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Mar 2013

Volume 40, Issue 3, pp. 030401-037302-1

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Med. Phys. 40, 032305 (2013); http://dx.doi.org/10.1118/1.4790466 (12 pages)

Shannon C. Agner, Jun Xu, and Anant Madabhushi
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X-RAY COMPUTED TOMOGRAPHY: 2012 ADVANCES IN IMAGE FORMATION (Online only): Evaluation of interpolation methods for surface-based motion compensated tomographic reconstruction for cardiac angiographic C-arm data

Kerstin Müller, Chris Schwemmer, Joachim Hornegger, Yefeng Zheng, Yang Wang, Günter Lauritsch, Christopher Rohkohl, Andreas K. Maier, Carl Schultz, and Rebecca Fahrig

Med. Phys. 40, 031107 (2013); http://dx.doi.org/10.1118/1.4789593 (12 pages)

Online Publication Date: 28 February 2013

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Purpose: For interventional cardiac procedures, anatomical and functional information about the cardiac chambers is of major interest. With the technology of angiographic C-arm systems it is possible to reconstruct intraprocedural three-dimensional (3D) images from 2D rotational angiographic projection data (C-arm CT). However, 3D reconstruction of a dynamic object is a fundamental problem in C-arm CT reconstruction. The 2D projections are acquired over a scan time of several seconds, thus the projection data show different states of the heart. A standard FDK reconstruction algorithm would use all acquired data for a filtered backprojection and result in a motion-blurred image. In this approach, a motion compensated reconstruction algorithm requiring knowledge of the 3D heart motion is used. The motion is estimated from a previously presented 3D dynamic surface model. This dynamic surface model results in a sparse motion vector field (MVF) defined at control points. In order to perform a motion compensated reconstruction, a dense motion vector field is required. The dense MVF is generated by interpolation of the sparse MVF. Therefore, the influence of different motion interpolation methods on the reconstructed image quality is evaluated.
Methods: Four different interpolation methods, thin-plate splines (TPS), Shepard's method, a smoothed weighting function, and a simple averaging, were evaluated. The reconstruction quality was measured on phantom data, a porcine model as well as on in vivo clinical data sets. As a quality index, the 2D overlap of the forward projected motion compensated reconstructed ventricle and the segmented 2D ventricle blood pool was quantitatively measured with the Dice similarity coefficient and the mean deviation between extracted ventricle contours. For the phantom data set, the normalized root mean square error (nRMSE) and the universal quality index (UQI) were also evaluated in 3D image space.
Results: The quantitative evaluation of all experiments showed that TPS interpolation provided the best results. The quantitative results in the phantom experiments showed comparable nRMSE of ≈0.047 ± 0.004 for the TPS and Shepard's method. Only slightly inferior results for the smoothed weighting function and the linear approach were achieved. The UQI resulted in a value of ≈ 99% for all four interpolation methods. On clinical human data sets, the best results were clearly obtained with the TPS interpolation. The mean contour deviation between the TPS reconstruction and the standard FDK reconstruction improved in the three human cases by 1.52, 1.34, and 1.55 mm. The Dice coefficient showed less sensitivity with respect to variations in the ventricle boundary.
Conclusions: In this work, the influence of different motion interpolation methods on left ventricle motion compensated tomographic reconstructions was investigated. The best quantitative reconstruction results of a phantom, a porcine, and human clinical data sets were achieved with the TPS approach. In general, the framework of motion estimation using a surface model and motion interpolation to a dense MVF provides the ability for tomographic reconstruction using a motion compensation technique.
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87.57.Q- Computed tomography
87.57.nm Segmentation
02.60.Ed Interpolation; curve fitting
87.57.nf Reconstruction
87.59.-e X-ray imaging
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X-RAY COMPUTED TOMOGRAPHY: 2012 ADVANCES IN IMAGE FORMATION (Online only): Quantitative material characterization from multi-energy photon counting CT

Adam M. Alessio and Lawrence R. MacDonald

Med. Phys. 40, 031108 (2013); http://dx.doi.org/10.1118/1.4790692 (8 pages)

