Lung Motion Vector Field Estimation for Surgical Planning
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Solution Overview
Problem
Current surgical planning systems for lung procedures fail to accurately compensate for the rhythmic motion of the lung due to breathing, which can lead to improper placement of implants or devices and potential patient injury, as they do not adequately account for posture and position changes between different states of lung expansion.
Innovation Solution
A method and system that involves obtaining 3D CT images of the lung at different states, performing rigid registration, followed by deformable registration to generate a motion vector field representing local lung motion, and displaying areas of greatest motion intensity, allowing for the computation and display of displacement of medical implements based on this vector field.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rigid registration is performed between 3D CT images to align images, then image alignment is improved, but local lung motion compensation is insufficient
Solution Approach 1:
The patent segments the lung into multiple regions of interest (ROIs) and performs deformable registration independently within each ROI. This allows different parts of the lung to be registered with appropriate local deformation fields, capturing local motion patterns while maintaining overall alignment. The segmentation enables the system to handle the contradiction by applying rigid alignment globally while allowing flexible deformation locally.
Solution Approach 2:
The patent employs deformable registration that models the lung as a dynamic, non-rigid structure capable of local deformation. The deformable registration algorithm computes displacement vectors and deformation fields that adapt to local lung motion patterns, transforming the static image alignment problem into a dynamic one that accounts for respiratory movement. This dynamic approach resolves the contradiction by allowing the registration to be rigid where needed and flexible where local motion occurs.
2Reliability
If deformable registration is used to compensate for lung motion, then local motion compensation is improved, but computational complexity increases
Solution Approach 1:
By dividing the lung into multiple ROIs, the patent reduces the computational complexity of deformable registration. Instead of performing deformable registration on the entire lung volume at once, the system processes smaller, manageable regions independently. This segmentation strategy maintains motion compensation accuracy while significantly reducing the computational burden and algorithmic complexity.
Solution Approach 2:
The patent applies different registration strategies to different regions of the lung based on their specific motion characteristics. Regions with significant motion undergo deformable registration, while regions with minimal motion may use simpler rigid alignment. This local quality approach optimizes the balance between motion compensation accuracy and computational complexity by applying complex algorithms only where necessary.
3Loss of information
If 3D CT images are obtained at different lung states, then motion analysis capability is improved, but imaging time and patient exposure increase
Solution Approach 1:
The patent performs preliminary rigid registration to establish a coarse alignment between 3D CT images obtained at different lung states before proceeding to deformable registration. This preliminary action reduces the computational time and imaging requirements by pre-aligning the images, thereby minimizing the time needed to obtain motion information while maintaining completeness of motion analysis.
Solution Approach 2:
The patent models lung motion as a dynamic process and uses deformable registration to capture motion trajectories and displacement fields. By representing motion as a continuous deformation field rather than requiring multiple static images, the system reduces the number of imaging acquisitions needed. The dynamic model allows reconstruction of motion information from fewer time points, thereby reducing imaging time while preserving motion information completeness.
Data Source
AI summary
A medical analysis method for estimating a motion vector field of the magnitude and direction of local motion of lung tissue of a subject is described. In one embodiment a first 3D image data set of the lung and a second 3D image data set is obtained. The first and second 3D image data sets correspond to images obtained during inspiration and expiration respectively. A rigid registration is performed to align the 3D image data sets with one another. A deformable registration is perforated to match the 3D image data sets with one another. A motion vector field of the magnitude and direction of local motion of lung tissue is estimated based on the deforming step. The motion vector field may be computed prior to treatment to assist with planning a treatment as well as subsequent to a treatment to gauge efficacy of a treatment. Results may be displayed to highlight.