Iterative Image Reconstruction with Preconditioner Denoising
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Solution Overview
Problem
Conventional model-based iterative reconstruction (MBIR) techniques in radiographic 3-D imaging are computationally intensive and slow in convergence, leading to time-consuming processing and non-uniform noise in reconstructed images.
Innovation Solution
A method employing a preconditioner for both tomographic updates and spatially varying denoising, using a gradient-based algorithm and a spatially varying denoising algorithm as a function of the preconditioner to accelerate convergence and improve noise uniformity in image reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If model-based iterative reconstruction (MBIR) techniques are used, then image quality and noise uniformity are improved, but processing time and computational complexity increase
Solution Approach 1:
The patent applies preliminary action by performing denoising operations on the projection data before the main iterative reconstruction process. By pre-processing the input data to reduce noise, the reconstruction algorithm converges faster to a high-quality solution, reducing overall processing time while maintaining image quality.
Solution Approach 2:
The reconstruction process is segmented into distinct stages: denoising of projection data, iterative reconstruction with objective function minimization, and final image generation. This segmentation allows each stage to be optimized independently, improving overall efficiency.
2Manufacturing precision
If MBIR techniques are used, then noise uniformity is improved, but computational intensity increases
Solution Approach 1:
Denoising is performed as a preliminary step before iterative reconstruction, which reduces the noise burden on the main algorithm. This allows the reconstruction to achieve uniform noise levels with fewer iterations, reducing computational complexity.
Solution Approach 2:
The patent modifies the standard MBIR approach by incorporating denoising operations and adjusting the objective function parameters to balance noise uniformity with computational efficiency. This changes the processing parameters to achieve better noise characteristics without proportionally increasing computational load.
3Object-affected harmful factors
If fewer 2-D projection images are used, then radiation dose is reduced, but reconstruction quality may deteriorate
Solution Approach 1:
The patent converts the limitation of having fewer projection images (which would normally degrade quality) into an advantage by applying denoising techniques. The denoising process extracts useful signal information from the limited noisy projections, allowing high-quality reconstruction from reduced-dose data.
Solution Approach 2:
Denoising algorithms act as an intermediary between the limited projection data and the final reconstruction. This intermediary process enhances the quality of input data, enabling accurate reconstruction even when the number of projections is reduced for lower radiation dose.
Data Source
AI summary
A method for reconstructing a volume image of a subject, executed at least in part by a computer, accesses projection x-ray images of the subject and performs a volume image reconstruction using the x-ray images by iteratively performing alternating steps of a tomographic update, where a gradient based algorithm having a preconditioner is used to update the volume image reconstruction and a spatially varying denoising that is a function of the preconditioner. The method displays, stores, or transmits the volume image reconstruction.