Medical Device Point-Cloud Sampling to Reduce Registration Oversampling
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Solution Overview
Problem
Existing minimally invasive medical procedures face inaccuracies in registering medical instruments due to oversampling of data points caused by disproportionate surveying of anatomical regions, leading to misalignment between real and model-based anatomical structures.
Innovation Solution
A system that analyzes sensor and data point parameters in real-time, comparing them to thresholds to mitigate oversampling by recording data points only when certain motion or density criteria are met, using methods like motion collection, point distance rejection, and point density normalization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the medical device surveys anatomical regions continuously without filtering, then comprehensive data coverage is achieved, but oversampling occurs causing registration inaccuracies
Solution Approach 1:
The patent applies parameter changes by analyzing motion parameters (velocity, acceleration, direction changes) of the medical device to dynamically filter data points. When the device moves quickly through a region or changes direction sharply, data points are rejected to prevent oversampling. This transforms the static data collection process into a dynamic one that adapts to the device's motion state, resolving the contradiction between comprehensive coverage and accurate sampling.
Solution Approach 2:
The system implements feedback by continuously monitoring the motion state of the medical device and using this information to control data point acceptance. The processor analyzes real-time motion parameters and provides feedback signals to accept or reject data points accordingly. This closed-loop control ensures that only appropriately sampled data points contribute to the survey point cloud, improving registration accuracy while preventing oversampling artifacts.
2Productivity
If the medical device moves quickly through anatomical regions, then surveying efficiency is improved, but oversampling of certain regions occurs leading to misalignment
Solution Approach 1:
The patent uses parameter changes by establishing thresholds for motion parameters (velocity, acceleration, directional change) to control data point acceptance. When the medical device moves quickly through a region, the system detects these parameter changes and rejects data points from high-velocity segments, preventing oversampling. This maintains surveying efficiency by accepting data from properly sampled regions while filtering out problematic high-speed data.
Solution Approach 2:
The system applies local quality by treating different spatial regions differently based on their sampling characteristics. Regions that are oversampled due to rapid device movement have their data points rejected, while regions with appropriate sampling density are accepted. This localized filtering approach ensures that each anatomical region contributes appropriately to the survey point cloud, preventing misalignment artifacts in the registration process.
3Measurement precision
If data points are collected without motion analysis, then data collection is simple and fast, but registration accuracy deteriorates due to oversampling errors
Solution Approach 1:
The patent implements feedback by creating a closed-loop system where motion parameters are continuously analyzed and used to control data point acceptance. The processor receives raw data points, analyzes motion parameters, and provides feedback signals to accept or reject points accordingly. This systematic approach improves registration accuracy by ensuring only quality data points are used, while the automated nature of the process manages the added complexity efficiently.
Solution Approach 2:
The system applies self-service by enabling the medical device and processing system to automatically analyze motion parameters and filter data points without requiring manual intervention. The processor autonomously determines which data points to accept or reject based on pre-established motion thresholds, eliminating the need for operator judgment while improving registration accuracy. This automation manages processing complexity through algorithmic decision-making.
Data Source
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AI summary
Disclosed are systems and methods for mitigating oversampling of data points collected by a medical device. In some aspects, a system is configured to receive data points of a sampled survey point cloud detected by a sensor of the medical device during surveying of an anatomic structure; determine, during the surveying, at least one parameter associated with (i) the medical device and/or (ii) the received data points detected by the sensor, including a change of translational and/or rotational motion of the medical device, a distance from a data point to a nearest neighbor within the sampled survey point cloud, or a density of the data points of a sub-set of the sampled survey point cloud corresponding to sub-region of the anatomic structure; analyze the parameter(s) by comparing it to a threshold; and record individual data points in a registration point cloud when the analyzed parameter(s) satisfies the respective threshold.