Online Publication Date: 28 February 2013

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Purpose: To quantify the concentration of soft-tissue components of water, fat, and calcium through the decomposition of the x-ray spectral signatures in multi-energy CT images.
Methods: Decomposition of dual-energy and multi-energy x-ray data into basis materials can be performed in the projection domain, image domain, or during image reconstruction. In this work, the authors present methodology for the decomposition of multi-energy x-ray data in the image domain for the application of soft-tissue characterization. To demonstrate proof-of-principle, the authors apply several previously proposed methods and a novel content-aware method to multi-energy images acquired with a prototype photon counting CT system. Data from phantom and ex vivo specimens are evaluated.
Results: The number and type of materials in a region can be limited based on a priori knowledge or classification strategies. The proposed difference classifier successfully classified the image into air only, water+fat, water+fat+iodine, and water+calcium regions. Then, the content-aware material decomposition based on weighted least-square optimization generated quantitative maps of concentration. Bias in the estimation of the concentration of water and oil components in a phantom study was <0.10 ± 0.15 g/cc on average. Decomposition of ex vivo carotid endarterectomy specimens suggests the presence of water, lipid, and calcium deposits in the plaque walls.
Conclusions: Initial application of the proposed methodology suggests that it can decompose multi-energy CT images into quantitative maps of water, adipose, iodine, and calcium concentrations.
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87.57.Q- Computed tomography
02.60.Pn Numerical optimization
02.70.Rr General statistical methods
07.60.Dq Photometers, radiometers, and colorimeters
87.57.nf Reconstruction
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X-RAY COMPUTED TOMOGRAPHY: 2012 ADVANCES IN IMAGE FORMATION (Online only): Database-assisted low-dose CT image restoration

Wei Xu, Sungsoo Ha, and Klaus Mueller

Med. Phys. 40, 031109 (2013); http://dx.doi.org/10.1118/1.4790693 (7 pages)

Online Publication Date: 28 February 2013

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Purpose: Acquiring data for CT at low radiation doses has become a pressing goal. Unfortunately, the reduced data quality adversely affects the quality of the reconstructions, impeding their readability. In previous work, the authors showed how a prior regular-dose scan of the same patient can efficiently be used to mitigate low-dose artifacts. However, since a prior is not always available, the authors now extend the authors’ method to use a database of images of other patients.
Methods: The authors’ framework first matches the low-dose (target) scan with the images in the database and then selects a set of images that contain anatomical content similar to the target. These “priors” are then registered to the target and form the set of regular-dose priors for restoration via an extended nonlocal means (NLM) filtering framework. To accommodate the larger spatial variability of the patient scans, the authors subdivide the image area into blocks and perform the filtering locally. The database itself is first preprocessed to map each image from its 2D image space to a corresponding high-D image feature space. From this encoding a visual vocabulary is learned that assists in the query of the database.
Results: The authors demonstrate the authors’ framework via a lung scan example, for both streak artifacts (resulting from smaller projection sets) as well as noise artifacts (resulting from lower mA settings). The authors find that in the authors’ particular example case three priors were sufficient to restore all features faithfully. The authors also observe that the authors’ method is quite robust in that it generates good results even when the noise conditions significantly worsen (here by 20%). Finally, the authors find that the restoration quality is significantly better than with conventional NLM filtering.
Conclusions: The authors image restoration algorithm successfully restores images to high quality when the registration is well performed and also when the priors match the target well. When the priors do not contain sufficient information, the affected image regions can only be restored to the quality achieved with conventional regularization. Hence, a sufficiently rich database is a key for successful artifact mitigation with this approach. Finally, the blockwise scheme demonstrates the potential of using small patches of images to form the database.
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99.10.Cd Errata
87.55.dk Dose-volume analysis
87.57.cm Noise
87.57.cp Artifacts and distortion
87.57.nf Reconstruction
87.57.Q- Computed tomography
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X-RAY COMPUTED TOMOGRAPHY: 2012 ADVANCES IN IMAGE FORMATION (Online only): Creating optimal code for GPU-accelerated CT reconstruction using ant colony optimization

