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Respiratory Motion Compensation Using Diaphragm Tracking for Cone-Beam C-Arm CT: a Simulation and a Phantom Study

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Respiratory Motion Compensation Using Diaphragm Tracking for Cone-Beam C-Arm CT: a Simulation and a Phantom Study Hindawi Publishing Corporation International Journal of Biomedical Imaging Volume 2013, Article ID 520540, 10 pages http://dx.doi.org/10.1155/2013/520540 Research Article Respiratory Motion Compensation Using Diaphragm Tracking for Cone-Beam C-Arm CT: A Simulation and a Phantom Study Marco Bögel,1 Hannes G. Hofmann,1 Joachim Hornegger,1 Rebecca Fahrig,2 Stefan Britzen,3 and Andreas Maier3 1 Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, 91058 Erlangen, Germany 2 Department of Radiology, Lucas MRS Center, Stanford University, Palo Alto, CA 94304, USA 3 Siemens AG, Healthcare Sector, 91301 Forchheim, Germany Correspondence should be addressed to Andreas Maier; [email protected] Received 21 February 2013; Revised 13 May 2013; Accepted 15 May 2013 Academic Editor: Michael W. Vannier Copyright © 2013 Marco Bogel¨ 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. Long acquisition times lead to image artifacts in thoracic C-arm CT. Motion blur caused by respiratory motion leads to decreased image quality in many clinical applications. We introduce an image-based method to estimate and compensate respiratory motion in C-arm CT based on diaphragm motion. In order to estimate respiratory motion, we track the contour of the diaphragm in the projection image sequence. Using a motion corrected triangulation approach on the diaphragm vertex, we are able to estimate a motion signal. The estimated motion signal is used to compensate for respiratory motion in the target region, for example, heart or lungs. First, we evaluated our approach in a simulation study using XCAT. As ground truth data was available, a quantitative evaluation was performed. We observed an improvement of about 14% using the structural similarity index. In a real phantom study, using the artiCHEST phantom, we investigated the visibility of bronchial tubes in a porcine lung. Compared to an uncompensated scan, the visibility of bronchial structures is improved drastically. Preliminary results indicate that this kind of motion compensation can deliver a first step in reconstruction image quality improvement. Compared to ground truth data, image quality is still considerably reduced. 1. Introduction investigated intensively in the literature [2–4], the problem of respiratory motion during cardiac C-arm CT is much less C-arm CT has enabled reconstruction of 3D images dur- frequently addressed. Residual respiratory motion during the ing medical procedures, for example, cardiac interventions. cardiac scan causes a considerable reduction in image quality. However, the rather long acquisition time of several seconds Motion artifacts are also very problematic in pulmonary may lead to motion artifacts, such as motion blur and procedures. In order to analyze the malignancy of a pul- streaks. These artifacts are very problematic in many clinical monary tumor, a sample has to be extracted. A bronchoscope applications. The commonly used technique to reduce the is inserted through the patient’s nose and has to be navigated influence of respiratory motion during cardiac procedures is through the bronchial tree towards the tumor. This procedure the so-called single breath-hold scan. This approach requires requires an accurate plan of the bronchial tree. However, most the patient to hold his breath for the duration of the scan. tumors are only accessible through bronchi with diameters Unfortunately, this technique does not guarantee perfect of less than 2 mm. Therefore, we require accurate imaging results. Jahnke et al. have measured residual respiratory without motion blur, otherwise the small bronchi are not motion in almost half of their test group containing 210 visible. Respiratory motion can be reduced with a jet ven- people [1].Wehavetwomainapplicationsinthefocusof tilatorthatinflatesthelungwithoxygen.Therearetwo our work. One is the improvement of cardiac C-arm CT. downsides to this approach. The efficiency of this approach While compensation of the motion of the heart has been depends on the amount of pressure that is used. While too 2 International Journal of Biomedical Imaging small pressure results in residual motion, too high pres- shaped hemidiaphragms. Therefore, it is necessary for the sure may cause rupture and pneumothorax in consequence. user to select the one to be tracked. The user selects a Additionally, natural reflexes of the human body may also point roughly located at the top of the desired contour. cause residual motion. Therefore, it is necessary to develop Subsequently, we define a rectangular Region