Comparison of Analytic and Algebraic Methods for Motion-Compensated Cone-Beam CT Reconstruction of the Thorax

Rit, Simon; Sarrut, David; Desbat, Laurent

doi:10.1109/tmi.2008.2008962

Cited by 66 publications

(58 citation statements)

References 58 publications

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“…Based on the difference between them, the initial volume is updated by backward-projection as many times as necessary. 13 Although iterative methods provide a higher signal-to-noise ratio (SNR) compared to the analytical reconstruction algorithms, 19,20 they are sometimes impractical due to high computational costs with relatively longer reconstruction times. Several studies have proposed either internal or external fiducial markers to track subjects' motion.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, it is very hard to define a realistic knee motion for manipulating a virtual phantom. Manufacturing mechanical motion-controlled phantoms using motors 20 also challenging due to the aperiodicity of knee motion. In this study, we used the acquired optical motion tracking data of nine subjects holding a squat as an accurate representation of the human knee kinematics and successfully implemented the complex motion into the 4D extended cardiactorso (XCAT) model.…”

Section: Introductionmentioning

confidence: 99%

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Fiducial marker‐based correction for involuntary motion in weight‐bearing C‐arm CT scanning of knees. Part I. Numerical model‐based optimization

et al. 2013

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Purpose: Human subjects in standing positions are apt to show much more involuntary motion than in supine positions. The authors aimed to simulate a complicated realistic lower body movement using the four-dimensional (4D) digital extended cardiac-torso (XCAT) phantom. The authors also investigated fiducial marker-based motion compensation methods in two-dimensional (2D) and threedimensional (3D) space. The level of involuntary movement-induced artifacts and image quality improvement were investigated after applying each method. Methods: An optical tracking system with eight cameras and seven retroreflective markers enabled us to track involuntary motion of the lower body of nine healthy subjects holding a squat position at 60• of flexion. The XCAT-based knee model was developed using the 4D XCAT phantom and the optical tracking data acquired at 120 Hz. The authors divided the lower body in the XCAT into six parts and applied unique affine transforms to each so that the motion (6 degrees of freedom) could be synchronized with the optical markers' location at each time frame. The control points of the XCAT were tessellated into triangles and 248 projection images were created based on intersections of each ray and monochromatic absorption. The tracking data sets with the largest motion (Subject 2) and the smallest motion (Subject 5) among the nine data sets were used to animate the XCAT knee model. The authors defined eight skin control points well distributed around the knees as pseudofiducial markers which functioned as a reference in motion correction. Motion compensation was done in the following ways: (1) simple projection shifting in 2D, (2) deformable projection warping in 2D, and (3) rigid body warping in 3D. Graphics hardware accelerated filtered backprojection was implemented and combined with the three correction methods in order to speed up the simulation process. Correction fidelity was evaluated as a function of number of markers used (4-12) and marker distribution in three scenarios. Results: Average optical-based translational motion for the nine subjects was 2.14 mm (±0.69 mm) and 2.29 mm (±0.63 mm) for the right and left knee, respectively. In the representative central slices of Subject 2, the authors observed 20.30%, 18.30%, and 22.02% improvements in the structural similarity (SSIM) index with 2D shifting, 2D warping, and 3D warping, respectively. The performance of 2D warping improved as the number of markers increased up to 12 while 2D shifting and 3D warping were insensitive to the number of markers used. The minimum required number of markers for 2D shifting, 2D warping, and 3D warping was 4-6, 12, and 8, respectively. An even distribution of markers over the entire field of view provided robust performance for all three correction methods. Conclusions:The authors were able to simulate subject-specific realistic knee movement in weightbearing positions. This study indicates that involuntary motion can seriously degrade the image quality. The proposed three methods were evaluated with the...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fiducial marker‐based correction for involuntary motion in weight‐bearing C‐arm CT scanning of knees. Part I. Numerical model‐based optimization

et al. 2013

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“…Such a low resolution results in a low image-quality. 6 The low-resolution 4D-CT imagery suffers from false apparent vessel discontinuities, shape distortions, etc., which introduce errors in the correct localization and shape assessment of tumor and vessel structures. Thus, the reduction in resolution compromises the full potential of 4D-CT for providing accurate motion and localization, and the problem of enhancing resolution of 4D-CT is an important one and needs to be addressed.…”