Eric Papenhausen, Ziyi Zheng, and Klaus Mueller

Med. Phys. 40, 031110 (2013); http://dx.doi.org/10.1118/1.4773045 (7 pages)

Online Publication Date: 28 February 2013

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Purpose: CT reconstruction algorithms implemented on the GPU are highly sensitive to their implementation details and the hardware they run on. Fine-tuning an implementation for optimal performance can be a time consuming task and require many updates when the hardware changes. There are some techniques that do automatic fine-tuning of GPU code. These techniques, however, are relatively narrow in their fine-tuning and are often based on heuristics which can be inaccurate. The goal of this paper is to present a framework that will automate the process of code optimization with maximum flexibility and produce a final result that is efficient and readable to the user.
Methods: The authors propose a method that is able to tune high level implementation details by using the ant colony optimization algorithm to find the optimal implementation in a relatively short amount of time. Our framework does this by taking as input, a file that describes a graph, such that a path through this graph represents a potential implementation. They then use the ant colony optimization algorithm to find the optimal path through this graph based on the execution time and the quality of the image.
Results: Two experimental studies are carried out. Using the presented framework, they optimize the performance of a GPU accelerated FDK backprojection implementation and a GPU accelerated separable footprint backprojection implementation. The authors demonstrate that the resulting optimal implementation can be different depending on the hardware specifications. They then compare the results of the framework produced with the results produced by manual optimization.
Conclusions: The framework they present is a useful tool for increasing programmer productivity and reducing the overhead of leveraging hardware specific resources. By performing an intelligent search, our framework produces a more efficient image reconstruction implementation in a shorter amount of time.
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87.57.Q- Computed tomography
02.60.Pn Numerical optimization
87.57.nf Reconstruction
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X-RAY COMPUTED TOMOGRAPHY: 2012 ADVANCES IN IMAGE FORMATION (Online only): Cone beam x-ray luminescence computed tomography: A feasibility study

Dongmei Chen, Shouping Zhu, Huangjian Yi, Xianghan Zhang, Duofang Chen, Jimin Liang, and Jie Tian

Med. Phys. 40, 031111 (2013); http://dx.doi.org/10.1118/1.4790694 (14 pages)

Online Publication Date: 28 February 2013

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Purpose: The appearance of x-ray luminescence computed tomography (XLCT) opens new possibilities to perform molecular imaging by x ray. In the previous XLCT system, the sample was irradiated by a sequence of narrow x-ray beams and the x-ray luminescence was measured by a highly sensitive charge coupled device (CCD) camera. This resulted in a relatively long sampling time and relatively low utilization of the x-ray beam. In this paper, a novel cone beam x-ray luminescence computed tomography strategy is proposed, which can fully utilize the x-ray dose and shorten the scanning time. The imaging model and reconstruction method are described. The validity of the imaging strategy has been studied in this paper.
Methods: In the cone beam XLCT system, the cone beam x ray was adopted to illuminate the sample and a highly sensitive CCD camera was utilized to acquire luminescent photons emitted from the sample. Photons scattering in biological tissues makes it an ill-posed problem to reconstruct the 3D distribution of the x-ray luminescent sample in the cone beam XLCT. In order to overcome this issue, the authors used the diffusion approximation model to describe the photon propagation in tissues, and employed the sparse regularization method for reconstruction. An incomplete variables truncated conjugate gradient method and permissible region strategy were used for reconstruction. Meanwhile, traditional x-ray CT imaging could also be performed in this system. The x-ray attenuation effect has been considered in their imaging model, which is helpful in improving the reconstruction accuracy.
Results: First, simulation experiments with cylinder phantoms were carried out to illustrate the validity of the proposed compensated method. The experimental results showed that the location error of the compensated algorithm was smaller than that of the uncompensated method. The permissible region strategy was applied and reduced the reconstruction error to less than 2 mm. The robustness and stability were then evaluated from different view numbers, different regularization parameters, different measurement noise levels, and optical parameters mismatch. The reconstruction results showed that the settings had a small effect on the reconstruction. The nonhomogeneous phantom simulation was also carried out to simulate a more complex experimental situation and evaluated their proposed method. Second, the physical cylinder phantom experiments further showed similar results in their prototype XLCT system. With the discussion of the above experiments, it was shown that the proposed method is feasible to the general case and actual experiments.
Conclusions: Utilizing numerical simulation and physical experiments, the authors demonstrated the validity of the new cone beam XLCT method. Furthermore, compared with the previous narrow beam XLCT, the cone beam XLCT could more fully utilize the x-ray dose and the scanning time would be shortened greatly. The study of both simulation experiments and physical phantom experiments indicated that the proposed method was feasible to the general case and actual experiments.
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87.57.Q- Computed tomography
87.85.Pq Biomedical imaging
02.60.-x Numerical approximation and analysis
87.59.-e X-ray imaging
87.57.nf Reconstruction
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X-RAY COMPUTED TOMOGRAPHY: 2012 ADVANCES IN IMAGE FORMATION (Online only): Temporal resolution and motion artifacts in single-source and dual-source cardiac CT

