Plasma Anti-Glial Fibrillary Acidic Protein Autoantibody Levels during the Acute and Chronic Phases of Traumatic Brain Injury: A Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot Study.

We described recently a subacute serum autoantibody response toward glial fibrillary acidic protein (GFAP) and its breakdown products 5-10 days after severe traumatic brain injury (TBI). Here, we expanded our anti-GFAP autoantibody (AutoAb[GFAP]) investigation to the multicenter observational study Transforming Research and Clinical Knowledge in TBI Pilot (TRACK-TBI Pilot) to cover the full spectrum of TBI (Glasgow Coma Scale 3-15) by using acute (<24 h) plasma samples from 196 patients with acute TBI admitted to three Level I trauma centers, and a second cohort of 21 participants with chronic TBI admitted to inpatient TBI rehabilitation. We find that acute patients self-reporting previous TBI with loss of consciousness (LOC) (n = 43) had higher day 1 AutoAb[GFAP] (mean ± standard error: 9.11 ± 1.42; n = 43) than healthy controls (2.90 ± 0.92; n = 16; p = 0.032) and acute patients reporting no previous TBI (2.97 ± 0.37; n = 106; p < 0.001), but not acute patients reporting previous TBI without LOC (8.01 ± 1.80; n = 47; p = 0.906). These data suggest that while exposure to TBI may trigger the AutoAb[GFAP] response, circulating antibodies are elevated specifically in acute TBI patients with a history of TBI. AutoAb[GFAP] levels for participants with chronic TBI (average post-TBI time 176 days or 6.21 months) were also significantly higher (15.08 ± 2.82; n = 21) than healthy controls (p < 0.001). These data suggest a persistent upregulation of the autoimmune response to specific brain antigen(s) in the subacute to chronic phase after TBI, as well as after repeated TBI insults. Hence, AutoAb[GFAP] may be a sensitive assay to study the dynamic interactions between post-injury brain and patient-specific autoimmune responses across acute and chronic settings after TBI.

Repeatability of DTI-based skeletal muscle fiber tracking.

Diffusion tensor imaging (DTI)-based muscle fiber tracking enables the measurement of muscle architectural parameters, such as pennation angle (theta) and fiber tract length (L(ft)), throughout the entire muscle. Little is known, however, about the repeatability of either the muscle architectural measures or the underlying diffusion measures. Therefore, the goal of this study was to investigate the repeatability of DTI fiber tracking-based measurements and theta and L(ft). Four DTI acquisitions were performed on two days that allowed for between acquisition, within day, and between day analyses. The eigenvalues and fractional anisotropy were calculated at the maximum cross-sectional area of, and fiber tracking was performed in, the tibialis anterior muscle of nine healthy subjects. The between acquisitions condition had the highest repeatability for the DTI indices and the architectural parameters. The overall inter class correlation coefficients (ICC's) were greater than 0.6 for both theta and L(ft) and the repeatability coefficients were theta < 10.2 degrees and L(ft) < 50 mm. In conclusion, under the experimental and data analysis conditions used, the repeatability of the diffusion measures is very good and repeatability of the architectural measurements is acceptable. Therefore, this study demonstrates the feasibility for longitudinal studies of alterations in muscle architecture using DTI-based fiber tracking, under similar noise conditions and with similar diffusion characteristics.

An automated algorithm to improve the precision of basilar artery diameter measurements before and after subarachnoid hemorrhage-induced vasospasm in an animal model.

OBJECTIVE: Quantifying vasospasm has traditionally been performed manually, a method prone to imprecision and user bias. An alternative approach is to use computerized image analysis techniques to define and quantify the diameter of a vessel. The goal of this article is to demonstrate a novel automated vessel measurement algorithm specific to the needs of vasospasm studies and to compare it with traditional manual measurements in an animal model of vasospasm. METHODS: A total of 576 arterial diameter measurements were collected by 4 independent, blinded examiners from 24 angiograms in a rabbit subarachnoid hemorrhage (SAH) model. Measurements were taken from 3 segments of the basilar artery in anteroposterior and lateral projections, both before SAH and after SAH-induced vasospasm. Means and standard deviations of 288 manual measurements were compared with 288 automated measurements. RESULTS: The precision of automated measurements was significantly improved compared with standardized manual measurements (85.7% decrease in variation; P < .001). When using automated measurements, the precision was not affected by vessel size, but when using manual measurements, smaller arteries were less precise (P = .04). There was no significant difference in precision between 2 different contrast concentrations (P = .32). CONCLUSION: Automated measurements of basilar artery diameters are more precise than manual measurements, both before and after SAH-induced vasospasm. The variability in the manual group worsens when the artery is smaller secondary to vasospasm, indicating a need for the use of this segmentation method.