Section: Introductionmentioning

confidence: 99%

Resolution enhancement of lung 4D‐CT via group‐sparsity

Bhavsar

Lian

et al. 2013

Medical Physics

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Purpose: 4D-CT typically delivers more accurate information about anatomical structures in the lung, over 3D-CT, due to its ability to capture visual information of the lung motion across different respiratory phases. This helps to better determine the dose during radiation therapy for lung cancer. However, a critical concern with 4D-CT that substantially compromises this advantage is the low superior-inferior resolution due to less number of acquired slices, in order to control the CT radiation dose. To address this limitation, the authors propose an approach to reconstruct missing intermediate slices, so as to improve the superior-inferior resolution. Methods: In this method the authors exploit the observation that sampling information across respiratory phases in 4D-CT can be complimentary due to lung motion. The authors' approach uses this locally complimentary information across phases in a patch-based sparse-representation framework. Moreover, unlike some recent approaches that treat local patches independently, the authors' approach employs the group-sparsity framework that imposes neighborhood and similarity constraints between patches. This helps in mitigating the trade-off between noise robustness and structure preservation, which is an important consideration in resolution enhancement. The authors discuss the regularizing ability of group-sparsity, which helps in reducing the effect of noise and enables better structural localization and enhancement. Results: The authors perform extensive experiments on the publicly available DIR-Lab Lung 4D-CT dataset [R. Castillo, E. Castillo, R. Guerra, V. Johnson, T. McPhail, A. Garg, and T. Guerrero, "A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets," Phys. Med. Biol. 54, 1849-1870 (2009)]. First, the authors carry out empirical parametric analysis of some important parameters in their approach. The authors then demonstrate, qualitatively as well as quantitatively, the ability of their approach to achieve more accurate and better localized results over bicubic interpolation as well as a related state-of-the-art approach. The authors also show results on some datasets with tumor, to further emphasize the clinical importance of their method. Conclusions:The authors have proposed to improve the superior-inferior resolution of 4D-CT by estimating intermediate slices. The authors' approach exploits neighboring constraints in the group-sparsity framework, toward the goal of achieving better localization and noise robustness. The authors' results are encouraging, and positively demonstrate the role of group-sparsity for 4D-CT resolution enhancement.

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“…3,6,7 Thus, by using the estimated motion envelope to account for breathing motion, instead of simply expanding based on experience, the size of ITV can be greatly reduced. Other applications of 4D-CT deformable registration include ventilation imaging, 8 motioncompensated cone-beam CT reconstruction, 9 and respiratory motion modeling. 10 With more application of 4D-CT, the 4D-CT deformable registration methods are expected to become more useful in the image-guided radiation therapy.…”

Section: Ia Motivationmentioning

confidence: 99%

Estimating the 4D respiratory lung motion by spatiotemporal registration and super‐resolution image reconstruction

Wang

Lian

et al. 2013

Medical Physics

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Purpose:One of the main challenges in lung cancer radiation therapy is how to reduce the treatment margin but accommodate the geometric uncertainty of moving tumor. 4D-CT is able to provide the full range of motion information for the lung and tumor. However, accurate estimation of lung motion with respect to the respiratory phase is difficult due to various challenges in image registration, e.g., motion artifacts and large interslice thickness in 4D-CT. Meanwhile, the temporal coherence across respiration phases is usually not guaranteed in the conventional registration methods which consider each phase image in 4D-CT independently. To address these challenges, the authors present a unified approach to estimate the respiratory lung motion with two iterative steps. Methods: First, the authors propose a novel spatiotemporal registration algorithm to align all phase images of 4D-CT (in low-resolution) to a high-resolution group-mean image in the common space. The temporal coherence of registration is maintained by a set of temporal fibers that delineate temporal correspondences across different respiratory phases. Second, a super-resolution technique is utilized to build the high-resolution group-mean image with more anatomical details than any individual phase image, thus largely alleviating the registration uncertainty especially in correspondence detection. In particular, the authors use the concept of sparse representation to keep the group-mean image as sharp as possible. Results: The performance of our 4D motion estimation method has been extensively evaluated on both the simulated datasets and real lung 4D-CT datasets. In all experiments, our method achieves more accurate and consistent results in lung motion estimation than all other state-of-the-art approaches under comparison. Conclusions:The authors have proposed a novel spatiotemporal registration method to estimate the lung motion in 4D-CT. Promising results have been obtained, which indicates the high applicability of our method in clinical lung cancer radiation therapy.

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Comparison of Analytic and Algebraic Methods for Motion-Compensated Cone-Beam CT Reconstruction of the Thorax

Cited by 66 publications

References 58 publications

Fiducial marker‐based correction for involuntary motion in weight‐bearing C‐arm CT scanning of knees. Part I. Numerical model‐based optimization

Fiducial marker‐based correction for involuntary motion in weight‐bearing C‐arm CT scanning of knees. Part I. Numerical model‐based optimization

Resolution enhancement of lung 4D‐CT via group‐sparsity

Estimating the 4D respiratory lung motion by spatiotemporal registration and super‐resolution image reconstruction

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