Image-guided procedure with intraoperative imaging updates has made a big impact on minimally invasive surgery. Compact and mobile CT imaging device combining with current commercial available image guided navigation system is a legitimate and cost-efficient solution for a typical operating room setup. However, the process of manual fiducial-based registration between image and physical spaces (image-to-world) is troublesome for surgeons during the procedure, which results in much procedure interruptions and is the main source of registration errors. In this study, we developed a novel method to eliminate the manual registration process. Instead of using probe to manually localize the fiducials during the surgery, a tracking plate with known fiducial positions relative to the reference coordinates is designed and fabricated through 3D printing technique. The workflow and feasibility of this method has been studied through a phantom experiment.
Minimally invasive neurosurgery needs intraoperative imaging updates and high efficient image guide system to facilitate the procedure. An automatic image guided system utilized with a compact and mobile intraoperative CT imager was introduced in this work. A tracking frame that can be easily attached onto the commercially available skull clamp was designed. With known geometry of fiducial and tracking sensor arranged on this rigid frame that was fabricated through high precision 3D printing, not only was an accurate, fully automatic registration method developed in a simple and less-costly approach, but also it helped in estimating the errors from fiducial localization in image space through image processing, and in patient space through the calibration of tracking frame. Our phantom study shows the fiducial registration error as 0.348±0.028mm, comparing the manual registration error as 1.976±0.778mm. The system in this study provided a robust and accurate image-to-patient registration without interruption of routine surgical workflow and any user interactions involved through the neurosurgery.
xCAT®, (Xoran Technologies, LLC., Ann Arbor, MI) is a CT imaging device that has been used for minimally invasive surgeries. Designed with flat panel and cone-beam imaging technique, it provides a fast, low-dose CT imaging alternative for diagnosis and examination purposes at hospitals. With its unique compact and mobile characteristics, it allows scanning inside crowded operating rooms (OR). The xCAT allows acquisition of images in the OR that show the most recent morphology during the procedure. This can potentially improve outcomes of surgical procedures such as deep brain stimulation (DBS) and other neurosurgeries, since brain displacement and deformation (brain shift) often occur between pre-operative imaging and electrode placement during surgery. However, the small gantry size of the compact scanner obstructs scanning of patients with stereotactic frames or skull clamp. In this study, we explored a novel method, in which we first utilized the xCAT to obtain CT images with fiducial markers, registered the stereotactic frame with those markers, and finally, target measurements were calculated and set up on the frame. The new procedure workflow provides a means to use CT images obtained inside of OR for stereotactic surgery and can be used in current intraoperative settings. Our phantom validation study in lab shows that the procedure workflow with this method is easy to conduct.
Liver cancer represents a major health care problem in the world, especially in China and several countries in Southeast
Asia. The most effective treatment is through tumor resection. To improve the outcome of surgery, a combination of
preoperative planning and intra operative image guided liver surgery (IGLS) system has been developed at Pathfinder
Therapeutics, Inc. The preoperative planning subsystem (Linasys® PlaniSight®) is user-oriented and applies several
novel algorithms on image segmentation and modeling, which allows the user to build various organ and tumor models
with anticipated resection planes in less than 30 minutes. The surgeons can analyze the patient-specific case and set up
surgical protocols. This information in image space can then be transferred into physical space through our intra
operative image guided liver surgery system (Linasys® SurgSight®) based on modifications of existing surface
registration algorithms, allowing surgeons to perform more accurate resections after preoperative planning. This tool
gives surgeons a better understanding of vessel structure and tumor locations within the liver parenchyma during the
surgery. Our ongoing clinical trial shows that it greatly facilitates liver resection operation and it is expected to improve
the surgery outcome and create more candidates for surgery.
KEYWORDS: Image segmentation, Liver, Surgery, Veins, 3D modeling, Tumors, Data modeling, Image-guided intervention, Image processing algorithms and systems, Systems modeling
Interactive image-guided liver surgery (Linasys device, Pathfinder Therapeutics, Inc., Nashville, TN) requires a user-oriented,
easy-to-use, fast segmentation preoperative surgical planning system. This system needs to build liver models
displaying the liver surface, tumors, and the vascular system of the liver. A robust and efficient tool for this purpose was
developed and evaluated. For the liver surface or other bulk shape organ segmentation, the delineation was conducted on
multiple slices of a CT image volume with a region growing algorithm. This algorithm incorporates both spatial and
temporal information of a propagating front to advance the segmenting contour. The user can reduce the number of
delineation slices during the processing by using interpolation. When comparing our liver segmentation results to those
from MeVis (Breman, Germany), the average overlap percentage was 94.6%. For portal and hepatic vein segmentation,
three-dimensional region growing based on image intensity was used. All second generation branches can be identified
without time-consuming image filtering and manual editing. The two veins are separated by using mutually exclusive
region growing. The tool can be used to conduct segmentation and modeling of the liver, veins, and other organs and can
prepare image data for export to Linasys within one hour.
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