BioMed Research International

Volume 2015 (2015), Article ID 858907, 8 pages

http://dx.doi.org/10.1155/2015/858907

## A Method to Improve Electron Density Measurement of Cone-Beam CT Using Dual Energy Technique

Department of Radiation Oncology, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China

Received 20 April 2015; Revised 19 July 2015; Accepted 22 July 2015

Academic Editor: Marco Francone

Copyright © 2015 Kuo Men et al. 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

*Purpose*. To develop a dual energy imaging method to improve the accuracy of electron density measurement with a cone-beam CT (CBCT) device. *Materials and Methods*. The imaging system is the XVI CBCT system on Elekta Synergy linac. Projection data were acquired with the high and low energy X-ray, respectively, to set up a basis material decomposition model. Virtual phantom simulation and phantoms experiments were carried out for quantitative evaluation of the method. Phantoms were also scanned twice with the high and low energy X-ray, respectively. The data were decomposed into projections of the two basis material coefficients according to the model set up earlier. The two sets of decomposed projections were used to reconstruct CBCT images of the basis material coefficients. Then, the images of electron densities were calculated with these CBCT images. *Results*. The difference between the calculated and theoretical values was within 2% and the correlation coefficient of them was about 1.0. The dual energy imaging method obtained more accurate electron density values and reduced the beam hardening artifacts obviously. *Conclusion*. A novel dual energy CBCT imaging method to calculate the electron densities was developed. It can acquire more accurate values and provide a platform potentially for dose calculation.

#### 1. Introduction

Radiotherapy aims to deliver sufficient dose to the tumor target and spare the organ at risk (OAR) around target to achieve the goal, that is, killing the tumor cell with minimum toxicity to the normal tissues. Advanced irradiation techniques such as intensity modulated radiotherapy (IMRT) [1], volumetric modulated arc therapy (VMAT) [2], and stereotactic radiation therapy (SRT) [3] can generate complex dose distribution with high dose areas firmly conformed to the target. Because of the high dose gradients at the boundary of target, the anatomical change due to weight loss, tumor shrinkage, and growth during the treatment will lead to inaccurate dose delivered to patient with respect to the initial planning. To address this problem, imaging is quite necessary in procedures of radiotherapy treatment specifically in the image guided radiation therapy (IGRT) and the adaptive radiation therapy (ART).

Cone-beam computed tomography (CBCT), implemented on linear accelerators, has been widely used in the clinic as the mainstream technology of IGRT [4–6]. It can provide volumetric information of a patient at the treatment position. Therefore it is used to verify the patient setup and detect the change of the tumor location and then correct it if necessary before treatment. Moreover, the CBCT images have the potential to delineate the patient anatomy online and determine the tissues’ electron densities which can be used to calculate the dose for adaptive treatment planning. The conversion of the CT number (in Hounsfield units, HU) into an electron density () enables the treatment planning system (TPS) to account for tissue heterogeneities. Therefore, the accuracy of patient dose calculations is largely dependent on the accuracy of the electron density.

One limitation of CBCT image quality is the serious artifacts introduced by X-ray scatter [7] and beam hardening [8]. This will reduce contrast resolution and increase noise in CBCT images, therefore affecting the HU and electron density values.

On the other hand, the HU- conversion is usually performed using a calibration phantom containing several modules with known electron densities. However, the CT numbers depend on not only the electron densities but also the effective atomic numbers. And the elemental composition of the modules is different from that of human tissues. As a result, of tissues cannot be exactly converted from the CT numbers via a voxel-to-voxel correspondence and the HU- conversion curve is often divided into several parts, assuming a linear relationship in each part [9].

Dual energy CT, first proposed by Alvarez and MacOvski [10], is a significant technological advancement in imaging. By obtaining CT data at high and low X-ray energies, dual energy CT can provide both electron density () and effective atomic number (), thus facilitating tissue type identification. It has been a hot topic in both research and application in the diagnosis field [11–16]. However, the advantages of dual energy CBCT in radiotherapy need to be further researched.

