Automated ECT Myocardial Perfusion Image Reorientation
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Solution Overview
Problem
Current ECT cardiac imaging methods require manual and interactive efforts for aligning and centering myocardial perfusion images, leading to misalignment and false diagnoses, especially in large or acutely ill patients, which hinders optimal diagnostic accuracy.
Innovation Solution
An automated computer-implemented method and system that reorients and realigns ECT myocardial perfusion images by determining the left ventricle's long-axis and center, calculating translation and rotation values, and applying an affine transform to achieve precise alignment and centering of the images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual and interactive methods are used for aligning ECT images, then flexibility and adaptability are maintained, but alignment precision and diagnostic reliability deteriorate due to misalignment and false diagnoses
Solution Approach 1:
The system performs automatic self-alignment by detecting anatomical landmarks (aortic root, pulmonary trunk, ventricles) and computing transformation parameters without requiring manual intervention. The computer automatically identifies the LV long-axis and applies affine transforms to align images, making the system self-sufficient in the alignment task.
Solution Approach 2:
The patent replaces manual mechanical adjustment operations with automated computer-based image processing. The system uses digital detection of anatomical structures and mathematical transformation (affine transform) instead of manual positioning and visual alignment methods.
2Reliability
If automated methods are implemented for image alignment, then alignment precision and diagnostic reliability improve, but system complexity increases
Solution Approach 1:
The alignment process is divided into distinct sequential steps: (1) detection of anatomical landmarks (aortic root, pulmonary trunk, ventricles), (2) determination of LV long-axis based on detected landmarks, (3) calculation of transformation parameters, and (4) application of affine transform. This segmentation makes the complex automated process more manageable and implementable.
Solution Approach 2:
The system performs preliminary detection and identification of anatomical structures before performing the actual alignment transformation. By first identifying the LV long-axis and calculating transformation parameters in advance, the system prepares the necessary information before applying the final alignment, ensuring reliability without excessive complexity during the critical alignment moment.
3Measurement precision
If precise alignment and centering are achieved through automated methods, then reproducibility of quantitative measurements improves, but computational requirements and processing time increase
Solution Approach 1:
The system changes the parameter representation by working with transformation parameters (rotation angle, translation distance) derived from anatomical landmarks. By parameterizing the alignment problem based on key anatomical points rather than processing entire image datasets, the system achieves high reproducibility with reduced computational burden compared to full-image registration methods.
Data Source
AI summary
An automated computer-implemented method for reorienting ECT myocardial perfusion images of a heart LV. The method includes receiving variously oriented tomographic images; receiving LV long-axis, LV center and LV axial limits based on the images; receiving the endocardial surface of the LV based on the images; determining a reorientation slice range based on the center and axial limits of the LV; receiving slices (N) within the reorientation slice range; for each slice, determining a center coordinate x[i], y[i] based on the endocardial surface and the area of the slice within a reorientation slice range coordinate system; determining translation Δxi, Δyi and rotation θx, θy values based on center coordinates x[i=1 to N], y[i=1 to N] to reorient the LV long axis to the z-axis and its origin of a reference Cartesian coordinate system; and automatically reorienting and realigning the tomographic images based on the translation and rotation values.