Coregistration of ultrasonography and magnetic resonance imaging with a preliminary investigation of the spatial colocalization of vascular endothelial growth factor receptor 2 expression and tumor perfusion in a murine tumor model.

We present an ultrasonography (US)-magnetic resonance imaging (MRI) coregistration technique and examine its application in a preliminary multimodal, multiparametric study in a preclinical model of breast cancer. Nine mice were injected with 67NR breast cancer cells and imaged 6 and 9 days later with 4.7 T MRI and high-frequency US. Tumor volumes from each data set were segmented independently by two investigators and coregistered using an iterative closest point algorithm. In addition to anatomic images, vascular endothelial growth factor receptor 2 (VEGFR2) distribution images from the central tumor slice using VEGFR2-targeted ultrasound contrast agent (UCA) and measurements of perfusion and extravascular-extracellular volume fraction using dynamic contrast-enhanced MRI were acquired from five mice for multiparametric coregistration. Parametric maps from each modality were coregistered and examined for spatial correlation. Average registration root mean square (RMS) error was 0.36 +/- 0.11 mm, less than approximately two voxels. Segmented volumes were compared between investigators to minimize interobserver variability; the average RMS error was 0.23 +/- 0.09 mm. In the preliminary study, VEGFR2-targeted UCA data did not demonstrate direct spatial correlation with magnetic resonance measures of vascular properties. In summary, a method for accurately coregistering small animal US and MRI has been presented that allows for comparison of quantitative metrics provided by the two modalities.

Quantitative assessment of DTI-based muscle fiber tracking and optimal tracking parameters.

Diffusion tensor imaging-based fiber tracking in skeletal muscle has been used to reconstruct and quantify muscle architecture. In addition, the consistent pattern of muscle fiber geometry enables a quantitative assessment of the fiber tracking. This work describes a method to determine the accuracy of individual muscle fiber tracts based on the location at which the fibers terminate, the fiber path, and similarity to the neighboring fibers. In addition, the effect of different stop criteria settings on this quantitative assessment was investigated. Fiber tracking was performed on the tibialis anterior muscle of nine healthy subjects. Complete fiber tracts covered 89.4 +/- 9.6% and 75.0 +/- 15.2% of the aponeurosis area in the superficial and deep compartments, respectively. Applications of the method include the exclusion of erroneous fiber-tracking results, quantitative assessment of data set quality, and the assessment of fiber-tracking stop criteria.

Integrating spatially resolved three-dimensional MALDI IMS with in vivo magnetic resonance imaging.

We have developed a method for integrating three dimensional-volume reconstructions of spatially resolved matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) ion images of whole mouse heads with high-resolution images from other modalities in an animal-specific manner. This approach enabled us to analyze proteomic profiles from MALDI IMS data with corresponding in vivo data provided by magnetic resonance imaging.

Intraoperative cortical surface characterization using laser range scanning: preliminary results.

OBJECTIVE: To present a novel methodology that uses a laser range scanner (LRS) capable of generating textured (intensity-encoded) surface descriptions of the brain surface for use with image-to-patient registration and improved cortical feature recognition during intraoperative neurosurgical navigation. METHODS: An LRS device was used to acquire cortical surface descriptions of eight patients undergoing neurosurgery for a variety of clinical presentations. Textured surface descriptions were generated from these intraoperative acquisitions for each patient. Corresponding textured surfaces were also generated from each patient's preoperative magnetic resonance tomograms. Each textured surface pair (LRS and magnetic resonance tomogram) was registered using only cortical surface information. Novel visualization of the combined surfaces allowed for registration assessment based on quantitative cortical feature alignment. RESULTS: Successful textured LRS surface acquisition and generation was performed on all eight patients. The data acquired by the LRS accurately presented the intraoperative surface of the cortex and the associated features within the surgical field-of-view. Registration results are presented as overlays of the intraoperative data with respect to the preoperative data and quantified by comparing mean distances between cortical features on the magnetic resonance tomogram and LRS surfaces after registration. The overlays demonstrated that accurate registration can be provided between the preoperative and intraoperative data and emphasized a potential enhancement to cortical feature recognition within the operating room environment. Using the best registration result from each clinical case, the mean feature alignment error is 1.7 +/- 0.8 mm over all cases. CONCLUSION: This study demonstrates clinical deployment of an LRS capable of generating textured surfaces of the surgical field of view. Data from the LRS was registered accurately to the corresponding preoperative data. Visual inspection of the registration results was provided by overlays that put the intraoperative data within the perspective of the whole brain's surface. These visuals can be used to more readily assess the fidelity of image-to-patient registration, as well as to enhance recognition of cortical features for assistance in comparing the neurotopography between magnetic resonance image volume and physical patient. In addition, the feature-rich data presented here provides considerable motivation for using LRS scanning to measure deformation during surgery.

