of the fusion 3D image construction process. B Neuroendoscopic surgical simulation using 3D fusion image of meningioma recurrence. C Gamma knife surgical planning for an arteriovenous malformation using 3D fusion image. The corticospinal tract and optic radiation were described using DTT. Green and yellow planning areas being irradiated, purple hematoma. D Simulation using 3D fusion images of glioma. The grade of malignancy of the tumor is three dimensionally displayed using PET data. Light blue area of activation of fmri, green corticospinal tract determined using DTT, yellow arcuate fiber identified with DTT. E Virtual cerebellar retraction with left hemifacial spasm. F A space time 3D fusion image with fluid dynamic changes using phase contrast MR angiography of the nidus of a right temporal lobe arteriovenous malformation. The portion with abundant blood flow in the nidus is shown by red. Pink artery, light blue vein. 3D three dimensional, CG computer graphics, DICOM digital imaging and communication in medicine, DTT diffusion tensor tractography, fmri functional magnetic resonance imaging, MR magnetic resonance, PET positron emission tomography ダリングにはサーフェスレンダリング法とボリューム面のコントラスト比が大きい場合は明瞭な画像が得られレンダリング法の 2 つが代表的であるサーフェスレンるがそうでない場合は画像処理の際に三次元モデダリング法とは標的組織の閾値を選択してポリゴンにルの一部を失うことになるボリュームレンダリング法変換し三次元立体を作成する方法である組織の境界表とは原則として元画像データのすべての情報を三次元化 506 脳外誌 22 巻 7 号 2013 年 7 月

4 Fig. 1B CT T1 fast imaging steady state acquisition FIESTA 3DCG Maya, Autodesk, Inc., California X 2 64 Fig. 1C Fig. 1D DICOM Avizo DICOM dtv fmri Dr. View Avizo 4 52 Fig. 1E Jpn J Neurosurg VOL. 22 NO

5 Fig. 1F phase contrast MR angiography feeder Chang W, Loecher MW, Wu Y, Niemann DB, Ciske B, Aagaard Kienitz B, Kecskemeti S, Johnson KM, Wieben O, Mistretta C, Turski P Hemodynamic changes in patients with arteriovenous malformations assessed using high resolution 3D radial phase contrast MR angiography. AJNR Am J Neuroradiol , Kin T, Nakatomi H, Shojima M, Tanaka M, Ino K, Mori H, Kunimatsu A, Oyama H, Saito N A new strategic neurosurgical planning tool for brainstem cavernous malformations using interactive computer graphics with multimodal fusion images. J Neurosurg , Kin T, Oyama H, Kamada K, Aoki S, Ohtomo K, Saito N Prediction of surgical view of neurovascular decompression using interactive computer graphics. Neurosurgery

6 , Kin T, Shin M, Oyama H, Kamada K, Kunimatsu A, Momose T, Saito N Impact of multiorgan fusion imaging and interactive 3 dimensional visualization for intraventricular neuroendoscopic surgery. Neurosurgery 69 ons40 48, Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging , Oishi M, Fukuda M, Hiraishi T, Yajima N, Sato Y, Fujii Y Interactive virtual simulation using a 3D computer graphics model for microvascular decompression surgery. J Neurosurg , Oishi M, Fukuda M, Ishida G, Saito A, Hiraishi T, Fujii Y Presurgical simulation with advanced 3 dimensional multifusion volumetric imaging in patients with skull base tumors. Neurosurgery , Pluim JP, Fitzpatrick JM Image registration. IEEE Trans Med Imaging , Satoh T, Onoda K, Date I Preoperative simulation for microvascular decompression in patients with idiopathic trigeminal neuralgia visualization with three dimensional magnetic resonance cisternogram and angiogram fusion imaging. Neurosurgery , Serra L, Kockro RA, Guan CG, Hern N, Lee ECK, Lee YH, Chan C, Nowinski WL Multimodal volume based tumor neurosurgery planning in the Virtual Workbench. Medical Image Computing and Computer Assisted Intervention Miccai , Studholme C, Hill DLG, Hawkes DJ An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition , Ullmann JF, Cowin G, Kurniawan ND, Collin SP A three dimensional digital atlas of the zebrafish brain. Neuroimage , Woods RP, Mazziotta JC, Cherry SR MRI PET registration with automated algorithm. J Comput Assist Tomogr , Yoshino M, Kin T, Shojima M, Nakatomi H, Oyama H, Saito N A high resolution method with increased matrix size can characterize small arteries around a giant aneurysm in three dimensions. Br J Neurosurg , CT MRI functional MRI phase contrast MRI flow dynamic simulation Jpn J Neurosurg VOL. 22 NO

Japan Academy of Health Science J Jpn Health Sci Vol.14 No

Japan Academy of Health Science J Jpn Health Sci Vol.14 No .The Journal of Japan Academy of HealthSciences,47:=Wt! p.32-39 Development of display program that visualizes the process of medical image registration Takeshi Itou1, Hiroyuki Shinoharal, Takeyuki Hashimoto