Biopsy Needle Reconstruction Error Assessment
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Solution Overview
Problem
During Digital Breast Tomosynthesis (DBT) guided breast interventional procedures, the low in-depth resolution of reconstructed images leads to reconstruction errors in the positioning of needle-like interventional tools, such as biopsy needles, due to limited angular range of the x-ray system, causing inaccuracies in determining the actual tool tip position and potentially missing the target tissue.
Innovation Solution
A method for an x-ray system that performs a digital breast tomosynthesis scan, reconstructs images, and determines an expected reconstruction error for an interventional tool based on an error model modeled as a function of acquisition geometry and tool parameters, including tool length, diameter, orientation, and tip position, derived from selected target positions and robotic feedback, to improve the accuracy of tool positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Digital Breast Tomosynthesis is used for imaging, then the ability to detect tumors and guide interventions is improved, but reconstruction errors in tool positioning occur due to limited angular range
Solution Approach 1:
The system incorporates feedback from robotic system tip position measurements to calculate and display expected reconstruction errors. The robotic system provides actual tip position data, which is compared against reconstructed positions to determine reconstruction error, enabling continuous monitoring and correction of positioning accuracy throughout the procedure
Solution Approach 2:
The system models reconstruction error as a function of multiple parameters including acquisition geometry, tool length, tool tip diameter, tool orientation, and tool tip position. By analyzing how these parameters affect reconstruction error, the system can predict and compensate for positioning inaccuracies under different imaging and tool configurations
2Productivity
If the x-ray system uses limited angular range for scanning, then the scanning time and radiation exposure are reduced, but the in-depth resolution of reconstructed images deteriorates
Solution Approach 1:
The system performs preliminary calculation of expected reconstruction errors based on acquisition geometry parameters before the actual biopsy procedure. This allows the system to predict resolution limitations in advance and compensate for them during tool positioning, rather than attempting to improve resolution through additional scanning angles that would increase procedure time
3Productivity
If needle parameters such as length and diameter are optimized for biopsy, then the biopsy effectiveness is improved, but reconstruction error in determining actual needle position increases
Solution Approach 1:
The system introduces an intermediary error model that acts as a mediator between the physical needle parameters and the reconstructed image position. This model translates needle characteristics (length, diameter, orientation) into expected reconstruction errors, allowing the system to account for how different needle specifications affect positioning accuracy without changing the needles themselves
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
This approach enhances the precision and efficiency of interventional procedures by accurately assessing tool position, reducing the need for repeat procedures and improving patient care by providing a more accurate evaluation of tool placement within the breast tissue.
Implementation Method 1
performing a digital breast tomosynthesis scan on a compressed breast with the x-ray system
Data Source
Figure 1A
Figure 1B
Figure 1C
AI summary
Methods and systems are provided for reconstruction error assessment for an interventional tool utilized in an image guided interventional procedure. In one example, an error model based on a target lesion position within a tissue, one or more interventional tool parameters, and imaging system parameters may be utilized to estimate an expected reconstruction error for the interventional tool. In another example, when the interventional tool is within the tissue, the expected reconstruction error may be utilized along with observed tool shape and size to infer an actual tool position and shape within the tissue.