Directional View Interpolation for Compensation of Sparse Angular Sampling in Cone-Beam CT

Bertram, Matthias; Wiegert, Jens; Schäfer, Dirk; Aach, Til; Rose, Georg

doi:10.1109/tmi.2008.2011550

Cited by 55 publications

(44 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various methods have been proposed to address the under-sampling problem and improve image quality in 4DCBCT, such as compressed sensing (Chen et al 2008; Sidky and Pan 2008; Sidky et al 2011; Gao et al 2012; Choi et al 2013), motion compensated reconstruction (Li et al 2006; Li et al 2007; Rit et al 2008; Rit et al 2009), intermediate frame interpolation (Weiss et al 1982; Lehmann et al 2001; Schreibmann et al 2006; Bertram et al 2009; Dhou et al 2014), and other recent advanced methods (Jia et al 2012; Wang and Gu 2013). …”

Section: Discussionmentioning

confidence: 99%

3D fluoroscopic image estimation using patient-specific 4DCBCT-based motion models

et al. 2015

View full text Add to dashboard Cite

3D fluoroscopic images represent volumetric patient anatomy during treatment with high spatial and temporal resolution. 3D fluoroscopic images estimated using motion models built using 4DCT images, taken days or weeks prior to treatment, do not reliably represent patient anatomy during treatment. In this study we develop and perform initial evaluation of techniques to develop patient-specific motion models from 4D cone-beam CT (4DCBCT) images, taken immediately before treatment, and use these models to estimate 3D fluoroscopic images based on 2D kV projections captured during treatment. We evaluate the accuracy of 3D fluoroscopic images by comparing to ground truth digital and physical phantom images. The performance of 4DCBCT- and 4DCT- based motion models are compared in simulated clinical situations representing tumor baseline shift or initial patient positioning errors. The results of this study demonstrate the ability for 4DCBCT imaging to generate motion models that can account for changes that cannot be accounted for with 4DCT-based motion models. When simulating tumor baseline shift and patient positioning errors of up to 5 mm, the average tumor localization error and the 95th percentile error in six datasets were 1.20 and 2.2 mm, respectively, for 4DCBCT-based motion models. 4DCT-based motion models applied to the same six datasets resulted in average tumor localization error and the 95th percentile error of 4.18 and 5.4 mm, respectively. Analysis of voxel-wise intensity differences was also conducted for all experiments. In summary, this study demonstrates the feasibility of 4DCBCT-based 3D fluoroscopic image generation in digital and physical phantoms, and shows the potential advantage of 4DCBCT-based 3D fluoroscopic image estimation when there are changes in anatomy between the time of 4DCT imaging and the time of treatment delivery.

show abstract

Section: Discussionmentioning

confidence: 99%

3D fluoroscopic image estimation using patient-specific 4DCBCT-based motion models

et al. 2015

View full text Add to dashboard Cite

show abstract

“…We applied the Jacobi rotation method to calculate the eigenvalues of the symmetric block matrix T and identify the eigenvector associated with the smallest eigenvalue [11,27]. The least-square method is used to find the interpolated S-curve with the closest direction to the vector e S ðβ; γÞ !…”

Section: B the Methodsmentioning

confidence: 99%

“…They are generally classified as inpainting-like and statistical methods. Typical inpainting-like techniques include total variation (TV) inpainting [7,8], partial differential equation (PDE)-based inpainting [9,10], and directional interpolation [11]. In some of these methods such as in [12][13][14], the inherent property of sinusoid-like curve (S-curve) composition is exploited to restore the incomplete sinograms.…”

Section: Introductionmentioning

confidence: 99%

Strategy of computed tomography sinogram inpainting based on sinusoid-like curve decomposition and eigenvector-guided interpolation

Chen

et al. 2011

J. Opt. Soc. Am. A

View full text Add to dashboard Cite

Projection incompleteness in x-ray computed tomography (CT) often relates to sparse sampling or detector gaps and leads to degraded reconstructions with severe streak and ring artifacts. To suppress these artifacts, this study develops a new sinogram inpainting strategy based on sinusoid-like curve decomposition and eigenvector-guided interpolation, where each missing sinogram point is considered located within a group of sinusoid-like curves and estimated from eigenvector-guided interpolation to preserve the sinogram texture continuity. The proposed approach is evaluated on real two-dimensional fan-beam CT data, for which the projection incompleteness, due to sparse sampling and symmetric detector gaps, is simulated. A Compute Unified Device Architecture (CUDA)-based parallelization is applied on the operations of sinusoid fittings and interpolations to accelerate the algorithm. A comparative study is then conducted to evaluate the proposed approach with two other inpainting methods and with a compressed sensing iterative reconstruction. Qualitative and quantitative performances demonstrate that the proposed approach can lead to efficient artifact suppression and less structure blurring.

show abstract

“…The second class keeps the acquisition methods the same as the standard 4D acquisition, and either the data is modified in pre-processing prior to reconstruction or the reconstruction model itself is altered. This class includes a variety of methods including data-driven artifact suppression [59–62], use of a motion model for image reconstruction [63–82], and total-variation based iterative reconstruction algorithms [59, 83–89]. …”

Section: Emerging Strategies For Dealing With Residual 4d Artifactsmentioning

confidence: 99%

Advances in 4D radiation therapy for managing respiration: Part I – 4D imaging

Hugo

Roşu

2012

Zeitschrift für Medizinische Physik

View full text Add to dashboard Cite

Techniques for managing respiration during imaging and planning of radiation therapy are reviewed, concentrating on free-breathing (4D) approaches. First, we focus on detailing the historical development and basic operational principles of currently-available “first generation” 4D imaging modalities: 4D computed tomography, 4D cone beam computed tomography, 4D magnetic resonance imaging, and 4D positron emission tomography. Features and limitations of these first generation systems are described, including necessity of breathing surrogates for 4D image reconstruction, assumptions made in acquisition and reconstruction about the breathing pattern, and commonly-observed artifacts. Both established and developmental methods to deal with these limitations are detailed. Finally, strategies to construct 4D targets and images and, alternatively, to compress 4D information into static targets and images for radiation therapy planning are described.

show abstract

Directional View Interpolation for Compensation of Sparse Angular Sampling in Cone-Beam CT

Cited by 55 publications

References 22 publications

3D fluoroscopic image estimation using patient-specific 4DCBCT-based motion models

3D fluoroscopic image estimation using patient-specific 4DCBCT-based motion models

Strategy of computed tomography sinogram inpainting based on sinusoid-like curve decomposition and eigenvector-guided interpolation

Advances in 4D radiation therapy for managing respiration: Part I – 4D imaging

Contact Info

Product

Resources

About