Harald Schöndube, Thomas Allmendinger, Karl Stierstorfer, Herbert Bruder, and Thomas Flohr

Med. Phys. 40, 031112 (2013); http://dx.doi.org/10.1118/1.4790695 (10 pages)

Online Publication Date: 28 February 2013

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Purpose: The temporal resolution of a given image in cardiac computed tomography (CT) has so far mostly been determined from the amount of CT data employed for the reconstruction of that image. The purpose of this paper is to examine the applicability of such measures to the newly introduced modality of dual-source CT as well as to methods aiming to provide improved temporal resolution by means of an advanced image reconstruction algorithm.
Methods: To provide a solid base for the examinations described in this paper, an extensive review of temporal resolution in conventional single-source CT is given first. Two different measures for assessing temporal resolution with respect to the amount of data involved are introduced, namely, either taking the full width at half maximum of the respective data weighting function (FWHM-TR) or the total width of the weighting function (total TR) as a base of the assessment. Image reconstruction using both a direct fan-beam filtered backprojection with Parker weighting as well as using a parallel-beam rebinning step are considered. The theory of assessing temporal resolution by means of the data involved is then extended to dual-source CT. Finally, three different advanced iterative reconstruction methods that all use the same input data are compared with respect to the resulting motion artifact level. For brevity and simplicity, the examinations are limited to two-dimensional data acquisition and reconstruction. However, all results and conclusions presented in this paper are also directly applicable to both circular and helical cone-beam CT.
Results: While the concept of total TR can directly be applied to dual-source CT, the definition of the FWHM of a weighting function needs to be slightly extended to be applicable to this modality. The three different advanced iterative reconstruction methods examined in this paper result in significantly different images with respect to their motion artifact level, despite exactly the same amount of data being used in the reconstruction process.
Conclusions: The concept of assessing temporal resolution by means of the data employed for reconstruction can nicely be extended from single-source to dual-source CT. However, for advanced (possibly nonlinear iterative) reconstruction algorithms the examined approach fails to deliver accurate results. New methods and measures to assess the temporal resolution of CT images need to be developed to be able to accurately compare the performance of such algorithms.
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99.10.Cd Errata
87.57.cf Spatial resolution
87.57.nf Reconstruction
87.57.Q- Computed tomography
87.59.bd Computed radiography
87.19.Hh Cardiac dynamics
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X-RAY COMPUTED TOMOGRAPHY: 2012 ADVANCES IN IMAGE FORMATION (Online only): A filtered backprojection algorithm with ray-by-ray noise weighting

Gengsheng L. Zeng and Alex Zamyatin

Med. Phys. 40, 031113 (2013); http://dx.doi.org/10.1118/1.4790696 (7 pages) | Cited 1 time

