Fast EM Ordered-Subsets Algorithm for CT Image Reconstruction
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Solution Overview
Problem
Existing EM algorithms for computed tomography (CT) suffer from long processing times, limiting their use in applications such as nondestructive testing and medical imaging due to their inefficiency in image reconstruction.
Innovation Solution
An improved EM algorithm, referred to as the Fast EM Ordered-Subsets (FEMOS) algorithm, which employs ordered-subsets and super-resolution techniques to enhance image reconstruction, allowing for finer spatial sampling and smoothing operations, thereby reducing processing time and improving image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional EM algorithm is used for image reconstruction, then image quality is maintained, but processing time becomes excessively long
Solution Approach 1:
The projection data is divided into multiple ordered subsets, each processed separately in successive iterations. This segmentation allows the algorithm to make progress with partial data at each iteration, significantly accelerating convergence while maintaining reconstruction quality through the ordered subset update scheme.
2Measurement precision
If spatial sampling is enhanced to finer resolution, then image resolving power is improved, but computational complexity increases
Solution Approach 1:
A smoothing operation is applied to the projection data before the iterative reconstruction process begins. This preliminary action reduces high-frequency noise and computational complexity in the data, allowing the subsequent fine-resolution reconstruction to converge faster while maintaining accuracy.
Data Source
AI summary
Disclosed is a method, program product, and computer system that provides iterative computed tomography (CT) image reconstruction. The approach produces an image whose resolving power exceeds that of conventional algorithms, and utilizes an inner and out iterative loop, configured by ordered subsets criteria, to perform: projecting a reconstructed image; resampling a resulting calculated projection, thereby enabling super-resolution; calculating a comparison quantity with the collected projection array (e.g., sinogram); backprojecting onto a correction array a function that utilizes the comparison quantity; and generating a new reconstructed image with an operation involving the correction array.