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PURPOSE: Radiation exposure in interventional cardiology is an important consideration, due to risk of cancer and other morbidity to the patient and clinical staff. Cardiac catheterizations rely heavily on fluoroscopic imaging exposing both patient and clinician to ionizing radiation. An image-guided surgery system capable of facilitating cardiac catheterizations was developed and tested to evaluate dose reduction. METHODS: Several electromagnetically tracked tools were constructed specifically a 7-Fr catheter with five 5-degree-of-freedom magnetic seeds. Catheter guidance was accomplished using our image guidance system Kit for Navigation by Image-Focused Exploration and fluoroscopy alone. A cardiac phantom was designed and 3D printed to validate the image guidance procedure. In mock procedures, an expert clinician guided and deployed an occluder across the septal defect of the phantom heart. RESULTS: The image guidance method resulted in a dose of 1.26 mSv of radiation dose per procedure, while traditional guidance resulted in a dose of 3.33 mSv. Average overall dose savings for the image-guided method was nearly 2.07 mSv or 62 %. CONCLUSION: The work showed significant ([Formula: see text]) decrease in radiation dose with use of image guidance methods at the expense of a modest increase in procedure time. This study lays the groundwork for further exploration of image guidance applications in pediatric cardiology.

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Laparoscopic surgery is a minimally invasive surgical technique where surgeons insert a small video camera into the patient's body to visualize internal organs and use small tools to perform surgical procedures. However, the benefit of small incisions has a drawback of limited visualization of subsurface tissues, which can lead to navigational challenges in the delivering of therapy. Image-guided surgery uses the images to map subsurface structures and can reduce the limitations of laparoscopic surgery. One particular laparoscopic camera system of interest is the vision system of the daVinci-Si robotic surgical system (Intuitive Surgical, Sunnyvale, California). The video streams generate approximately 360 MB of data per second, demonstrating a trend toward increased data sizes in medicine, primarily due to higher-resolution video cameras and imaging equipment. Processing this data on a bedside PC has become challenging and a high-performance computing (HPC) environment may not always be available at the point of care. To process this data on remote HPC clusters at the typical 30 frames per second (fps) rate, it is required that each 11.9 MB video frame be processed by a server and returned within 1/30th of a second. The ability to acquire, process, and visualize data in real time is essential for the performance of complex tasks as well as minimizing risk to the patient. As a result, utilizing high-speed networks to access computing clusters will lead to real-time medical image processing and improve surgical experiences by providing real-time augmented laparoscopic data. We have performed image processing algorithms on a high-definition head phantom video (1920 × 1080 pixels) and transferred the video using a message passing interface. The total transfer time is around 53 ms or 19 fps. We will optimize and parallelize these algorithms to reduce the total time to 30 ms.

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PURPOSE: In cardiac ablation therapy, accurate anatomic guidance is necessary to create effective tissue lesions for elimination of left atrial fibrillation. While fluoroscopy, ultrasound, and electroanatomic maps are important guidance tools, they lack information regarding detailed patient anatomy which can be obtained from high resolution imaging techniques. For this reason, there has been significant effort in incorporating detailed, patient-specific models generated from preoperative imaging datasets into the procedure. Both clinical and animal studies have investigated registration and targeting accuracy when using preoperative models; however, the effect of various error sources on registration accuracy has not been quantitatively evaluated. METHODS: Data from phantom, canine, and patient studies are used to model and evaluate registration accuracy. In the phantom studies, data are collected using a magnetically tracked catheter on a static phantom model. Monte Carlo simulation studies were run to evaluate both baseline errors as well as the effect of different sources of error that would be present in a dynamic in vivo setting. Error is simulated by varying the variance parameters on the landmark fiducial, physical target, and surface point locations in the phantom simulation studies. In vivo validation studies were undertaken in six canines in which metal clips were placed in the left atrium to serve as ground truth points. A small clinical evaluation was completed in three patients. Landmark-based and combined landmark and surface-based registration algorithms were evaluated in all studies. In the phantom and canine studies, both target registration error and point-to-surface error are used to assess accuracy. In the patient studies, no ground truth is available and registration accuracy is quantified using point-to-surface error only. RESULTS: The phantom simulation studies demonstrated that combined landmark and surface-based registration improved landmark-only registration provided the noise in the surface points is not excessively high. Increased variability on the landmark fiducials resulted in increased registration errors; however, refinement of the initial landmark registration by the surface-based algorithm can compensate for small initial misalignments. The surface-based registration algorithm is quite robust to noise on the surface points and continues to improve landmark registration even at high levels of noise on the surface points. Both the canine and patient studies also demonstrate that combined landmark and surface registration has lower errors than landmark registration alone. CONCLUSIONS: In this work, we describe a model for evaluating the impact of noise variability on the input parameters of a registration algorithm in the context of cardiac ablation therapy. The model can be used to predict both registration error as well as assess which inputs have the largest effect on registration accuracy.

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Current techniques in image-guided surgery rely on the use of localizers for the measurement of position in physical space. These measurements are prone to error due to intrinsic properties of the localizer used. The error and thus accuracy of a localizer can be determined using various techniques, many of which assume that the error is isotropic and free of bias. A bias error adds an orientation dependence to the error of measured points. Determination of the presence of a bias error is an important component in the characterization of a localizer's performance. Statistical analysis of localized points on a rigid phantom can be used to detect the presence of a bias error. In this paper, we will examine the use of statistical techniques in the characterization of a series of localizers and how that information is useful in determining localizer efficacy.

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