Biomedical subjects

Robert L Galloway

Publications and source records attributed to Robert L Galloway.

5 recordsLinked to original sources

PubMed2003

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.

Adult↗

PubMed2003

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.

Aged↗

PubMed2002

Three-dimensional image registration of phantom vertebrae for image-guided surgery: a preliminary study.

OBJECTIVE: Applications of three-dimensional ultrasound (3D US) are emerging throughout the field of medicine. In this study, tracked, free-hand 3D phantom US images were mapped to computed tomograms (CT) as a development for image-guided surgery (IGS) of the spine. In the operating room, the registration of tracked 3D US images to other imaging modalities, such as CT, could allow the surgeon to identify more precisely the surgical target area prior to the incision. An independent quantitative measure of registration accuracy using a fiducial marker system was provided. METHODS: Three-dimensional free-hand US images of a phantom spine were created by tracking the transducer with an optical sensing system. Two sets of images were acquired from three lumbar vertebrae using 4.5- and 7.5-MHz transducers. These images were then segmented for the extraction of the posterior vertebral surface. Next, a surface-based registration of US to the corresponding segmented CT images was performed. Registration errors were computed as the distance between a set of target points transformed using the experimental transformation and the same set of target points transformed using fiducial markers as a gold standard. RESULTS: Results indicated that alignment of these image sets is feasible using only part of the vertebral surface. In particular, the regions of the spinous process and laminae were used for registration. Target registration errors (TREs) were found to be lowest using the highest resolution CT images. Using the CT scans with 2-mm slice thickness, the TRE was calculated to be 1.33 +/- 0.30 mm for the 7.5-MHz US data set and 2.81 +/- 0.10 mm for the 4.5-MHz US data set. Moreover, residual errors in these surface alignments were 0.69 +/- 0.18 mm and 0.61 +/- 0.20 mm for the 4.5- and 7.5-MHz sets, respectively. CONCLUSION: A rigid, surface-based registration of CT images to phantom spinal US images, acquired with a free-hand, tracked transducer, is achievable with a limited, easily obtainable portion of the vertebral surface.

Algorithms↗

PubMed2002

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.

Algorithms↗

PubMedDate not supplied

Centroid-based maximum intensity projections.

PURPOSE: Maximum intensity projection (MIP) is a three-dimensional visualization technique for tomographic angiograms. While conventional MIPs display contralateral vascular anatomy, this study uses centroid calculations to remove this information. It is necessary to provide accurate, unambiguous vessel depiction and identical projections regardless of slice orientation. METHOD: A mathematical model was formed using parameters from clinical images of the head. The vessel widths from the resulting projections were measured and compared with the model. To test the consistency of the projection process, a clinical image set was reformatted and projections of the same view were compared. RESULTS: The vessel widths were smaller than in the model while varying interpolation and noise. Similar projection views were generated for all slice orientations, but some misalignment was present. CONCLUSION: Vessel width is affected by the ray's path length and interpolation method. Some slight misalignment is present because the reformatting process alters the centroid calculations.

Cerebral Angiography↗