Iterative Image Reconstruction for CBCT Imaging
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Solution Overview
Problem
Current radiation therapy treatment planning is time-consuming and complex due to the need for precise control of radiation beams to minimize damage to healthy tissues, especially with multiple organs at risk, and existing iterative reconstruction methods suffer from computational inefficiencies and inaccuracies in CBCT imaging.
Innovation Solution
A system that generates updates to a structural estimate of a region of interest using simulated and real X-ray measurements, invariant to the current structural estimate, through a process involving statistical objective functions, regularization, and machine learning techniques to improve image quality and convergence rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional iterative reconstruction methods are used for CBCT imaging in radiation therapy, then image reconstruction can be performed, but the process suffers from computational inefficiency and slow convergence rate
Solution Approach 1:
The patent pre-calculates and stores system matrix elements and their derivatives before the actual reconstruction process. By preparing these computational components in advance, the iterative reconstruction algorithm can proceed faster without recalculating these values during each iteration, thus improving productivity while reducing computational time loss
Solution Approach 2:
The patent modifies the objective function by incorporating regularization terms with carefully selected parameters. This changes the convergence characteristics of the iterative algorithm, allowing it to reach accurate solutions faster. The parameter optimization in the objective function balances computational efficiency with image quality, resolving the contradiction between reconstruction speed and accuracy
2Measurement precision
If conventional iterative reconstruction algorithms are used, then image reconstruction is achieved, but phantom artifacts are present and image accuracy is reduced
Solution Approach 1:
The patent converts the harmful phantom artifacts into beneficial information by using the difference between simulated and measured X-ray measurements as a gradient indicator. This gradient guides the iterative update process to specifically target and correct the artifact-prone regions, transforming the artifact problem into a solution mechanism that improves image accuracy while eliminating phantom artifacts
Solution Approach 2:
The patent implements a feedback mechanism where the difference between simulated and actual measurements is continuously fed back into the reconstruction algorithm. This feedback loop allows the system to iteratively refine the image, correcting errors and reducing phantom artifacts in each iteration, thereby improving measurement precision and image accuracy
3Measurement precision
If detailed structural estimation is performed to improve image quality, then reconstruction accuracy improves, but computational complexity and resource requirements increase
Solution Approach 1:
The patent segments the computational task into distinct components: system matrix calculation, objective function evaluation, gradient computation, and image update. By dividing the complex reconstruction process into manageable segments, the algorithm achieves detailed structural estimation with improved accuracy while keeping computational complexity manageable through modular processing
Solution Approach 2:
The patent replaces complex mechanical iterative computation with a more efficient mathematical approach using pre-calculated system matrices and closed-form gradient updates. This substitution reduces computational complexity while maintaining the ability to perform detailed structural estimation, thereby improving reconstruction accuracy without proportionally increasing device complexity
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
This approach results in faster and more accurate image reconstruction with reduced phantom artifacts, enhancing the efficiency and accuracy of radiation therapy planning and delivery by improving CBCT image quality and reducing computational resources.
Implementation Method 1
generating a first simulated X-ray measurement based on the current structural estimate of the region of interest; receiving a first real X-ray measurement
Data Source
AI summary
Systems and methods are provided for performing operations including: accessing a current structural estimate of a region of interest; generating a first simulated X-ray measurement based on the current structural estimate of the region of interest; receiving a first real X-ray measurement; and generating an update to the current structural estimate of the region of interest as a function of the first simulated X-ray measurement and the first real X-ray measurement, the update being generated invariant on the current structural estimate.