Pose-Based 3D Reconstruction for Manual C-Arm X-Ray Imaging
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Solution Overview
Problem
Conventional manual C-arm imaging systems lack the capability to accurately reconstruct three-dimensional structures due to the absence of depth information in two-dimensional fluoroscopic images, making it difficult to determine the relative pose of medical tools relative to target tissues, and existing automated systems are expensive and not widely accessible.
Innovation Solution
A system and method that utilizes a time-ordered series of X-ray images taken at varying poses, combined with a pose sensor and shape sensor, to refine initial pose estimates by comparing projected images with known objects, enabling three-dimensional reconstruction and segmentation without additional hardware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional manual C-arm imaging systems are used, then the system cost is low and ease of operation is high, but three-dimensional reconstruction capability is lost and measurement precision deteriorates
Solution Approach 1:
The patent introduces pose sensors and shape sensors as intermediary devices that capture additional information about the imaging system's position and the object's geometry. These sensors serve as mediators between the existing C-arm system and the desired three-dimensional reconstruction capability, enabling accurate pose estimation and 3D structure recovery without replacing the entire imaging system.
Solution Approach 2:
The patent replaces complex mechanical positioning systems with sensor-based pose estimation. Instead of using complex mechanical linkages or automated robotic arms to control C-arm positioning, the system uses pose sensors to detect the actual position and orientation, substituting mechanical complexity with sensor-based measurement and computation.
2Measurement precision
If automated C-arm systems are used, then three-dimensional reconstruction capability is improved, but system cost increases and ease of operation deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where pose sensors continuously monitor the C-arm position and orientation, providing real-time information to the reconstruction system. This feedback loop enables accurate pose estimation while maintaining manual operation, as the system adapts to the operator's movements through continuous sensing and computation rather than requiring automated control.
Solution Approach 2:
The system enables self-service pose estimation by using the C-arm's own movement and the object's known shape information to automatically determine pose parameters. The pose sensors work with the existing imaging infrastructure to self-determine positioning without requiring external automated control systems, maintaining operator flexibility while achieving accurate reconstruction.
3Loss of information
If two-dimensional fluoroscopic images are used, then imaging simplicity is maintained, but depth information is lost and manufacturing precision deteriorates
Solution Approach 1:
The patent transitions from two-dimensional image analysis to three-dimensional structure reconstruction by incorporating pose sensor data and shape information. This dimensional extension allows the system to recover depth cues that are invisible in conventional 2D fluoroscopic images, enabling accurate three-dimensional representation of the imaged object.
Solution Approach 2:
The patent performs preliminary actions by capturing pose sensor data and shape information before the actual imaging process. By pre-acquiring the C-arm's position/orientation data and the object's geometric characteristics, the system prepares the necessary information for subsequent three-dimensional reconstruction, ensuring depth information is available when needed without adding complexity during the imaging moment.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate three-dimensional reconstruction and segmentation of medical tools and target tissues using inexpensive manual C-arm systems, improving localization and reducing the need for expensive automated systems.
Implementation Method 1
receive a time-ordered plurality of X-ray images of an object, wherein the plurality of X-ray images are taken at a plurality of poses relative to the object
Data Source
AI summary
Three-dimensional structure reconstruction systems and related methods are disclosed. In some examples, a three-dimensional structure reconstruction system may include at least one processor configured to: receive a time-ordered plurality of X-ray images of an object, where the plurality of X-ray images are taken at a plurality of poses relative to the object, where at least one of the plurality of X-ray images depicts at least one object of known shape and/or location; determine an initial estimate of at least one pose of at least one of the plurality of X-ray images; and refine the at least one pose based on at least one comparison with the plurality of X-ray images. In some examples, a method may include receiving time-ordered images taken at poses; determining an initial estimate of at least one pose; and refining a pose based on at least one comparison with the plurality of X-ray images.