Online Publication Date: 28 February 2013

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Purpose: This paper derives a ray-by-ray weighted filtered backprojection (rFBP) algorithm, based on our recently developed view-by-view weighted, filtered backprojection (vFBP) algorithm.
Methods: The rFBP algorithm directly extends the vFBP algorithm by letting the noise weighting vary from channel to channel within each view. The projection data can be weighted in inverse proportion to their noise variances. Also, an edge-preserving bilateral filter is suggested to perform post filtering to further reduce the noise. The proposed algorithm has been implemented for the circular-orbit cone-beam geometry based on Feldkamp's algorithm.
Results: Image reconstructions with computer simulations and clinical cadaver data are presented to illustrate the effectiveness and feasibility of the proposed algorithm. The new FBP-type algorithm is able to significantly reduce or remove the noise texture, which the conventional FBP is unable to do. The computation time of the proposed rFBP algorithm is approximately the same as the conventional FBP algorithm.
Conclusions: A ray-based noise-weighting scheme is introduced to the FBP algorithm. This new FBP-type algorithm significantly reduces or removes the streaking artifacts in low-dose CT.
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87.57.Q- Computed tomography
87.57.cm Noise
87.57.cp Artifacts and distortion
87.57.nf Reconstruction
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X-RAY COMPUTED TOMOGRAPHY: 2012 ADVANCES IN IMAGE FORMATION (Online only): Micro-CT artifacts reduction based on detector random shifting and fast data inpainting

Yining Zhu, Mengliu Zhao, Hongwei Li, and Peng Zhang

Med. Phys. 40, 031114 (2013); http://dx.doi.org/10.1118/1.4790697 (14 pages)

Online Publication Date: 28 February 2013

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Purpose: In Micro-CT systems based on optical coupling detectors, the defects of scintillator or CCD-camera would lead to heavy artifacts in reconstructed CT images. Meanwhile, different detector units usually suffer from inhomogeneous response, which also leads to artifacts in the CT images. Detector shifting is a simple and efficient method to remove the artifacts due to inhomogeneous responses of detector units. However, it does not work well for heavy artifacts due to defects in scintillator or CCD. The purpose of this paper is to develop a data preprocessing method to reduce both kinds of artifacts.
Methods: A hybrid method which involves detector random shifting and data inpainting is proposed to correct the projection data, so as to suppress the artifacts in the reconstructed CT images. The defects in scintillator or CCD-camera lead to data lost in some areas of the projection data. The Criminisi algorithm is employed to recover the lost data. By detector random shifting, the location of the lost data in one view might be shifted away in adjacent views. This feature is utilized to design the search window, such that the best match patch shall be searched across adjacent views. By this way, the best match patches should really enjoy high similarity. As a result, the heavy artifacts due to defects of scintillator or CCD-camera should be suppressed. Furthermore, a multiscale tessellation method is proposed to locate the defects and similarity patches, which makes the Criminisi algorithm very fast.
Results: The authors tested the proposed method on both simulated projection data and real projection data. Experiments show that the proposed method could correct the bad data in the projections quite well. Compared to other popular methods, such as linear interpolation, wavelet combining Fourier transform, and TV-inpainting, experimental results suggest that the CT images reconstructed from the preprocessed data sets by our method is significantly better in quality.
Conclusions: They have proposed a hybrid method for projection data preprocessing which fits well to typical Micro-CT systems. The hybrid method could suppress the ring artifacts in the reconstructed CT images efficiently, while the spatial resolution is not reduced even with a critical eye.
Show PACS
99.10.Cd Errata
87.57.cf Spatial resolution
87.57.cp Artifacts and distortion
87.57.nf Reconstruction
87.57.Q- Computed tomography
02.30.Uu Integral transforms
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X-RAY COMPUTED TOMOGRAPHY: 2012 ADVANCES IN IMAGE FORMATION (Online only): First-order convex feasibility algorithms for x-ray CT

Emil Y. Sidky, Jakob S. Jørgensen, and Xiaochuan Pan

Med. Phys. 40, 031115 (2013); http://dx.doi.org/10.1118/1.4790698 (15 pages)

