Iterative Image Reconstruction Using Estimated Update Vectors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional iterative approximation methods for image reconstruction in CT and MRI devices require numerous iterations, making it difficult to generate reconstruction images within practical calculation time.
Innovation Solution
An image computing device employing a first iterative approximation processor, an estimated update image generator, and a second iterative approximation processor to reduce the number of updates required for convergence, using an estimated update vector to accelerate the image reconstruction process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional iterative approximation method is used for image reconstruction, then image quality can be improved, but the number of iterations required becomes large (dozens to hundreds of times), making it difficult to generate reconstruction images within practical calculation time
Solution Approach 1:
The patent applies preliminary action by performing a predetermined number of initial iterations (first iterative approximation process) to generate an estimated update image, which is then used as the initial image for the second iterative approximation process. This preliminary computation establishes a foundation that accelerates subsequent convergence, reducing the total number of iterations needed while maintaining image quality improvement.
Solution Approach 2:
The patent implements dynamics by switching between two different iterative approximation processes with different update rules. The first process uses a standard update rule for initial iterations, while the second process uses a modified update rule that incorporates the estimated update image. This dynamic switching allows the system to adapt its computation strategy based on the iteration stage, optimizing both convergence speed and image quality.
2Manufacturing precision
If iterative approximation method is used to improve image quality, then reconstruction precision increases, but the computational complexity and number of updates required becomes excessive for practical applications
Solution Approach 1:
The patent performs preliminary computational work by executing the first iterative approximation process for a predetermined number of iterations to generate the estimated update image. This preliminary action captures the essential update patterns needed for convergence, allowing the second process to build upon this foundation with reduced computational complexity while maintaining high reconstruction precision.
Solution Approach 2:
The patent applies parameter changes by modifying the update rule parameters in the second iterative approximation process. Specifically, the update vector is computed using the estimated update image from the first process, and the update rule incorporates scaled differences between update images. This parameter modification enables faster convergence to high-precision reconstruction results with reduced computational complexity.
Data Source
AI summary
A reconstruction image is generated with a small number of updates, with the use of an iterative approximation method. A specified tomographic image of a subject is received, and a process is performed two or more times, where an update process is performed according to the iterative approximation method, using the tomographic image as an initial image and an update image is obtained. Then, an update vector corresponding to a difference between thus generated update images of the update process performed twice is multiplied by predetermined coefficients, so as to generate an estimated update vector. Using this vector, an update image is generated. Then, this update image is used as a new initial image, and a process is repeated where the update process is performed according to the iterative approximation method and an update image is obtained, thereby generating a tomographic image of the subject.