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Technical Note: Emission expectation maximization look-alike algorithms for x-ray CT and other applications
1Department of Engineering, Weber State University, Ogden, UT, 84408, USA.
Medical Physics
|July 3, 2018
Summary
This study introduces a new, user-friendly algorithm for transmission tomography reconstruction, inspired by emission tomography's expectation maximization (EM) algorithm. The novel method demonstrates improved contrast-to-noise ratio for lesion detection in computed tomography (CT) imaging.
Area of Science:
- Medical Imaging
- Computational Science
Background:
- Expectation maximization (EM) algorithms are widely used in emission tomography due to their simplicity.
- Traditional EM algorithms for transmission tomography present usability challenges and numerous issues.
- This research addresses the need for more user-friendly and effective transmission tomography algorithms.
Purpose of the Study:
- To develop a novel transmission tomography algorithm that mimics the ease of use of emission EM algorithms.
- To create a family of EM-look-alike algorithms applicable to transmission tomography, including X-ray computed tomography (CT).
Main Methods:
- The emission EM algorithm was reformulated in an additive form.
- A new weighting factor was introduced to create a family of emission-EM-look-alike algorithms.
- One algorithm from this family was specifically adapted for transmission CT.
Main Results:
- Computer simulations compared the new algorithm against a similar existing method using a CT noise model.
- The developed algorithm achieved convergence rates comparable to the existing method.
- The new algorithm demonstrated a superior contrast-to-noise ratio, enhancing lesion detection capabilities.
Conclusions:
- Emission-EM-look-alike algorithms can be derived for any noise variance function.
- These algorithms retain beneficial properties of the original emission EM algorithm, including multiplicative updates and non-negativity.
- The new algorithms offer faster convergence for bright objects and simplified implementation for transmission tomography.
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