Active Localization for Medical Instrument Tracking
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Solution Overview
Problem
Intra-operative medical procedures, such as device insertion and therapy delivery, are complicated by target movement and device bending due to physiological motions and tissue deformations, leading to uncertainties in target position and device configuration, which existing imaging techniques struggle to accurately address in real-time.
Innovation Solution
The system employs active localization methods using probabilistic measurement and motion models to estimate the states of instruments and targets within the body, employing Bayesian filters and entropy minimization to select control parameters for the imaging and intervention systems, ensuring precise tracking and real-time control of instruments like needles and catheters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If intra-operative medical imaging techniques are used to provide real-time information about device and target locations, then the ability to track and steer devices is improved, but the complexity of the system and computational requirements increase
Solution Approach 1:
The system pre-computes motion models and prediction algorithms before the procedure, and pre-positions the imaging system to capture critical views. Probabilistic models are pre-calculated to anticipate target motion based on physiological parameters, reducing real-time computational burden while maintaining high tracking accuracy
Solution Approach 2:
The system continuously acquires imaging data and feeds it back to update the probabilistic motion models in real-time. The measured target position and device location are compared against predictions, and the system adjusts control parameters dynamically to maintain accurate tracking despite tissue deformation and physiological motion
2Reliability
If real-time imaging is performed to track moving targets and deformable devices, then the reliability of intervention is improved, but the time required for image acquisition and processing increases
Solution Approach 1:
The imaging system acquires data at optimized periodic intervals rather than continuously, synchronized with the physiological cycle (e.g., respiratory phase, cardiac cycle). This periodic sampling captures critical motion states while minimizing redundant acquisitions, reducing overall imaging time while maintaining reliable tracking of periodic physiological motions
Solution Approach 2:
Motion compensation transformations are pre-computed based on anticipated physiological motion patterns. The system predicts target position at future time points using pre-established motion models, allowing intervention to proceed without waiting for complete real-time image processing cycles
3Measurement precision
If probabilistic motion models and Bayesian filters are used to estimate instrument and target states, then the precision of localization is improved, but the computational complexity increases
Solution Approach 1:
The complex probabilistic model is segmented into separate modular components: a motion model for physiological motion, a device model for instrument dynamics, and a measurement model for imaging uncertainty. Each module can be independently calibrated and computed, reducing overall computational complexity while maintaining the precision benefits of the integrated Bayesian framework
Data Source
AI summary
A method includes acquiring image data from for a given time step. The image data represents at least one of an instrument and a target within an image space that includes patient anatomy. A given belief of system state is estimated to include an estimate of instrument state and target state determined from corresponding measurement and motion models computed based on the acquired image data and control input parameters for the given time step. An expected belief of system state is estimated for a subsequent time step based on the estimated given belief of system state and based on the measurement models and the motion models computed over a plurality of different control inputs parameters. An information metric is computed for each of the expected beliefs of system state and control input parameters are selected for the subsequent time step based on the computed information metrics.