Dr. Karen I. Trovato Profile

Dr. Karen I. Trovato

Member of Research Staff at Philips Reseach North America

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Proceedings Article | 13 March 2009 Paper

Collision-free 6D non-holonomic planning for nested cannulas

Karen Trovato, Aleksandra Popovic

Proceedings Volume 7261, 72612H (2009) https://doi.org/10.1117/12.813511

KEYWORDS: Lung, Computed tomography, Image segmentation, Tumors, Bronchoscopy, Tissues, Free space, Detection and tracking algorithms, Diagnostics and therapeutics, 3D image processing

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Natural orifice access is the next frontier in minimally invasive technology. This requires dexterity for reaching through complex translumenal paths to a target. We propose a fast algorithm to define shapes of tiny, Nested Cannula devices based on patient CT images to deliver diagnostic and therapeutic procedures, and apply it to deep lung access. Each pre-shaped tube is extended sequentially in either a curved or a straight direction, requiring the solution to a 6D non-holonomic problem with obstacle avoidance in order to reach through free anatomy. A 3D image of the lung provides the specification of free and forbidden regions as well as the core structure for a configuration space. By using an A* search, each state holds the detailed specification leading to the 'goal'. These specifications include the shape, 3D orientation, and 3D position, which can be stored in an adjacent structure in high precision. This allows the normally massive 6D configuration space to be stored in an augmented 3D structure, reducing massive memory requirements by about two orders of magnitude. The adapted configuration space and A* algorithm requires under a minute on a desktop PC to compute a set of shaped tubes that can reach far inside a segmented lung. This paper describes three advances. The first defines new ways to structure searched configuration spaces so that it no longer requires intractable memory. The second solves the non-holonomic 6D problem of defining shaped tubes that extend sequentially into the body while avoiding obstacles. The third incorporates the physics of the interacting tubes.

Proceedings Article | 13 March 2009 Paper

Automated RFA planning for complete coverage of large tumors

Karen Trovato, Sandeep Dalal, Jochen Krücker, Aradhana Venkatesan, Bradford Wood

Proceedings Volume 7261, 72610D (2009) https://doi.org/10.1117/12.811593

KEYWORDS: Radiofrequency ablation, Tumors, Spherical lenses, Skin, Optical spheres, Composites, Visualization, Tissues, Image segmentation, Liver

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Radiofrequency ablation (RFA) is a minimally invasive procedure used for the treatment of small-to-moderate sized tumors most commonly in the liver, kidney and lung. An RFA procedure for successfully treating large or complex shape tumors may require many ablations, in a non-obvious pattern. Tumor size > 3cm predisposes to incomplete treatment [1] and potential recurrence, therefore RFA is less often successful and less often used for treating large tumors. A mental solution is the current clinical practice standard, but is a daunting task for defining the complete 3D geometrical coverage of a tumor and margin (planned target volume, PTV) with the fewest ellipsoidal ablation volumes, while also minimizing collateral damage to healthy tissue. In order to generate a repeatable and reliable result, a solution must quantify precise locations. A new interactive planning system with an automated coverage algorithm is described. The planning system allows the interventional radiologist to segment the potentially complex PTV, select an RFA needle (which determines the specific 3D ablation shape), and identify the skin entry location that defines the shape's orientation. The algorithm generates a cluster of overlapping ablations from the periphery of the PTV, filling toward the center. The cluster is first tightened toward the center to reduce the overall number of ablations and collateral damage, and then pulled toward optimal attractors to further reduce the number of ablations. For most clinical applications, computation requires less than 15 seconds. This fast ablation planning enables rapid scenario assessment, including proper probe selection, skin entry location, collateral damage and procedure duration. The plan can be executed by transferring target locations to a navigation system.

Proceedings Article | 14 February 1992 Paper

Autonomous vehicle maneuvering

Karen Trovato

Proceedings Volume 1613, (1992) https://doi.org/10.1117/12.135170

KEYWORDS: Wave propagation, Mobile robots, Control systems, Kinematics, Space robots, Spine, Cameras, Image processing, Sun, Sensors

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This paper describes a method to automatically generate optimal, collision-free maneuvers for a vehicle to follow. The technique requires information about the vehicle dimensions, wheel layout and turning radius, so that the vehicle's kinematic capabilities can be computed automatically. Locations of obstacles in the maneuvering environment must also be given. The maneuver computed is kinematically feasible, and is given in terms of the control parameters of the vehicle, namely steering angle and forward/backward motion. The method includes techniques for transforming obstacles, performing cost wave propagation, and using kinematically correct neighborhoods to generate an augmented configuration space specific to a vehicle. With this structure, the vehicle can move from any starting position, correct for run-time deviations, and drive to the goal position. A 1/10 scale radio-controlled testbed is used to verify that the theoretical path can indeed be carried out in practice.

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