Hierarchical Heart Valve Motion Modeling

The patent segments the complex four-dimensional volumetric data into discrete time-resolved frames representing different phases of the cardiac cycle. By dividing the continuous volumetric dataset into manageable temporal segments, the system can process and analyze valve motion at specific cardiac phases independently, reducing the overall computational complexity while preserving complete structural and dynamic information.

The patent applies preliminary image processing and registration steps to the volumetric scan data before detailed valve assessment. By pre-processing the four-dimensional data to establish spatial-temporal alignment and extract key anatomical structures in advance, the system simplifies subsequent analysis of valve motion and morphology, making the detection and measurement processes more tractable.

The patent implements automated algorithms that perform valve detection, segmentation, and measurement without requiring manual intervention. The system self-calibrates by automatically identifying anatomical landmarks and valve structures within the volumetric data, then autonomously computes motion parameters and morphological measurements across the cardiac cycle, eliminating time-consuming manual processing while maintaining high measurement precision.

The patent replaces manual mechanical measurement methods with automated computational image analysis. Instead of physical calipers or manual tracing on images, the system uses digital signal processing, pattern recognition, and 3D reconstruction algorithms to automatically extract valve geometry and motion characteristics from the volumetric scan data, dramatically increasing productivity while preserving measurement accuracy.

The patent implements patient-specific valve models that capture local anatomical variations in valve geometry and motion patterns. Rather than applying uniform generic parameters, the system extracts and models patient-specific characteristics such as individual leaflet shapes, annulus geometry, and unique motion trajectories from each patient's volumetric scan data, enabling precise adaptation to local anatomical quality while managing complexity through automated parameter extraction.

The patent dynamically adjusts model parameters based on patient-specific data extracted from the volumetric scans. The system varies geometric parameters, material properties, and boundary conditions to reflect individual patient anatomy and physiology, transforming fixed generic models into adaptive patient-specific representations. This parameter customization is achieved through automated image-to-model mapping that translates scan data directly into model parameters.