Stochastic Backprojection for CT Image Reconstruction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current medical imaging techniques, such as CT, face challenges in computationally intensive image reconstruction that can lead to artifacts due to mismatched forward and backprojection operators, especially in scenarios with varying voxel sizes and multi-resolution approaches, resulting in long reconstruction times and degraded image quality.
Innovation Solution
The implementation of stochastic backprojection, which involves randomly perturbing the location of x-rays within voxels during the reconstruction process, allowing for the use of simplified, mismatched forward and backprojection operators to reduce computational burden and artifacts, while maintaining image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional backprojection operators are used for image reconstruction, then image quality can be maintained, but computational burden increases and reconstruction time lengthens
Solution Approach 1:
The patent employs simplified, mismatched forward and backprojection operators that are computationally less expensive than conventional matched operators. These simplified operators sacrifice some precision individually but work together to produce accurate reconstructions much faster, analogous to using disposable, low-cost components that collectively achieve the desired function without requiring expensive, precision-engineered parts for each step
Solution Approach 2:
The patent changes the parameters of the projection operators by deliberately introducing mismatches between forward and backprojection operators. Instead of using precisely matched operators with high computational cost, the system uses operators with modified parameters (different sampling patterns, resolutions, or geometries) that reduce computational burden while maintaining reconstruction accuracy through the stochastic framework
2Manufacturing precision
If matched forward and backprojection operators are used, then image accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent uses computationally simple, mismatched projection operators that can be applied rapidly without the need for complex, time-consuming matched operator pairs. These simplified operators serve as efficient approximations that achieve accurate results when used in the stochastic iterative reconstruction framework
Solution Approach 2:
The patent applies partial matching between forward and backprojection operators rather than full matching. By intentionally using mismatched operators with different levels of approximation, the system reduces computational complexity while still achieving sufficient accuracy for clinical applications, avoiding the excessive computational requirements of fully matched operators
3Measurement precision
If high-resolution reconstruction is performed for the entire volume, then image quality is improved, but computational burden and processing time increase significantly
Solution Approach 1:
The patent applies different resolution levels to different regions of the imaging volume. High-resolution reconstruction is applied only to regions of interest where diagnostic accuracy is critical, while peripheral or less critical regions are reconstructed at lower resolutions. This localized approach maintains image quality where needed while dramatically reducing overall computational burden and reconstruction time
Solution Approach 2:
The patent divides the imaging volume into multiple regions or segments with different resolution requirements. By segmenting the reconstruction problem into high-priority and low-priority regions, the system can allocate computational resources efficiently, performing detailed reconstruction only where necessary and using coarser reconstruction elsewhere, thereby reducing total processing time while maintaining diagnostic quality
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Stochastic backprojection significantly reduces artifacts and achieves faster reconstruction times, with a ten-times reduction in root-mean-squared error and 3.3 times lower runtime compared to conventional methods, while maintaining equivalent image quality, especially in multi-resolution and mismatched voxel size scenarios.
Implementation Method 1
each detector signal of a plurality of the detector signals is obtained from an x-ray passing through the location at a different viewing angle
Data Source
AI summary
Techniques for computed tomography (CT) image reconstruction are presented. The techniques can include acquiring, by a detector grid of a computed tomography system, detector signals for a location within an object of interest representing a voxel, where each detector signal of a plurality of the detector signals is obtained from an x-ray passing through the location at a different viewing angle; reconstructing a three-dimensional representation of at least the object of interest, the three-dimensional representation comprising the voxel, where the reconstructing comprises computationally perturbing a location of each detector signal of the plurality of detector signals within the detector grid, where the computationally perturbing corresponds to randomly perturbing a location of the x-ray within the voxel; and outputting the three-dimensional representation.