Multi-Resolution Image Reconstruction via Segmented Voxel Projections
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Solution Overview
Problem
Traditional image reconstruction techniques in PET technology struggle to simultaneously reconstruct images of different object portions using varying reconstruction parameters, requiring significant computational resources and being complex in nature.
Innovation Solution
A method and system for multi-resolution image reconstruction that involves determining specific regions of an object, performing forward and back projections on voxels, and using iterative algorithms like the Ordered Subset Expectation Maximization algorithm, along with image matrix processing and lookup table generation to optimize image reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional reconstruction techniques are used to reconstruct images of different object portions with varying parameters, then image quality can be maintained, but computational complexity and resource requirements increase significantly
Solution Approach 1:
The patent divides the object into multiple regions of interest (ROIs), each with its own image matrix. The reconstruction process is segmented to operate independently on each ROI using region-specific parameters, rather than processing the entire object uniformly. This segmentation allows tailored reconstruction for each region while reducing overall computational burden.
Solution Approach 2:
The patent applies local quality by allowing different reconstruction parameters (such as voxel size, filtering, and iteration numbers) to be used for different regions of the object. Each ROI can be reconstructed with optimal parameters specific to its characteristics, improving image quality locally without requiring the same high computational resources for all regions.
2Area of stationary object
If traditional reconstruction techniques are used to handle multiple regions with different parameters, then comprehensive image coverage is achieved, but processing time and computational resources increase
Solution Approach 1:
The patent segments the reconstruction task by creating separate image matrices for different regions of interest. Each matrix is processed independently with region-specific parameters, allowing parallel or sequential processing that reduces total computation time compared to processing the entire object with a single set of parameters.
Solution Approach 2:
The patent applies partial action by focusing computational resources only on specific regions of interest rather than processing the entire object. By selecting only the relevant ROIs that require detailed reconstruction, the system avoids unnecessary computation on areas that do not require the same level of detail, thereby reducing processing time.
Data Source
AI summary
A system and method for image reconstruction are provided. A first region of an object may be determined. The first region may correspond to a first voxel. A second region of the object may be determined. The second region may correspond to a second voxel. Scan data of the object may be acquired. A first regional image may be reconstructed based on the scan data. The reconstruction of the first regional image may include a forward projection on the first voxel and the second voxel and a back projection on the first voxel.