Online Publication Date: 28 February 2013

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Purpose: Iterative image reconstruction (IIR) algorithms in computed tomography (CT) are based on algorithms for solving a particular optimization problem. Design of the IIR algorithm, therefore, is aided by knowledge of the solution to the optimization problem on which it is based. Often times, however, it is impractical to achieve accurate solution to the optimization of interest, which complicates design of IIR algorithms. This issue is particularly acute for CT with a limited angular-range scan, which leads to poorly conditioned system matrices and difficult to solve optimization problems. In this paper, we develop IIR algorithms which solve a certain type of optimization called convex feasibility. The convex feasibility approach can provide alternatives to unconstrained optimization approaches and at the same time allow for rapidly convergent algorithms for their solution—thereby facilitating the IIR algorithm design process.
Methods: An accelerated version of the Chambolle−Pock (CP) algorithm is adapted to various convex feasibility problems of potential interest to IIR in CT. One of the proposed problems is seen to be equivalent to least-squares minimization, and two other problems provide alternatives to penalized, least-squares minimization.
Results: The accelerated CP algorithms are demonstrated on a simulation of circular fan-beam CT with a limited scanning arc of 144°. The CP algorithms are seen in the empirical results to converge to the solution of their respective convex feasibility problems.
Conclusions: Formulation of convex feasibility problems can provide a useful alternative to unconstrained optimization when designing IIR algorithms for CT. The approach is amenable to recent methods for accelerating first-order algorithms which may be particularly useful for CT with limited angular-range scanning. The present paper demonstrates the methodology, and future work will illustrate its utility in actual CT application.
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87.57.Q- Computed tomography
87.59.B- Radiography
02.60.-x Numerical approximation and analysis
02.60.Pn Numerical optimization

RADIATION THERAPY PHYSICS: An accuracy assessment of different rigid body image registration methods and robotic couch positional corrections using a novel phantom

Sankar Arumugam, Michael G. Jameson, Aitang Xing, and Lois Holloway

Med. Phys. 40, 031701 (2013); http://dx.doi.org/10.1118/1.4789490 (9 pages)

Online Publication Date: 8 February 2013

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Purpose: Image guided radiotherapy (IGRT) using cone beam computed tomography (CBCT) images greatly reduces interfractional patient positional uncertainties. An understanding of uncertainties in the IGRT process itself is essential to ensure appropriate use of this technology. The purpose of this study was to develop a phantom capable of assessing the accuracy of IGRT hardware and software including a 6 degrees of freedom patient positioning system and to investigate the accuracy of the Elekta XVI system in combination with the HexaPOD robotic treatment couch top.
Methods: The constructed phantom enabled verification of the three automatic rigid body registrations (gray value, bone, seed) available in the Elekta XVI software and includes an adjustable mount that introduces known rotational offsets to the phantom from its reference position. Repeated positioning of the phantom was undertaken to assess phantom rotational accuracy. Using this phantom the accuracy of the XVI registration algorithms was assessed considering CBCT hardware factors and image resolution together with the residual error in the overall image guidance process when positional corrections were performed through the HexaPOD couch system.
Results: The phantom positioning was found to be within 0.04 (σ = 0.12)°, 0.02 (σ = 0.13)°, and −0.03 (σ = 0.06)° in X, Y, and Z directions, respectively, enabling assessment of IGRT with a 6 degrees of freedom patient positioning system. The gray value registration algorithm showed the least error in calculated offsets with maximum mean difference of −0.2(σ = 0.4) mm in translational and −0.1(σ = 0.1)° in rotational directions for all image resolutions. Bone and seed registration were found to be sensitive to CBCT image resolution. Seed registration was found to be most sensitive demonstrating a maximum mean error of −0.3(σ = 0.9) mm and −1.4(σ = 1.7)° in translational and rotational directions over low resolution images, and this is reduced to −0.1(σ = 0.2) mm and −0.1(σ = 0.79)° using high resolution images.
Conclusions: The phantom, capable of rotating independently about three orthogonal axes was successfully used to assess the accuracy of an IGRT system considering 6 degrees of freedom. The overall residual error in the image guidance process of XVI in combination with the HexaPOD couch was demonstrated to be less than 0.3 mm and 0.3° in translational and rotational directions when using the gray value registration with high resolution CBCT images. However, the residual error, especially in rotational directions, may increase when the seed registration is used with low resolution images.
Show PACS
87.57.nj Registration
87.57.Q- Computed tomography
87.85.St Robotics
87.53.Jw Therapeutic applications, including brachytherapy
87.57.cf Spatial resolution
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