Clinical trials with novel fluorescence contrast agents for head and neck cancer are driving new applications for fluorescence-guided surgery. Two-dimensional fluorescence imaging systems, however, provide limited in vivo assessment capabilities to determine tumor invasion depth below the mucosal surface. Here, we investigate the use of spatial frequency domain imaging (SFDI) methods for sub-surface fluorescence in tissue-simulating oral cancer phantoms. A two-step profile-correction approach for SFDI is under development to account for the complex surface topography of the oral cavity. First, for structured-illumination estimation of the surface profile, we are evaluating gray code and phase shift profilometry methods in agar-based oral cavity phantoms to maximize resolution and minimize sensitivity to surface discontinuities. Second, for profile-correction of the diffuse reflectance, global lighting effects within the oral cavity – analogous to an integrating sphere – are modeled using a multi-bounce numerical model. Subsurface fluorescence imaging is enabled based on the variations in optical sampling depth that result from changes in spatial frequency. An analytical depth recovery approach is based on a numerical diffusion theory model for semi-infinite fluorescence slabs of variable thickness. Depth estimation is evaluated in an agar-based phantom with fluorescence inclusions of thicknesses 1-5.5 mm originating from the top surface (“iceberg model”). Future clinical studies are necessary to assess in vivo performance and intraoperative workflow.
Intraoperative characterization of blood flow and visualization of microvasculature can have a huge impact on surgical outcomes. Knowledge about vasculature can provide diagnostic leverage, reducing operating times and improving patient recovery. Currently used Doppler-based techniques suffer from various shortcomings such as poor spatial resolution, high susceptibility to motion artifacts, and the inability to detect longitudinal flows. Our aim is to develop a fast, non-invasive approach to intraoperative microvascular imaging of slow-moving blood. In this work, we present a spatio-temporal approach to detect blood flow in vessels on the order of 0.1 mm. Specifically, a speckle-variance flow processing algorithm is used to detect small changes in B-mode pixel intensity on a micro-ultrasound (μUS) system operating in the range of 22-70 MHz. Data used in this study was acquired intraoperatively for patients undergoing neurosurgical procedures. Microcirculation was clearly visible in various anatomical structures and the spatial resolution in flow detection was much superior in comparison to Doppler-based flow detection. Moreover, using infrared optical tracking (Northern Digital Inc., Waterloo, Canada), a three-dimensional reconstruction of the microvasculature was constructed. This 3D vessel map allows for better visualization of the vasculature in the surgical cavity – allowing surgeons to plan their incisions, minimizing blood loss and potentially improving patient outcomes. To our knowledge, this is the first implementation of a three-dimensional, intraoperative microcirculation imaging technique using statistical and optical methods, alongside a non-Doppler high frequency ultrasound.
Cranial neurosurgical procedures are especially delicate considering that the surgeon must localize the subsurface anatomy with limited exposure and without the ability to see beyond the surface of the surgical field. Surgical accuracy is imperative as even minor surgical errors can cause major neurological deficits. Traditionally surgical precision was highly dependent on surgical skill. However, the introduction of intraoperative surgical navigation has shifted the paradigm to become the current standard of care for cranial neurosurgery.
Intra-operative image guided navigation systems are currently used to allow the surgeon to visualize the three-dimensional subsurface anatomy using pre-acquired computed tomography (CT) or magnetic resonance (MR) images. The patient anatomy is fused to the pre-acquired images using various registration techniques and surgical tools are typically localized using optical tracking methods. Although these techniques positively impact complication rates, surgical accuracy is limited by the accuracy of the navigation system and as such quantification of surgical error is required. While many different measures of registration accuracy have been presented true navigation accuracy can only be quantified post-operatively by comparing a ground truth landmark to the intra-operative visualization.
In this study we quantified the accuracy of cranial neurosurgical procedures using a novel optical surface imaging navigation system to visualize the three-dimensional anatomy of the surface anatomy. A tracked probe was placed on the screws of cranial fixation plates during surgery and the reported position of the centre of the screw was compared to the co-ordinates of the post-operative CT or MR images, thus quantifying cranial neurosurgical error.
Computer-assisted navigation is used by surgeons in spine procedures to guide pedicle screws to improve placement
accuracy and in some cases, to better visualize patient’s underlying anatomy. Intraoperative registration is performed to
establish a correlation between patient’s anatomy and the pre/intra-operative image. Current algorithms rely on seeding
points obtained directly from the exposed spinal surface to achieve clinically acceptable registration accuracy. Registration
of these three dimensional surface point-clouds are prone to various systematic errors. The goal of this study was to
evaluate the robustness of surgical navigation systems by looking at the relationship between the optical density of an
acquired 3D point-cloud and the corresponding surgical navigation error. A retrospective review of a total of 48
registrations performed using an experimental structured light navigation system developed within our lab was conducted.
For each registration, the number of points in the acquired point cloud was evaluated relative to whether the registration
was acceptable, the corresponding system reported error and target registration error. It was demonstrated that the number
of points in the point cloud neither correlates with the acceptance/rejection of a registration or the system reported error.
However, a negative correlation was observed between the number of the points in the point-cloud and the corresponding
sagittal angular error. Thus, system reported total registration points and accuracy are insufficient to gauge the accuracy
of a navigation system and the operating surgeon must verify and validate registration based on anatomical landmarks
prior to commencing surgery.
Computer-assisted navigation (CAN) may guide spinal surgeries, reliably reducing screw breach rates. Definitions of
screw breach, if reported, vary widely across studies. Absolute quantitative error is theoretically a more precise and
generalizable metric of navigation accuracy, but has been computed variably and reported in fewer than 25% of clinical
studies of CAN-guided pedicle screw accuracy. We reviewed a prospectively-collected series of 209 pedicle screws
placed with CAN guidance to characterize the correlation between clinical pedicle screw accuracy, based on postoperative
imaging, and absolute quantitative navigation accuracy. We found that acceptable screw accuracy was
achieved for significantly fewer screws based on 2mm grade vs. Heary grade, particularly in the lumbar spine. Inter-rater
agreement was good for the Heary classification and moderate for the 2mm grade, significantly greater among
radiologists than surgeon raters. Mean absolute translational/angular accuracies were 1.75mm/3.13° and 1.20mm/3.64°
in the axial and sagittal planes, respectively. There was no correlation between clinical and absolute navigation accuracy,
in part because surgeons appear to compensate for perceived translational navigation error by adjusting screw
medialization angle. Future studies of navigation accuracy should therefore report absolute translational and angular
errors. Clinical screw grades based on post-operative imaging, if reported, may be more reliable if performed in multiple
by radiologist raters.
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