Tomographic Projection Image Generation Using Constrained Weighting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for generating thick slabs from tomographic images, such as Average Intensity Projection (AIP) and Maximum Intensity Projection (MIP), result in loss of diagnostic information, reduced contrast, and blurred edges, making them unsuitable for thick slabs necessary for evaluating breast tissue lesions, which complicates data transfer and increases radiologist workload.
Innovation Solution
A method involving weighting pixels along projection beams with factors constrained to a specific range to homogenize noise, ensuring the sum of squared weighting factors falls within given limits, which enhances the sharpness and contrast of projection images, effectively reducing noise and preserving diagnostic details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If thick slabs are generated using conventional methods (AIP or MIP), then data volume is reduced and data transfer is facilitated, but diagnostic information is lost with reduced contrast and blurred edges
Solution Approach 1:
The patent changes the weighting parameter from uniform (AIP) or max-selection (MIP) to a constrained optimization approach where weights are determined by minimizing a cost function that balances information preservation and noise homogenization. This parameter change enables thick slab generation while maintaining diagnostic quality.
Solution Approach 2:
The patent introduces feedback through an iterative optimization process where the weighting factors are adjusted based on the cost function that evaluates both information preservation and noise characteristics. This feedback mechanism allows the system to automatically find optimal weights for thick slab reconstruction.
2Ease of operation
If thick slabs are generated using conventional methods, then data transfer is facilitated, but image sharpness and contrast deteriorate
Solution Approach 1:
The patent changes the weighting parameter from uniform (AIP) or max-selection (MIP) to a constrained optimization approach where weights are determined by minimizing a cost function that balances information preservation and noise homogenization. This parameter change enables thick slab generation while maintaining diagnostic quality.
3Loss of time
If thick slabs are generated using conventional methods, then radiologist workload is reduced, but diagnostic accuracy decreases due to information loss
Solution Approach 1:
The patent introduces feedback through an iterative optimization process where the weighting factors are adjusted based on the cost function that evaluates both information preservation and noise characteristics. This feedback mechanism allows the system to automatically find optimal weights for thick slab reconstruction.
Solution Approach 2:
The patent introduces an intermediary optimization process that mediates between the conflicting requirements of thick slab generation and diagnostic quality preservation. The cost function acts as an intermediary criterion that balances multiple objectives.
Data Source
AI summary
A method for generating a projection image from tomographic images first captures (S101) a number of stacked two-dimensional tomographic images representing a tomographic volume. The pixels of the stacked two-dimensional tomographic images are weighted (S102) along a number of projection beams through the tomographic volume with weighting factors that are chosen under the constraint that the sum of all squared weighting factors for each individual projection beam is between a given lower limit and a given upper limit. Then the weighted pixels are summed up (S103) along the number of projection beams for generating the two-dimensional projection image.