of Interest new methods to estimate and compensate respiratory motion (ROI) symmetrically around the selection. We propose an in C-arm CT. There are many different ways to acquire ROI of size 250×55 forprojectionimagesofsize640×480.The respiratory signals. Most are based on additional equipment, imageisthenpreprocessedusingagaussianlow-passfilter for example, Time-of-Flight or stereo vision cameras [5]. and the Canny edge detector. Other techniques try to extract the respiratory signal directly In the next step, the Random Sample Consensus from the projection images. Using an image-based approach (RANSAC) [14]isusedtofitaparaboliccurvetotheobtained the extracted respiration signal is perfectly synchronized set of edge points. RANSAC can deal with datasets with large with the projection images. Image-based respiratory motion percentages of gross errors and is thus the ideal choice to extraction often relies on tracking of fiducial markers in the fitamodeltoourverynoisysetofpoints.Theaimofthis projection images [6, 7]. Wang et al. have shown that the method is to model the diaphragm as a quadratic function 2 motion of the diaphragm is highly correlated to respiration- V = ++,where and V are the detector coordinates. induced motion of the heart [8].Sonkeetal.proposeto The parabolic model is a good fit for the top of the diaphragm, extract a 1D respiration signal by projecting diaphragm- in which we are interested in. The asymmetry in lower parts like features on the superior-inferior axis and selecting the ofthediaphragmdoesnotaffectourmodel,asourROIlimits features with the highest temporal change [9]. However, the the estimation to the top region. The parabolic model allows downside of this approach is that the extracted signal is not for very fast model estimation, as well as a simple extraction the real respiration signal. Due to perspective projection, of the diaphragm vertex. RANSAC has to estimate the three the projected amplitude depends on the C-arm rotation parameters , ,and. In the first step, three random points angle. Kavanagh et al. recently proposed a similar approach are selected. The model estimation is then formulated as the analyzing the intensity values between projections that works following optimization problem: without any external or internal oscillating structures [10]. 3 Another recent approach by Chen and Siochi tracks the 2 2 ∑(⋅ +⋅ +−V) → min . (1) diaphragm using a combination of Hough Transform and =1 Active Contours and an interpolated ray-tracing algorithm to estimate a respiration signal [11]. Vergalasova et al. proposed Atotalof models are estimated, each based on different a Fourier Transform based approach that also works without randomly selected points, and evaluated to determine the best any markers [12]. one. A model’s quality is defined by the number of inliers. In this paper, we propose to estimate respiratory motion An inlier is a point that lies within a predefined distance by tracking the diaphragm in the projection image sequence. to the model. Since an accurate model is desired, we only The tracked position of the diaphragm top is used to compute consider points with a one pixel distance to the model inliers. a 1D respiration signal, which is then incorporated into Subsequently, out of the estimated models we choose the reconstruction algorithm to compensate for respiratory the one with the highest number of inliers. Assuming small motion in the volume of interest. motion between subsequent frames, the contour is tracked by calculating the current contour’s vertex and using it as the startpointinthesubsequentframe. 2. Materials and Methods One additional important optimization is made. Instead of continuing to use the rectangular ROI, we restrict it to The proposed method is composed of three major steps that a parabolic ROI based on the model from the previous are each discussed in the following sections. In the first frame. The parabolic region should be of sufficient height step, the contour of the diaphragm is tracked throughout to contain the current diaphragm. In our experiments we theentireprojectionimagesequence.Basedonthistracking, used a parabolic region of 21 pixels height, centered around we are able to obtain the 2D projection of the diaphragm the previous model. This approach decreases the number top for each image. In the second step, a motion corrected of points we have to consider in the model estimation triangulation approach is used to compute the 3D position of drastically. the diaphragm top for each projection. Assuming superior- To guarantee accurate tracking in projections where inferior respiratory motion, the 1D respiration signal is both hemidiaphragms are visible in the ROI, we propose extracted. In the final step, the respiration signal is used to additional constraints based on the
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