Quantitative modeling of the accuracy in registering preoperative patient-specific anatomic models into left atrial cardiac ablation procedures.

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.

Fast adaptation of pre-operative patient specific models to real-time intra-operative volumetric data streams.

Image-guided catheter ablation therapy is becoming an increasingly popular treatment option for atrial fibrillation. Successful treatment relies on accurate guidance of the treatment catheter. Integration of high-resolution, pre-operative data with electrophysiology data and positional data from tracked catheters improves targeting, but lacks the means to monitor changes in the atrial wall. Intra-operative ultrasound provides a method for imaging the atrial wall, but the real-time, dynamic nature of the data makes it difficult to seamlessly integrate with the static pre-operative patient-specific model. In this work, we propose a technique which uses a self-organizing map (SOM) for dynamically adapting a pre-operative model to surface patch data. The surface patch would be derived from a segmentation of the anatomy in a real-time, intra-operative ultrasound data stream. The method is demonstrated on two regular geometric shapes as well as data simulated from a real, patient computed tomography dataset.

Tissue engineering templates using minimal surfaces.

The status quo of tissue engineering can be summarized as "a random walk through the design space". The existing scaffold designs based on computeraided design (CAD) and solid freeform fabrication (SFF) are anti-biomorphic and mechanically weak cubic partitions with sharp edges. We introduce minimal surface based unit cells to create biomorphic scaffolds with optimal stress/strain distribution and superior mechanical strength.

Virtual-reality-assisted interventional procedures.

Observable objects in biology and medicine extend across a range of scale, from individual molecules and cells; through the varieties of tissue and interstitial interfaces; and to complete organs, organ systems, and body parts. These objects include functional attributes of these systems such as biophysical, biomechanical, and physiologic properties. Imaging in three dimensions of such objects and their functions is possible now with the advent of high-resolution tomographic scanners and imaging systems. Medical applications include accurate anatomy and function mapping, enhanced diagnosis, accurate treatment planning and rehearsal, and education and training. Biologic applications include study and analysis of structure-to-function relationships in individual cells and organelles. The potential for revolutionary innovation in the practice of medicine and in biologic investigations lies in direct, fully immersive, real-time multisensory fusion of real and virtual information data streams into online, real-time images available during actual clinical procedures or biologic experiments. Current high-performance computing, advanced image processing and high-fidelity rendering capabilities have facilitated major progress toward realization of these goals. With these advances in hand, there are several important applications of three-dimensional viewing that will have a substantial impact on the practice of medicine.