Compensating for intraoperative soft-tissue deformations using incomplete surface data and finite elements.

Image-guided liver surgery requires the ability to identify and compensate for soft tissue deformation in the organ. The predeformed state is represented as a complete three-dimensional surface of the organ, while the intraoperative data is a range scan point cloud acquired from the exposed liver surface. The first step is to rigidly align the coordinate systems of the intraoperative and preoperative data. Most traditional rigid registration methods minimize an error metric over the entire data set. In this paper, a new deformation-identifying rigid registration (DIRR) is reported that identifies and aligns minimally deformed regions of the data using a modified closest point distance cost function. Once a rigid alignment has been established, deformation is accounted for using a linearly elastic finite element model (FEM) and implemented using an incremental framework to resolve geometric nonlinearities. Boundary conditions for the incremental formulation are generated from intraoperatively acquired range scan surfaces of the exposed liver surface. A series of phantom experiments is presented to assess the fidelity of the DIRR and the combined DIRR/FEM approaches separately. The DIRR approach identified deforming regions in 90% of cases under conditions of realistic surgical exposure. With respect to the DIRR/FEM algorithm, subsurface target errors were correctly located to within 4 mm in phantom experiments.

A method to track cortical surface deformations using a laser range scanner.

This paper reports a novel method to track brain shift using a laser-range scanner (LRS) and nonrigid registration techniques. The LRS used in this paper is capable of generating textured point-clouds describing the surface geometry/intensity pattern of the brain as presented during cranial surgery. Using serial LRS acquisitions of the brain's surface and two-dimensional (2-D) nonrigid image registration, we developed a method to track surface motion during neurosurgical procedures. A series of experiments devised to evaluate the performance of the developed shift-tracking protocol are reported. In a controlled, quantitative phantom experiment, the results demonstrate that the surface shift-tracking protocol is capable of resolving shift to an accuracy of approximately 1.6 mm given initial shifts on the order of 15 mm. Furthermore, in a preliminary in vivo case using the tracked LRS and an independent optical measurement system, the automatic protocol was able to reconstruct 50% of the brain shift with an accuracy of 3.7 mm while the manual measurement was able to reconstruct 77% with an accuracy of 2.1 mm. The results suggest that a LRS is an effective tool for tracking brain surface shift during neurosurgery.

Incorporation of a laser range scanner into image-guided liver surgery: surface acquisition, registration, and tracking.

As image guided surgical procedures become increasingly diverse, there will be more scenarios where point-based fiducials cannot be accurately localized for registration and rigid body assumptions no longer hold. As a result, procedures will rely more frequently on anatomical surfaces for the basis of image alignment and will require intraoperative geometric data to measure and compensate for tissue deformation in the organ. In this paper we outline methods for which a laser range scanner may be used to accomplish these tasks intraoperatively. A laser range scanner based on the optical principle of triangulation acquires a dense set of three-dimensional point data in a very rapid, noncontact fashion. Phantom studies were performed to test the ability to link range scan data with traditional modes of image-guided surgery data through localization, registration, and tracking in physical space. The experiments demonstrate that the scanner is capable of localizing point-based fiducials to within 0.2 mm and capable of achieving point and surface based registrations with target registration error of less than 2.0 mm. Tracking points in physical space with the range scanning system yields an error of 1.4 +/- 0.8 mm. Surface deformation studies were performed with the range scanner in order to determine if this device was capable of acquiring enough information for compensation algorithms. In the surface deformation studies, the range scanner was able to detect changes in surface shape due to deformation comparable to those detected by tomographic image studies. Use of the range scanner has been approved for clinical trials, and an initial intraoperative range scan experiment is presented. In all of these studies, the primary source of error in range scan data is deterministically related to the position and orientation of the surface within the scanner's field of view. However, this systematic error can be corrected, allowing the range scanner to provide a rapid, robust method of acquiring anatomical surfaces intraoperatively.

