CT Motion Compensation with Tissue Density Retention
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Solution Overview
Problem
Conventional motion compensation techniques in CT imaging fail to retain tissue density differences across different motion phases, resulting in loss of tissue density information for moving structures like lung tissue during respiratory cycles.
Innovation Solution
A method that reconstructs projection data using a supplemented motion vector field, which accounts for both motion and tissue density changes by scaling voxel positions and intensities based on tissue volume changes, allowing for accurate retention of tissue density differences across motion states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If motion compensation is applied to correct for organ motion during CT scanning, then image quality and signal-to-noise ratio are improved, but tissue density information is lost due to averaging across different motion states
Solution Approach 1:
The patent segments the motion correction process into two independent components: a motion field that handles spatial displacement and a density field that handles tissue density variations. By dividing the correction into these separate segments, the system can preserve density information while still correcting for motion artifacts.
Solution Approach 2:
The patent introduces a density field as an intermediary component that works alongside the motion field. This density field acts as a mediator that carries and preserves tissue density information through the motion compensation process, preventing the loss of density data that occurs in conventional approaches.
2Object-affected harmful factors
If conventional motion compensation averages intensity information from different motion states, then motion artifacts are reduced, but differential tissue density between respiratory states cannot be differentiated
Solution Approach 1:
The patent separates motion correction from density preservation by using distinct fields for each function. The motion field corrects spatial displacements to reduce motion artifacts, while the density field independently preserves tissue density information, allowing both goals to be achieved simultaneously.
Solution Approach 2:
The patent applies different correction characteristics to different aspects of the image data. The motion field applies spatial transformation to correct for organ displacement, while the density field applies density-specific transformation to preserve local tissue density characteristics, allowing each aspect to be optimized independently.
Data Source
AI summary
A method includes reconstructing the projection data based on a reconstruction algorithm that compensates for both motion and tissue density changes of the moving organ across different motion phases, thereby generating motion and density compensated image data. A data compensator includes a reconstructor that reconstructs motion compensated image data based on an reconstruction algorithm that compensates for tissue density changes in a moving object.