Enhanced navigation using pre-operative multi-modal images (fMRI, MRI-DTI) can provide useful information during tumor resection. However, brain shift can produce non-linear changes in the anatomy that will induce inaccuracies of conventional rigid registration techniques (used by current commercial neurosurgery navigation systems).
Modeling the behavior of the brain remains a key issue in providing a priori
knowledge for image-guided surgery. Sophisticated models of brain tissue undergoing surgery are presented and validated in Miga et al. (2000)
and Platenik et al. (2002)
. However, a practical difficulty of these models is the extensive time necessary to mesh the brain and solve the problem, which is too long for intra-operative purposes. Similar approaches have investigated the use of brain biomechanical models updated during neurosurgery, using brain surface measurements based on laser range scanner (Miga et al. 2003
) or stereo vision system based (Skrinjar, Nabavi, and Duncan 2002
The use of intraoperative ultrasound in order to provide data that could be used to update pre-operative models to account for brain shift has been also investigated. (Dey et al. 2002
; Gobbi and Peters 2003
) have utilized a tracked, free-hand ultrasound probe. Pennec, Cachier, and Ayache (2003)
demonstrated a system that utilizes full-volume, intensity-based registration and 3D ultrasound, rather than the landmark-based methods discussed above.
These studies show that several intraoperative imaging modalities have the potential to accurately measure brain deformation, but that further study is needed. Methods that require the identification of landmarks in the intraoperative image need to overcome the difficulty of robust landmark detection in those images. Also, further evaluation in the operating room is needed to determine how well these methods can capture, and adjust for, true brain deformation, as opposed to the mechanically-induced simulations that have been investigated thus far.
An interesting aspect is how our mathematical model for DTI reorientation compares with the intra-operative DTI acquired at 0.5T. Our group has demonstrated the feasibility of acquiring intra-operative diffusion weighted imaging in the 0.5 T MRI (Mamata et al. 2001
). However, for neurosurgical procedures, this is impractical due to the long acquisition time (scan time per slice 94 seconds for diffusion tensor imaging, and 46 seconds for diffusion trace imaging), and poor spatial resolution of the diffusion weighted images (rectangular FOV = 260×195 mm; effective slice thickness = 7 mm; slice gap = 3mm; matrix size = 128 × 48). Therefore, for our prospective study we did not have diffusion weighted intra-operative images of the patients. Nevertheless, future studies will be performed on animals and phantoms to compare the results of intra-operative DTI with our mathematical estimation.
An important aspect is the way multi-modal imaging information is presented during tumor resection to neurosurgeon. Relevant fiber tracts for each patient are displayed during tumor resection, along with information about the fMRI activation areas. Because the DTI data is also non-rigidly registered with the brain anatomy during the tumor resection, extracted fiber tracts correspond precisely to the brain changes induced by the surgery. And our system works effectively even if some parts of a fiber tract system (such as visual pathway) were removed during the surgical procedure, or in a case where fiber tracts become visible after resection that were not before the resection (due to compression).
The proposed solution fits well within the time constraints imposed by neurosurgery at the MRT. An initial SPGR scan is acquired before the craniotomy (scanning time is approximately 12 minutes). The first rigid registration between the pre-operative T1w MRI and the first intra-operative SPGR is performed, and an affine transformation is estimated. Less than 30 seconds are needed to perform this task on a conventional workstation. The other pre-operative images (T2, fMRI, DTI) are also aligned with the intra-operative SPGR based on the estimated affine transformation (in approximately 30 seconds on a conventional workstation). At this point, neurosurgeons will express an interest in having the fMRI and DTI information during the navigation, to decide about the best strategy for the craniotomy.
As the surgery progresses (depending on the tumor histology and location), brain deformation inevitably occurs, and the initial alignment becomes inaccurate. A new SPGR scan is acquired, typically after 45 minutes. The pre-operative T1w MRI scan is non-rigidly registered with the intra-operative scan, using the grid-computing architecture previously presented, and the results are available in less than 5 minutes. The other pre-operative datasets (T2w, fMRI, DTI) are also aligned with the intra-operative brain anatomy in less than 1 minute on a conventional workstation. The multi-modal images are registered and available for navigation in less than 7 minutes from the scan acquisition time. At this point, the neurosurgeons are interested in the fiber tracts that can be found in the vicinity of the tumor. Towards the end of neurosurgery, additional intra-operative SPGR scans are often acquired, typically every 30 minutes. The non-rigid registration process is performed for each new SPGR scan, and hence, the time constraints are extremely significant.
An interesting question is whether the introduced technology could potentially improve the patients outcome, by reducing the tumor residual, while avoiding clinical deficits. Our group has recently published a study that assesses the main variables that affect the complete magnetic resonance (MR) imaging–guided resection of supratentorial low-grade gliomas (Talos et al. 2006
). Of all the variables assessed individually in the univariate analyses, 11 were found to be significantly associated with incomplete tumor resection (). Among the tumor characteristics, an ill defined tumor margin on T2-weighted MR images, LGO or low-grade mixed oligoastrocytoma histopathologic tumor type (ie, both types appear to be more difficult to resect than LGA), and large tumor volume were found to be associated with incomplete resection. Furthermore, tumor involvement of the following functionally critical structures led to incomplete resection: corpus callosum, CST, insular lobe, middle cerebral artery, primary motor cortex, optic radiation, visual cortex, and basal ganglia (one-sided P < .05 for all correlations).Therefore, in a future prospective study, we will employ our technology on a large number of patients that have predictors of potentially incomplete tumor resection. The intra-operative information from DTI, as provided with our novel system, can contribute to improved patients' outcome.