Mathematical Problems in Engineering

Volume 2017 (2017), Article ID 4850317, 8 pages

https://doi.org/10.1155/2017/4850317

## Relaxation Factor Optimization for Common Iterative Algorithms in Optical Computed Tomography

^{1}School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China^{2}Sichuan Province Key Laboratory of Signal and Information Processing, Xihua University, Chengdu 610039, China^{3}Hiroshima Institute of Technology, Hiroshima 731-5193, Japan

Correspondence should be addressed to Wenbo Jiang

Received 12 April 2017; Revised 6 June 2017; Accepted 12 June 2017; Published 16 July 2017

Academic Editor: Eric Feulvarch

Copyright © 2017 Wenbo Jiang and Xiaohua Zhang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Optical computed tomography technique has been widely used in pathological diagnosis and clinical medicine. For most of optical computed tomography algorithms, the relaxation factor plays a very important role in the quality of the reconstruction image. In this paper, the optimal relaxation factors of the ART, MART, and SART algorithms for bimodal asymmetrical and three-peak asymmetrical tested images are analyzed and discussed. Furthermore, the reconstructions with Gaussian noise are also considered to evaluate the antinoise ability of the above three algorithms. The numerical simulation results show that the reconstruction errors and the optimal relaxation factors are greatly influenced by the Gaussian noise. This research will provide a good theoretical foundation and reference value for pathological diagnosis, especially for ophthalmic, dental, breast, cardiovascular, and gastrointestinal diseases.

#### 1. Introduction

Computed tomography (CT) was firstly proposed in 1970s. From then on, many research results on theoretical analysis and actual applications of CT were obtained [1–11]. With the continuous development of CT, more and more research branches are formed. As a new branch of CT, optical computed tomography (OCT) was firstly proposed in 1990s, which has the advantages of noncontact, nonimmersion, and nondestruction. In the next few decades, OCT has attracted widespread attentions from biomedical or related field scientists all over the world [12–15], especially in pathological diagnosis and clinical medicine. Until now, it has shown great application value in ophthalmic [16, 17], dental [18–20], breast [21–23], cardiovascular [24, 25], and gastrointestinal diseases [26, 27]. Other applications are still under further study.

In general, analytic method and iterative method are usually used to reconstruct the image in OCT. The foundation of analytic method is continuous signal model, which is sensitive to noise and requires complete projection data [5]. However, the complete projection data are often not available in clinical application, which are restricted by these factors of the detection environment, detection time, and the characteristics of the detected object. For example, it is necessary to scan the breast quickly due to safe radiation dose, and only sparse projection data can be obtained [21–23]. The complete projection data also cannot be obtained in dental diagnosis due to limited sampling angle [18–20]. From the view of mathematics, the problem of reconstructing an image from incomplete data is usually considered as an underdetermined problem.

Compared with the analytic method, the iterative method can obtain good reconstruction quality in the case of low SNR and incomplete projection data, which is more suitable for clinical application. The common iterative algorithms include algebraic reconstruction technique (ART) [28], multiplicative algebraic reconstruction technique (MART) [29], and simultaneous algebraic reconstruction technique (SART) [30].

As we know, for common reconstruction algorithms, the quality of the reconstruction image is related to the number of projection directions, the number of sampling points for each projection direction, the range of the field of view, and so forth. Besides these, some other factors are also important for iterative algorithms, such as relaxation factor, basic function, prior knowledge, and iterative times. Among these factors, the relaxation factor plays a very important role in the quality of the reconstruction image.

In this paper, the optimal relaxation factors of the ART, MART, and SART algorithms for bimodal asymmetrical and three-peak asymmetrical tested images are analyzed and discussed. The reconstructions with Gaussian noise are also considered to evaluate the antinoise ability of the above three algorithms; some related numerical simulation results are also presented.

#### 2. Theory

According to the mathematical foundations of computed tomography [5, 31], the optical computed tomography reconstruction can be expressed aswhere represents the known image data vector which can be obtained from an imaging device, such as medical apparatus and instruments. represents the projection matrix which can be chosen through one or more optimal criterions, which is related to the basic function. represents the unknown data which we want to reconstruct. is the reconstruction error vector. The purpose of reconstruction is to minimize . In order to minimize , there are many algorithms, such as the Fourier transform algorithm [32, 33], convolution opposite projection algorithm [34], and iterative algorithm [28–30]. For incomplete projection data reconstruction, the iterative algorithm is the best from the aforementioned three methods. In this section, the reconstruction theories and iterative formulas of three common iterative algorithms are proposed, respectively. They have similar iterative steps but different iterative formulas.

##### 2.1. Algebraic Reconstruction Technique (ART)

The detailed steps of the ART are as follows.

*Step 1. *Set the initial value of = arbitrary value.

*Step 2. *According to the iterative formula, calculate .

*Step 3. *According to the iterative formula, calculate .

*Step 4. *Continue to iterate until the preconditions are satisfied and the iteration ends.

The iterative formula can be expressed aswhere ; is the iterative times; is total number of projection rays, which is related to the number of projection directions and the number of sampling points for each projection direction; is the th row of the projection matrix; is the relaxation factor.

It is not difficult to prove that the sequence generated by the iterative algorithm converges to a vector when the relaxation factor is appropriate.

From formula (2), one can see that only the pixels passing through the th ray are considered during the pixel values correction of . This correction method is called “line by line correction.”

##### 2.2. Multiplicative Algebraic Reconstruction Technique (MART)

The detailed steps of MART are similar to that of ART, but the initial value of cannot be set as 0. The iterative formula can be expressed aswhere is the maximum of the projection matrix; other parameters are the same as the ART algorithm.

From formula (3), one can see that only the pixels passing through the th ray are considered during the pixel values correction of . The only difference is that formula (2) is corrected by adding a number, and formula (3) is corrected by multiplying by a number. This correction method is also called “line by line correction.”

##### 2.3. Simultaneous Algebraic Reconstruction Technique (SART)

In the above two iterative algorithms, only one ray is considered for each iteration, and if the projection of this ray contains errors, the solution will also have errors. To avoid these errors, SART was proposed.

The iterative formula can be expressed asFrom formula (4), one can see that all the rays are considered for each iteration step, some random errors are averaged, and the total errors are reduced. Besides this, the initial value of is quite different from the above two iterative algorithms, where . This correction method is also called “point by point correction.”

#### 3. Simulations

##### 3.1. Initial Parameters

Although the quality of the reconstruction image is related to many factors, the relaxation factor plays the most important role in it. In this numerical simulation, we fix the values of other parameters to evaluate the influence rules of relaxation factor on the above three iterative algorithms and figure out the optimal value of relaxation factor.

The number of projection directions is 4, the number of grids is 25 × 25, the range of the field of view is 180°, the basis function is sinc-function, the iterative time is 30, and the bimodal asymmetrical function and three-peak asymmetrical function are chosen as tested images.

Figure 1 shows the bimodal asymmetrical image, which can be expressed by [35]And Figure 2 shows the three-peak asymmetrical image, which can be expressed by [35]