Simplified Statistical Image Reconstruction Algorithm for Polyenergetic X-Ray CT

Srivastava, Sudhanshu; Fessler, Jeffrey A.

doi:10.1109/nssmic.2005.1596614

Cited by 9 publications

(9 citation statements)

References 44 publications

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“…The mismatch between this monoenergetic algorithm and polyenergetic data will have an effect on convergence and residual artifacts. While these investigations are beyond the scope of this paper, an investigation of this mismatch and an incorporation of a polyenergetic forward model [41–44] is the subject of ongoing work, with initial results found in [45]. Such modifications will likely be important to application in real clinical data since beam hardening is a major contributor to metal artifacts.…”

Section: Discussionmentioning

confidence: 99%

Model-Based Tomographic Reconstruction of Objects Containing Known Components

Stayman

Otake

Prince

et al. 2012

IEEE Trans. Med. Imaging

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The likelihood of finding manufactured components (surgical tools, implants, etc.) within a tomographic field-of-view has been steadily increasing. One reason is the aging population and proliferation of prosthetic devices, such that more people undergoing diagnostic imaging have existing implants, particularly hip and knee implants. Another reason is that use of intraoperative imaging (e.g., cone-beam CT) for surgical guidance is increasing, wherein surgical tools and devices such as screws and plates are placed within or near to the target anatomy. When these components contain metal, the reconstructed volumes are likely to contain severe artifacts that adversely affect the image quality in tissues both near and far from the component. Because physical models of such components exist, there is a unique opportunity to integrate this knowledge into the reconstruction algorithm to reduce these artifacts. We present a model-based penalized-likelihood estimation approach that explicitly incorporates known information about component geometry and composition. The approach uses an alternating maximization method that jointly estimates the anatomy and the position and pose of each of the known components. We demonstrate that the proposed method can produce nearly artifact-free images even near the boundary of a metal implant in simulated vertebral pedicle screw reconstructions and even under conditions of substantial photon starvation. The simultaneous estimation of device pose also provides quantitative information on device placement that could be valuable to quality assurance and verification of treatment delivery.

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Section: Discussionmentioning

confidence: 99%

Model-Based Tomographic Reconstruction of Objects Containing Known Components

Stayman

Otake

Prince

et al. 2012

IEEE Trans. Med. Imaging

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“…In single-energy scanning a common approach to reduce the number of unknowns is to assume that each pixel is either bone or soft tissue, i.e., f k j is a binary function that is equal to 1 for pixels of tissue type k and 0 for the rest. Usually the values j k f are determined by segmenting a preliminary reconstruction [14,16]. Here we consider an alternative approach [15] that avoids segmentation by modeling the tissue fraction value in the pixel as a function of the estimated density in that pixel.…”

Section: Segmentation Free Implementationmentioning

confidence: 99%

“…Both approaches needed tabulated measurements of the line integrals of bone and water over a reasonable range of object thicknesses for the CT system of interest. To avoid modeling the entire X-ray spectrum, Srivastava and Fessler [16] proposed a simplified statistical image reconstruction approach for polyenergetic X-ray CT using the using the same calibration data and tuning parameters as Joseph and Spital. However, that method uses approximations that can lead to nonphysical negative values that can cause convergence problems.…”

Section: Introductionmentioning

confidence: 99%

A new statistical image reconstruction algorithm for polyenergetic X-ray CT

Abella

Fessler

2009

2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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This paper presents a new statistical reconstruction algorithm for X-ray CT. The algorithm is based on Poisson statistics and a physical model that accounts for the measurement nonlinearities caused by energy-dependent attenuation. We model each voxel's attenuation as a mixture of bone and soft tissue by defining density-dependent tissue fractions, maintaining one unknown per voxel avoiding the need of a pre-segmentation. Rather than requiring the entire X-ray spectrum, the method approximates the 2D beam hardening function corresponding to bone and soft tissue with the 1D function corresponding to water and one or two empirical tuning parameters.Results on simulated human data (NCAT phantom) showed a beam hardening reduction similar to conventional post-processing techniques, but with an improved signal to noise ratio.

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“…The method did not require a preliminary bone segmentation but it still needed knowledge of the polyenergetic source spectrum. This was substituted by Srivastava and Fessler in [19] with the same calibration data and tuning parameters as Joseph and Spital. However, the proposed model makes an approximation that can lead to nonphysical negative values prone to cause convergence problems.…”

Section: Introductionmentioning

confidence: 99%