Cortical surface registration for image-guided neurosurgery using laser-range scanning.

In this paper, a method of acquiring intraoperative data using a laser range scanner (LRS) is presented within the context of model-updated image-guided surgery. Registering textured point clouds generated by the LRS to tomographic data is explored using established point-based and surface techniques as well as a novel method that incorporates geometry and intensity information via mutual information (SurfaceMI). Phantom registration studies were performed to examine accuracy and robustness for each framework. In addition, an in vivo registration is performed to demonstrate feasibility of the data acquisition system in the operating room. Results indicate that SurfaceMI performed better in many cases than point-based (PBR) and iterative closest point (ICP) methods for registration of textured point clouds. Mean target registration error (TRE) for simulated deep tissue targets in a phantom were 1.0 +/- 0.2, 2.0 +/- 0.3, and 1.2 +/- 0.3 mm for PBR, ICP, and SurfaceMI, respectively. With regard to in vivo registration, the mean TRE of vessel contour points for each framework was 1.9 +/- 1.0, 0.9 +/- 0.6, and 1.3 +/- 0.5 for PBR, ICP, and SurfaceMI, respectively. The methods discussed in this paper in conjunction with the quantitative data provide impetus for using LRS technology within the model-updated image-guided surgery framework.

Non-rigid Registration of Serial Intra-operative Images for Automatic Brain Shift Estimation.

Measurement of intra-operative brain motion is important to provide boundary conditions to physics-based deformation models that can be used to register pre- and intra-operative information. In this paper we present and test a technique that can be used to measure brain surface motion automatically. This method relies on a tracked laser range scanner (LRS) that can acquire simultaneously a picture and the 3D physical coordinates of objects within its field of view. This reduces the 3D tracking problem to a 2D non-rigid registration problem which we solve with a Mutual Information-based algorithm. Results obtained on images of a phantom and on images acquired intra-operatively that demonstrate the feasibility of the method are presented.

Cortical Shift Tracking Using a Laser Range Scanner and Deformable Registration Methods.

A novel brain shift tracking protocol is introduced in this paper which utilizes laser range scan (LRS) data and 2D deformable image registration. This protocol builds on previous efforts to incorporate intra-operative LRS data into a model-updated image guided surgery paradigm for brain shift compensation. The shift tracking method employs the use of a LRS system capable of capturing textures of the intra-operative scene during range data acquisition. Textures from serial range images are then registered using a 2D deformable registration approach that uses local support radial basis functions and mutual information. Given the deformation field provided by the registration, 3D points in serial LRS datasets can then be tracked. Results from this paper indicate that the error associated with tracking brain movement is 1.1mm on average given brain shifts of approximately 20.5mm. Equally important, a strategy is presented to rapidly acquire intra-operative measurements of shift which are compatible with model-based strategies for brain deformation compensation.

Design and implementation of a PC-based image-guided surgical system.

In interactive, image-guided surgery, current physical space position in the operating room is displayed on various sets of medical images used for surgical navigation. We have developed a PC-based surgical guidance system (ORION) which synchronously displays surgical position on up to four image sets and updates them in real time. There are three essential components which must be developed for this system: (1) accurately tracked instruments; (2) accurate registration techniques to map physical space to image space; and (3) methods to display and update the image sets on a computer monitor. For each of these components, we have developed a set of dynamic link libraries in MS Visual C++ 6.0 supporting various hardware tools and software techniques. Surgical instruments are tracked in physical space using an active optical tracking system. Several of the different registration algorithms were developed with a library of robust math kernel functions, and the accuracy of all registration techniques was thoroughly investigated. Our display was developed using the Win32 API for windows management and tomographic visualization, a frame grabber for live video capture, and OpenGL for visualization of surface renderings. We have begun to use this current implementation of our system for several surgical procedures, including open and minimally invasive liver surgery.