Cardiac Anatomy Shape Reconstruction from Aligned 2D Images
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Solution Overview
Problem
Cardiac MRI acquisition often results in poor 3D shape determination due to misalignment of imaging planes, motion artifacts, poor image quality, and irregular heart motion, which are not accurately addressed by existing 2D image processing methods.
Innovation Solution
A method involving alignment of contours from multiple 2D images using translation and rotation, combined with a motion model fitting process, to accurately reconstruct 3D cardiac anatomy, including the use of anatomical landmarks and adaptive motion models to handle irregularities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If 2D imaging is used for cardiac MRI, then acquisition time is reduced and imaging speed is improved, but 3D shape reconstruction accuracy deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-aligning contours from multiple 2D images using translation and rotation transformations before reconstructing the 3D shape. This pre-processing step prepares the 2D data in advance to ensure accurate 3D reconstruction, resolving the contradiction between fast 2D acquisition and accurate 3D reconstruction.
Solution Approach 2:
The patent transitions from 2D imaging to 3D reconstruction by adding the temporal dimension and using motion models that deform 2D contours into 3D shapes. This dimensional transformation enables accurate 3D shape determination while maintaining the speed benefits of 2D acquisition.
2Measurement precision
If registration algorithms are applied to improve slice alignment, then 3D shape determination accuracy is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent performs preliminary alignment of contours using translation and rotation transformations before applying more complex registration algorithms. This staged approach reduces the computational burden on subsequent processing steps while maintaining alignment accuracy.
Solution Approach 2:
The patent segments the alignment process into multiple steps: initial contour extraction, translation alignment, rotation alignment, and final registration. This segmentation allows each step to be optimized independently, reducing overall processing time while improving accuracy.
3Reliability
If multiple motion models are tested and fitted, then robustness against irregular motion and poor image quality is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent applies partial action by testing a limited number of motion models (including at least a first motion model and at least one additional motion model) rather than exhaustively testing all possible models. This approach provides sufficient robustness against irregular motion while controlling computational complexity.
Solution Approach 2:
The patent uses feedback mechanisms to evaluate the fit of each motion model against the aligned contours and selects the model that provides the best fit. This feedback-driven selection process ensures reliable results while avoiding unnecessary computation on poorly fitting models.
Data Source
AI summary
For shape determination of cardiac anatomy with a medical imager, irregularities in motion, poor image quality, and misalignment of imaging planes are counteracted by a process relying on alignment of contours in combination with selection and fitting of a motion model. Contours are extracted from 2D images and aligned for each frame, which is extracted from the sequence of 2D images. The alignment may use a translation for each frame and rotation across frames for improved performance. A motion model is fit to the aligned contours and tested. If insufficient (greater than threshold difference), other motion models are aligned and tested. Motion models may be created on demand for improved performance. If sufficient, the shape of the heart structure is determined from the fit model.