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DTSTAMP:20250110T023302Z
LOCATION:Hall C\, C Block\, Level 4
DTSTART;TZID=Asia/Tokyo:20241206T160000
DTEND;TZID=Asia/Tokyo:20241206T180000
UID:siggraphasia_SIGGRAPH Asia 2024_sess208_real_106@linklings.com
SUMMARY:Viewtify: Next-Generation Medical Image Viewer with Stereoscopic D
 isplay
DESCRIPTION:Key Events, Real-Time Live!\n\nHirofumi Seo (SCIEMENT, Inc.)\n
 \nViewtify® fully leverages a game engine and GPU to generate and render h
 igh-quality 3DCG from CT and MRI images in real time. By using stereoscopi
 c displays, it presents 3D visuals with depth information. Its high-speed 
 processing also enables the real-time display of 4D CT and 4D MRI images a
 s animated 3DCG.\n\n1 Introduction\n\n3DCG reconstruction from volumetric 
 medical data, such as CT or MRI images, is commonly used in medical practi
 ce. There are many medical imaging software for it, from free tools like O
 siriX (Pixmeo SARL, Switzerland) and 3D Slicer (open source) to commercial
  products like 3mensio (3mensio Medical Imaging B.V., Netherlands) and Mim
 ics (Materialise NV, Belgium). However, the 3D rendering techniques are of
 ten simple and outdated, making the visuals less clear. Recently, some sof
 tware like syngo.via (Siemens Healthineers, Germany) has applied global il
 lumination for volume rendering, but resolution drops during manipulation.
  Unclear shading and resolution drop during interaction make interactive o
 perations and understanding complex structures difficult.\nViewtify® (SCIE
 MENT, Inc., Japan) enables real-time 3DCG adjustments by achieving both fa
 st 3D surface mesh synthesis from CT or MRI images via thresholding and hi
 gh-quality real-time rendering. It also supports commercial glasses-free, 
 eye-sensing stereoscopic displays (ELF-SR1, ELF-SR2 (SONY, Japan), and Spa
 tialLabs View Pro (Acer Inc., Taiwan)), presenting the 3D visuals as objec
 ts with depth information.\n\n2 Technical Overview\n\nViewtify is develope
 d on Unreal Engine (Epic Games, Inc., U.S.) for high-quality real-time ren
 dering, while the 3D mesh is generated on the GPU using custom development
  with CUDA. By sharing GPU resources between them, 3D data synthesized by 
 CUDA can be directly read by Unreal Engine. For example, a mesh with about
  9.6 million triangles has been produced from 512×512x412 (about 100 milli
 on) voxels. With an NVIDIA GeForce RTX 3090, this takes about 20 milliseco
 nds, allowing for the rendering and display of 4D CT images at up to 40 fp
 s in 4K resolution, including user interface elements.\n\n3 High-Quality R
 endering\n\nViewtify leverages Unreal Engine for modern, high-quality rend
 ering, which significantly enhances the understanding of surgical anatomy 
 by emphasizing shading. While a stereoscopic display can help with depth p
 erception, exaggerated shading provides a clearer view, making modern rend
 ering techniques essential for understanding complex anatomy.\n\n4 Clinica
 l Applications\n\nViewtify is not yet a medical device, but it has facilit
 ated biventricular repair instead of univentricular repair in congenital h
 eart disease by providing a 3D understanding of intracardiac structures. I
 t has also aided in estimating endoleak types by visually analyzing their 
 location and shape after abdominal stent-graft placement. Case reports and
  retrospective studies utilizing Viewtify are emerging, particularly in ca
 rdiac surgery.\n\n5 Conclusion\n\nViewtify employs the simplest and most c
 lassic method of thresholding but can effectively generates high-quality 3
 DCG in real time.\nBy supporting stereoscopy, it also faithfully presents 
 the information in CT and MRI images. There still remains significant pote
 ntial for 3DCG to contribute to the medical field.\n\nRegistration Categor
 y: Enhanced Access, Full Access, Full Access Supporter\n\nLanguage Format:
  English Language\n\nSession Chair: Takahito Tejima (Polyphony Digital Inc
 .)
URL:https://asia.siggraph.org/2024/program/?id=real_106&sess=sess208
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