This project will continue a productive academic-industrial partnership between Dartmouth and Medtronic through which Dartmouth?s intraoperative image updating techniques have already been merged with Med- tronic?s state-of-the-art S7 navigation system to produce and visualize updated MR (uMR) images concurrent with surgery. In a second funding period, we will fully validate and prospectively evaluate uMRs in open-cranial surgery via comparisons with intraoperative MR (iMR). We also propose a new direction: generating uMR to guide minimally invasive neurosurgeries as a possible low-cost, yet effective, alternative to in-bore MR- guidance (new preliminary results of image-updating in minimally-invasive deep brain stimulation indicate feas- ibility and suggest promise). While validation studies completed to date with a tracked stylus as ?ground truth? have been impressive, relatively few points are available at any given time during surgery, and tracking and feature identification/localization errors are embedded in the TRE (target registration error) results. Thus, a cornerstone of our continuation proposal is validation with iMR, which is available in Dartmouth?s Center for Surgical Innovation (CSI). Since iMR is deployed at end-of-resection to survey for residual disease in open cranial surgery and multiple iMRs are difficult to justify because of patient safety concerns, we have proposed a new large animal glioma model which has been developed successfully in our hands (we can now grow solid tumors of varying size and shape located in different intra-cranial positions) in which iMR will be acquired multiple times during a resection procedure for uMR validation. These animal studies are also ideally-suited to minimally-invasive cases because we can determine the image-updating requirements to guide the procedure as a more efficient and cost-effective alternative to iMR. CSI can accommodate these experiments, and as a result, we are in a unique position to conduct animal and human studies in the same space with the same navigation/imaging equipment for uMR validation with iMR under both open cranial and minimally invasive conditions. Based on progress to date, and these considerations, we propose technical advances that will apply image-updating to minimally-invasive neurosurgical procedures, accelerate image-updating through GPU processing, and add uMR data into the surgeon?s heads-up display; validation in large animal glioma open- cranial and minimally-invasive studies where iMR acquisitions are not limited, and during similar human brain tumor cases where iMR use is restricted; and prospective evaluation of end-of-resection surgical accuracy of procedures navigated with preoperative MR (pMR) to those where uMR is also available. By the end of the proposed 2nd funding period, we will have an uMR guidance platform that is fully validated for open cranial and minimally-invasive procedures, and will have demonstrated the extent to which it enables neurosurgeons to achieve more accurate open-cranial resections more often, and whether it is sufficiently accurate and timely for guidance of minimally-invasive neurosurgical interventions relative to in-bore MR guidance.
Through continuation of an academic-industrial partnership with Medtronic Navigation, Dartmouth?s image- updating methods for intraoperative guidance during brain tumor resection during open surgery that have been integrated with Medtronic?s commercial-grade S7 navigation system will be validated through comparisons with intraoperative MRI in large animal and human studies. These image updating methods will also be adapted to minimally-invasive neurosurgeries and evaluated for their potential to serve as a low-cost, efficient, but effective alternative to the in-bore MRI guidance commonly used during these procedures.
|Fan, Xiaoyao; Roberts, David W; Schaewe, Timothy J et al. (2017) Intraoperative image updating for brain shift following dural opening. J Neurosurg 126:1924-1933|
|Fan, Xiaoyao; Roberts, David W; Ji, Songbai et al. (2015) Intraoperative fiducial-less patient registration using volumetric 3D ultrasound: a prospective series of 32 neurosurgical cases. J Neurosurg 123:721-31|
|Ji, Songbai; Fan, Xiaoyao; Roberts, David W et al. (2014) Efficient stereo image geometrical reconstruction at arbitrary camera settings from a single calibration. Med Image Comput Comput Assist Interv 17:440-7|
|Fan, Xiaoyao; Ji, Songbai; Hartov, Alex et al. (2014) Stereovision to MR image registration for cortical surface displacement mapping to enhance image-guided neurosurgery. Med Phys 41:102302|
|Ji, Songbai; Fan, Xiaoyao; Roberts, David W et al. (2014) Cortical surface shift estimation using stereovision and optical flow motion tracking via projection image registration. Med Image Anal 18:1169-83|
|McGarry, Matthew; Johnson, Curtis L; Sutton, Bradley P et al. (2013) Including spatial information in nonlinear inversion MR elastography using soft prior regularization. IEEE Trans Med Imaging 32:1901-9|
|McGarry, M D J; Van Houten, E E W; Johnson, C L et al. (2012) Multiresolution MR elastography using nonlinear inversion. Med Phys 39:6388-96|
|Ji, Songbai; Roberts, David W; Hartov, Alex et al. (2012) Intraoperative patient registration using volumetric true 3D ultrasound without fiducials. Med Phys 39:7540-52|
|Ji, Songbai; Fan, Xiaoyao; Roberts, David W et al. (2011) Cortical surface strain estimation using stereovision. Med Image Comput Comput Assist Interv 14:412-9|