3D Lesion Short Axis Determination via Long Axis Projection
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Solution Overview
Problem
Current methods for estimating the short axis in 3D medical images, such as tumors, lack accuracy and efficiency, particularly in 3D reconstructions from MRI and CT scans, which are crucial for medical diagnosis and treatment evaluation.
Innovation Solution
A computer-implemented method that computes the short axis by sampling the long axis into equal-sized ranges, evaluating pairs of points within these ranges for maximum distance, and refining the results to ensure perpendicularity to the long axis, using user-defined parameters for deviation control, applicable to both CT and MRI 3D reconstructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing algorithms for estimating the short axis are used, then computation speed is maintained, but measurement precision deteriorates
Solution Approach 1:
The method segments the continuous search space for the short axis into discrete angular intervals. By evaluating candidate axes at specific angle increments rather than continuously, the algorithm achieves accurate short axis estimation while maintaining computational efficiency. The segmentation allows systematic exploration of perpendicular directions without exhaustive computation.
Solution Approach 2:
The algorithm performs partial action by evaluating candidate short axes at discrete angular intervals rather than exhaustively checking all possible directions. This selective sampling approach provides sufficient accuracy for medical imaging applications while significantly reducing computational burden compared to a complete search.
2Measurement precision
If the short axis is computed with high accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The algorithm segments the complex problem of finding the optimal perpendicular axis into manageable discrete angular steps. This segmentation transforms a complex continuous optimization problem into a series of simpler discrete evaluations, reducing algorithmic complexity while preserving accuracy.
Solution Approach 2:
The method performs preliminary action by first determining the long axis and establishing the reference plane before computing the short axis. This preliminary setup simplifies subsequent calculations by constraining the search space to perpendicular directions within a defined plane, reducing overall algorithmic complexity.
Data Source
AI summary
A short axis in a 3 dimensional image of a lesion is determined starting from voxels defining the long axis and voxels in the plane of the long axis. Voxels within the plane of the long axis are projected perpendicularly onto the long axis and receive an identifier indicative of the region on the long axis onto which they are projected. Distances between points (projected sub-voxels) in pairs of points within the same range and within adjacent ranges are evaluated in order to determine the longest distance.