Multi-objective optimization for structured illumination in dynamic x-ray tomosynthesis

Xu Ma; Hao Xu; Carlos M. Restrepo; Gonzalo R. Arce

doi:10.1364/AO.428871

Applied Optics
Vol. 60,
Issue 21,
pp. 6177-6188
(2021)
•https://doi.org/10.1364/AO.428871

Multi-objective optimization for structured illumination in dynamic x-ray tomosynthesis

Xu Ma, Hao Xu, Carlos M. Restrepo, and Gonzalo R. Arce

Get PDF
Email
Share
Get Citation
Copy Citation Text
Xu Ma, Hao Xu, Carlos M. Restrepo, and Gonzalo R. Arce, "Multi-objective optimization for structured illumination in dynamic x-ray tomosynthesis," Appl. Opt. 60, 6177-6188 (2021)

Export Citation
- BibTex
- Endnote (RIS)
- HTML
- Plain Text
Citation alert
Save article

Check for updates

Related Topics
Table of Contents Category
- Imaging Systems, Image Processing, and Displays
Optics & Photonics Topics
?

The topics in this list come from the Optics and Photonics Topics applied to this article.

About this Article
History
- Original Manuscript: April 26, 2021
- Revised Manuscript: June 16, 2021
- Manuscript Accepted: June 22, 2021
- Published: July 16, 2021

Accepted Manuscript
Download Accepted Manuscript
Access to this article is made available via and is subject to OSA Publishing Terms of Use.

Abstract

Dynamic coded x-ray tomosynthesis (CXT) uses a set of encoded x-ray sources to interrogate objects lying on a moving conveyor mechanism. The object is reconstructed from the encoded measurements received by the uniform linear array detectors. We propose a multi-objective optimization (MO) method for structured illuminations to balance the reconstruction quality and radiation dose in a dynamic CXT system. The MO framework is established based on a dynamic sensing geometry with binary coding masks. The Strength Pareto Evolutionary Algorithm 2 is used to solve the MO problem by jointly optimizing the coding masks, locations of x-ray sources, and exposure moments. Computational experiments are implemented to assess the proposed MO method. They show that the proposed strategy can obtain a set of Pareto optimal solutions with different levels of radiation dose and better reconstruction quality than the initial setting.

Full Article | PDF Article

More Like This

Optimization of the structured illumination series for compressive x-ray tomosynthesis

Hao Xu, Xu Ma, Qile Zhao, Carlos M. Restrepo, and Gonzalo R. Arce
Appl. Opt. 60(9) 2686-2694 (2021)

K-edge coded aperture optimization for uniform illumination in compressive spectral X-ray tomosynthesis

Tong Zhang, Shengjie Zhao, Xu Ma, Angela P. Cuadros, and Gonzalo R. Arce
Opt. Express 29(25) 41048-41066 (2021)

Compressive X-ray tomosynthesis using model-driven deep learning

Qile Zhao, Xu Ma, Gonzalo R. Arce, and Zhiqiang Wang
Opt. Express 29(15) 24576-24591 (2021)

Previous Article Next Article

Data Availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Figures (13)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Tables (1)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Equations (14)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Algorithm: Strength Pareto Evolutionary Algorithm 2
Input: The population size $D$ , external archive size $\bar{D}$ , and maximum iteration number $K$ .
1:	Step 1: Create an initial population $Q_{κ}$ and an empty external archive ${\bar{Q}}_{κ}$ . Set current iteration number as $κ = 0$ .
2:	For $κ = 0$ to $K$
3.	Step 2: Calculate the objective function values $F_{1}$ and $F_{2}$ of the individuals within $Q_{κ}$ and ${\bar{Q}}_{κ}$
4.	Step 3: Calculate the fitness values of the individuals within $Q_{κ}$ and ${\bar{Q}}_{κ}$ .
5.	Step 4: Copy all Pareto optimal individuals from $Q_{κ}$ and ${\bar{Q}}_{κ}$ to ${\bar{Q}}_{κ + 1}$ .
6.	If the number of individuals in ${\bar{Q}}_{κ + 1}$ is less than $\bar{D}$ Then
7.	Fill up ${\bar{Q}}_{κ + 1}$ with dominated individuals in $Q_{κ}$ and ${\bar{Q}}_{κ}$ .
8.	Else If the number of individuals in ${\bar{Q}}_{κ + 1}$ exceeds $\bar{D}$ Then
9.	Reduce the number of individuals in ${\bar{Q}}_{κ + 1}$ by truncation operation.
10.	Else
11.	Go to Step 5.
12.	End If
13.	Step 5: Compare $κ$ with $K$
14.	If $κ \geq K$ Then
15.	Assign the individuals in ${\bar{Q}}_{κ + 1}$ to R, and stop the algorithm.
16.	Else
17.	Go to Step 6.
18.	End If
19.	Step 6: Use the binary tournament selection operator to pick out the individuals from ${\bar{Q}}_{κ + 1}$ to the mating pool for genetic manipulations.
20.	Step 7: Apply the crossover and mutation operators to the individuals in the mating pool, and then a new population $Q_{κ + 1}$ is created. Set $κ = κ + 1$ .
21.	End For
Output: The set of Pareto optimal solutions, denoted by R.

Abstract

Data Availability

Cited By

Figures (13)

Tables (1)

Equations (14)

Applied Optics