Challenges in image-guided therapy system design

System development for image-guided therapy (IGT), or image-guided interventions (IGI), continues to be an area of active interest across academic and industry groups. This is an emerging field that is growing rapidly: major academic institutions and medical device manufacturers have produced IGT technologies that are in routine clinical use, dozens of high-impact publications are published in well regarded journals each year, and several small companies have successfully commercialized sophisticated IGT systems. In meetings between IGT investigators over the last two years, a consensus has emerged that several key areas must be addressed collaboratively by the community to reach the next level of impact and efficiency in IGT research and development to improve patient care. These meetings culminated in a two-day workshop that brought together several academic and industrial leaders in the field today. The goals of the workshop were to identify gaps in the engineering infrastructure available to IGT researchers, develop the role of research funding agencies and the recently established US-based National Center for Image Guided Therapy (NCIGT), and ultimately to facilitate the transfer of technology among research centers that are sponsored by the National Institutes of Health (NIH). Workshop discussions spanned many of the current challenges in the development and deployment of new IGT systems. Key challenges were identified in a number of areas, including: validation standards; workflows, use-cases, and application requirements; component reusability; and device interface standards. This report elaborates on these key points and proposes research challenges that are to be addressed by a joint effort between academic, industry, and NIH participants.

[1]  James F. Allen Maintaining knowledge about temporal intervals , 1983, CACM.

[2]  Jean Vanderdonckt,et al.  Analyzing Interaction in Augmented Reality Systems , 2002 .

[3]  T. Peters Image-guided surgery: From X-rays to Virtual Reality , 2001, Computer methods in biomechanics and biomedical engineering.

[4]  F. Jolesz,et al.  Survival rates in patients with low‐grade glioma after intraoperative magnetic resonance image guidance , 2005, Cancer.

[5]  N. Hata,et al.  Image-guided neurosurgery at Brigham and Women's Hospital , 2006, IEEE Engineering in Medicine and Biology Magazine.

[6]  Georg Eggers,et al.  Accuracy of image-guided implantology. , 2005, Clinical oral implants research.

[7]  Darius Burschka,et al.  Navigating inner space: 3-D assistance for minimally invasive surgery , 2005, Robotics Auton. Syst..

[8]  Richard D. Bucholz,et al.  Accurate and ergonomic method of registration for image-guided neurosurgery , 1994, Photonics West - Lasers and Applications in Science and Engineering.

[9]  S B Green,et al.  Randomized trial of three chemotherapy regimens and two radiotherapy regimens and two radiotherapy regimens in postoperative treatment of malignant glioma. Brain Tumor Cooperative Group Trial 8001. , 1989, Journal of neurosurgery.

[10]  Andreas Raabe,et al.  Actual aspects of image-guided surgery. , 2003, Surgical technology international.

[11]  Jay B. West,et al.  Predicting error in rigid-body point-based registration , 1998, IEEE Transactions on Medical Imaging.

[12]  Daniela Gorski Trevisan,et al.  Modeling Augmented Reality System , Image Guided Surgery Case Study , 2002 .

[13]  M. Brian Blake,et al.  IGSTK: an open source software toolkit for image-guided surgery , 2006, Computer.

[14]  Daniela Gorski Trevisan,et al.  Modeling interaction for image-guided procedures , 2003, SPIE Medical Imaging.

[15]  Michael W. Vannier,et al.  The operating room and the need for an IT infrastructure and standards , 2006, International Journal of Computer Assisted Radiology and Surgery.

[16]  Peter Anderson,et al.  Method for estimating dynamic EM tracking accuracy of surgical navigation tools , 2006, SPIE Medical Imaging.

[17]  Dieter Schmalstieg,et al.  An open software architecture for virtual reality interaction , 2001, VRST '01.

[18]  S J Wigmore,et al.  The value of residual liver volume as a predictor of hepatic dysfunction and infection after major liver resection , 2005, Gut.

[19]  Ron Kikinis,et al.  Workflow modeling and analysis of computer guided prostate brachytherapy under MR imaging control. , 2004, Studies in health technology and informatics.

[20]  Greg Welch,et al.  Motion Tracking: No Silver Bullet, but a Respectable Arsenal , 2002, IEEE Computer Graphics and Applications.

[21]  Raúl San José Estépar,et al.  EUS with CT improves efficiency and structure identification over conventional EUS. , 2007, Gastrointestinal endoscopy.

[22]  Marlon Dumas,et al.  UML Activity Diagrams as a Workflow Specification Language , 2001, UML.

[23]  Fabio Paternò Model-Based Design and Evaluation of Interactive Applications , 2000 .

[24]  Dieter Schmalstieg,et al.  OpenTracker-an open software architecture for reconfigurable tracking based on XML , 2001, Proceedings IEEE Virtual Reality 2001.

[25]  Kevin Cleary,et al.  Workflow in interventional radiology: nerve blocks and facet blocks , 2006, SPIE Medical Imaging.

[26]  Dinggang Shen,et al.  Optimized prostate biopsy via a statistical atlas of cancer spatial distribution , 2004, Medical Image Anal..

[27]  Cristiano Paggetti,et al.  Interface Design and Evaluation for CAS Systems , 2001, MICCAI.

[28]  Richard D. Bucholz,et al.  An accurate and ergonomic method of registration for image-guided neurosurgery , 1994 .

[29]  Taku Komura,et al.  Computing inverse kinematics with linear programming , 2005, VRST '05.

[30]  Jean Vanderdonckt,et al.  Computer-Aided Design of User Interfaces III , 2002, Springer Netherlands.

[31]  Robert G. Selker,et al.  Randomized trial of three chemotherapy regimens and two radiotherapy regimens in postoperative treatment of malignant glioma , 2009 .

[32]  Guido Gerig,et al.  Determining Malignancy of Brain Tumors by Analysis of Vessel Shape , 2004, MICCAI.