Cavity Wall Surface Model Segmentation for Heart Chamber Imaging
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Solution Overview
Problem
Existing medical imaging processes struggle to create precise surface models of cavity walls with non-smooth interiors, such as the human heart, due to the presence of trabeculae and papillary muscles, leading to inaccuracies in volume measurement and inter-observer variability.
Innovation Solution
A dynamic surface model is established using region-growing, threshold procedures, edge detection, and statistical analysis to define a probability function for voxel assignment, allowing for deformation and correction of the surface model to accurately represent the cavity volume, including structures like trabeculae and papillary muscles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a surface model is created using traditional contour detection and threshold procedures, then the boundary surface can be defined, but the measurement precision deteriorates due to the non-smooth interior surface with trabeculae and papillary muscles
Solution Approach 1:
The cavity wall surface is divided into multiple segments or regions, allowing different modeling approaches to be applied to different areas. This segmentation enables the model to capture complex local geometries of trabeculae and papillary muscles while maintaining overall model manageability.
Solution Approach 2:
The invention transitions from traditional 2D contour detection to 3D volumetric modeling approaches. By incorporating depth information and spatial relationships in three dimensions, the model can accurately represent the complex interior surface structures that cannot be adequately captured by planar methods.
2Manufacturing precision
If a decision-making function based on grey level threshold is used to define the boundary surface, then the surface can be segmented, but the manufacturing precision deteriorates due to subjective threshold selection
Solution Approach 1:
The modeling process incorporates feedback mechanisms where the initial threshold-based segmentation is evaluated and refined iteratively. The system provides feedback on segmentation quality and allows adjustment of parameters to achieve optimal boundary definition, reducing subjectivity in threshold selection.
Solution Approach 2:
Instead of relying on fixed grey level thresholds, the invention employs adaptive parameter adjustment where threshold values and other modeling parameters are dynamically changed based on local image characteristics and anatomical knowledge, improving boundary definition accuracy across different regions.
3Measurement precision
If feature-tracking processes are used to monitor surface movement, then temporal dynamics can be captured, but the measurement precision deteriorates due to tracking errors in radial direction
Solution Approach 1:
The invention introduces intermediary reference structures or landmarks on the cavity surface that facilitate more accurate tracking. These intermediaries serve as stable reference points that improve tracking precision in the radial direction while maintaining computational efficiency.
Solution Approach 2:
The modeling approach incorporates dynamic adaptation where tracking parameters and algorithms are adjusted in real-time based on observed motion patterns. This dynamic adjustment improves tracking accuracy during radial movements while maintaining overall processing efficiency.
Data Source
AI summary
A process for creating a surface model of a surface of a cavity wall (2), especially a heart chamber including the steps of: (a) accessing at least one three dimensional image data record of the cavity; (b) creating a preliminary deformable surface model of the interior surface or the exterior surface of the cavity wall for each three dimensional image data record; (c) dividing the surface of the preliminary surface model into surface segments; (d) defining volume segments each including one surface segment and extending radially inwards and/or outwards from their associated surface segment; (e) statistical analysis of the grey levels of the voxels present in the volume segments for analyzing the volume proportion of the cavity wall, in the respective volume segment; and (f) deforming the surface segments on the basis of the volume proportion thus creating a corrected surface model.