ABSTRACT
Traditional stand-alone computer-assisted surgery (CAS) systems impede the ubiquitous and simultaneous access by multiple users. With advances in computing and networking technologies, ubiquitous access to CAS systems becomes possible and promising. Based on our preliminary work, CASMIL, a stand-alone CAS server developed at Wayne State University, we propose a novel mobile CAS system, UbiCAS, which allows surgeons to retrieve, review and interpret multimodal medical images, and to perform some critical neurosurgical procedures on heterogeneous devices from anywhere at anytime. Furthermore, various optimization techniques, including caching, prefetching, pseudo-streaming-model, and compression, are used to guarantee the QoS of the UbiCAS system. UbiCAS enables doctors at remote locations to actively participate remote surgeries, share patient information in real time before, during, and after the surgery.
Subject(s)
Surgery, Computer-Assisted/instrumentation , Algorithms , Computer Communication Networks , Computer Systems , Computers , Computers, Handheld , Data Display , Equipment Design , Humans , Image Processing, Computer-Assisted , Neurosurgery/instrumentation , Neurosurgery/methods , Signal Processing, Computer-Assisted , Software , Surgery, Computer-Assisted/methods , User-Computer InterfaceABSTRACT
BACKGROUND: CASMIL aims to develop a cost-effective and efficient approach to monitor and predict deformation during surgery, allowing accurate, and real-time intra-operative information to be provided reliably to the surgeon. METHOD: CASMIL is a comprehensive Image-guided Neurosurgery System with extensive novel features. It is an integration of various modules including rigid and non-rigid body co-registration (image-image, image-atlas, and image-patient), automated 3D segmentation, brain shift predictor, knowledge based query tools, intelligent planning, and augmented reality. One of the vital and unique modules is the Intelligent Planning module, which displays the best surgical corridor on the computer screen based on tumor location, captured surgeon knowledge, and predicted brain shift using patient specific Finite Element Model. Also, it has multi-level parallel computing to provide near real-time interaction with iMRI (Intra-operative MRI). In addition, it has been securely web-enabled and optimized for remote web and PDA access. RESULTS: A version of this system is being used and tested using real patient data and is expected to be in use in the operating room at the Detroit Medical Center in the first half of 2006. CONCLUSION: CASMIL is currently under development and is targeted for minimally invasive surgeries. With minimal changes to the design, it can be easily extended and made available for other surgical procedures.