The overall purpose of this paper is to implement a dual energy CBCT imaging method to improve the measurement by reducing the beam hardening and determining the electron densities of human tissues directly without using the problematic HU- conversion curve. The method uses a CBCT imaging device mounted on a linear accelerator and two scans are performed to acquire high and low kVp images. The performance of the proposed method was evaluated using a virtual phantom simulation and experiments of a CTP 486 module and a CIRS model.

#### 2. Materials and Methods

##### 2.1. Imaging System

This work was performed using the X-ray volumetric imaging (XVI) CBCT system of an Elekta Synergy machine. The X-ray source uses a rotating anode X-ray tube (Dunlee D604, Aurora, IL) with peak tube potential of 150 kVp and maximum current of 500 mA. The kV source arm contains two slots for fitting a collimator cassette and a filtration cassette. The detector is an indirect detection flat-panel imager with a spatial resolution of 1,024 × 1,024 array of 0.4 × 0.4 mm^{2} pixels (RID1640-A11, Perkin Elmer, Wiesbaden, Germany). The source-to-axis distance and source-to-detector distance are 1,000 and 1,536 mm, respectively. Projection images can be acquired with three different “fields of view” (FOVs): small (S), medium (M), and large (L). The number of projections for a full 360-degree rotation is approximately 600. The XVI software uses a cone-beam reconstruction process based on the Feldkamp-Davis-Kress (FDK) algorithm.

##### 2.2. Basic Principles of Dual Energy Imaging Method

Dual energy imaging method is fundamentally derived from the fact that the X-ray absorption linear attenuation coefficient () of an element depends on photon energy . In the diagnostic energy range, the attenuation coefficient of a material () can be approximated by a linear combination of two basis materials [4, 12]:where and are the attenuation coefficient of the two basis materials and and are named as the basis material coefficients, respectively.

In dual energy imaging, the phantom is scanned twice with the peak tube potential of 120 kVp (high energy) and 70 kVp (low energy), respectively. For the high and low energy X-ray spectra and , the measured projections and are [4]where is the projection of basis material coefficient and is represented byThe core of dual energy imaging process is to calculate the basis material coefficient that can be derived from the attenuation measured at two different energy spectra. Once are determined, the electron density () can be calculated by [13]where and are the electron densities of the two basis materials, respectively.

##### 2.3. Basis Material Coefficients Calculation

A key step of this method is to calculate the basis material coefficients calculation ( and ) from the dual energy gray projections ( and ).

When the X-ray passes through the two basis materials with path lengths of and , the projections and measured with high and low energy X-ray spectra and areAs can be seen from (2) and (5), if the measurements () are the same in the two formulas, the pair () equals the X-ray path length pair () numerically. Because is the projection of , the basis material coefficient can be reconstructed from using the FDK algorithm.

In our method, projections () of the basis materials were acquired by changing the X-ray path lengths () through the two basis materials. Once a set of projections that covered the desired range were obtained for the respective lengths of basis material, a look-up table was generated. The look-up table related a pair of projection values (e.g., and ) at high and low energy spectra to a pair of path lengths of basis materials (e.g., and ). After the object was scanned, this look-up table was used to convert the dual energy gray projections () to the path lengths (). Then the 2D projections of path lengths (), which equaled (), were used to reconstruct a 3D image of the basis material coefficient ().

In the process of creating the look-up table, the projection data () of the basis materials were also affected by the beam hardening effect. As a result, when the look-up table was used to calculate and , the beam hardening effect had already been taken into account and this method was expected to reduce the beam hardening artifact.

##### 2.4. The Process of Dual Energy Imaging

The flowchart in Figure 1 shows the implementation procedure of the proposed method. The procedure had six steps: (1) select two known basis materials; (2) acquire a series of 2D projections of the basis materials with high and low X-ray spectra, respectively, and then create a look-up table () versus (); (3) scan the phantom with the corresponding high and low X-ray spectra and acquire projections (); (4) calculate the 2D projections () of basis material coefficients using the look-up table; (5) reconstruct the 3D images of basis material coefficients () using the Elekta XVI software; (6) calculate the electron densities according to (4).