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Analysis of iterative region-of-interest image reconstruction for x-ray computed tomography.

Sidky EY, Kraemer DN, Roth EG, Ullberg C, Reiser IS, Pan X - J Med Imaging (Bellingham) (2014)

Bottom Line: In order to characterize this optimization problem, we apply it to computer-simulated two-dimensional fan-beam CT data, using both ideal noiseless data and realistic data containing a level of noise comparable to that of the breast CT application.The proposed method is demonstrated for both complete field-of-view and ROI imaging.To demonstrate the potential utility of the proposed ROI imaging method, it is applied to actual CT scanner data.

View Article: PubMed Central - PubMed

Affiliation: University of Chicago, Department of Radiology, 5841 South Maryland Avenue, Chicago, Illinois 60637, United States.

ABSTRACT

One of the challenges for iterative image reconstruction (IIR) is that such algorithms solve an imaging model implicitly, requiring a complete representation of the scanned subject within the viewing domain of the scanner. This requirement can place a prohibitively high computational burden for IIR applied to x-ray computed tomography (CT), especially when high-resolution tomographic volumes are required. In this work, we aim to develop an IIR algorithm for direct region-of-interest (ROI) image reconstruction. The proposed class of IIR algorithms is based on an optimization problem that incorporates a data fidelity term, which compares a derivative of the estimated data with the available projection data. In order to characterize this optimization problem, we apply it to computer-simulated two-dimensional fan-beam CT data, using both ideal noiseless data and realistic data containing a level of noise comparable to that of the breast CT application. The proposed method is demonstrated for both complete field-of-view and ROI imaging. To demonstrate the potential utility of the proposed ROI imaging method, it is applied to actual CT scanner data.

No MeSH data available.


Breast phantom for computed tomography (CT) and its corresponding gradient magnitude image (GMI). (a) The linear attenuation map of the phantom in the gray scale window . The attenuation values for the tissues, fat, and fibro-glandular, simulated in the phantom are 0.194 and , respectively. These values model a monochromatic x-ray source at 50 keV. (b) The GMI  illustrating the test phantom’s sparsity in the GMI.
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f2: Breast phantom for computed tomography (CT) and its corresponding gradient magnitude image (GMI). (a) The linear attenuation map of the phantom in the gray scale window . The attenuation values for the tissues, fat, and fibro-glandular, simulated in the phantom are 0.194 and , respectively. These values model a monochromatic x-ray source at 50 keV. (b) The GMI illustrating the test phantom’s sparsity in the GMI.

Mentions: The simulated 2-D CT data are based on the breast CT imaging application. The computer generated phantom shown in Fig. 2 models fat and fibro-glandular tissues. The complex structure is obtained by generating an image with power-law filtered white noise, followed by thresholding. The structure model is described in Ref. 13. The scanning geometry is 2-D fan-beam CT with full circular coverage, as what would be available in the midplane of an actual breast CT scan. The source-to-isocenter distance is 36 cm, and the source-to-detector distance is 72 cm. A linear detector with 1024 bins is modeled, and for the majority of the simulations, 256 projection views are taken. The exceptions are 1024 views are used for the studies without TV regularization, and 64 views are employed in one study examining the possibility of sparse-view sampling. In this initial series of characterization studies, we examine image reconstruction from ideal noiseless data, where the projections are generated from a phantom defined on the same grid used for the image reconstruction. We also perform studies for inconsistent CT data with noise modeling a typical breast CT scan, and projection operating on mismatched grids. The phantom projections are generated from a grid and the image estimate projections are generated from a grid.


Analysis of iterative region-of-interest image reconstruction for x-ray computed tomography.

Sidky EY, Kraemer DN, Roth EG, Ullberg C, Reiser IS, Pan X - J Med Imaging (Bellingham) (2014)

Breast phantom for computed tomography (CT) and its corresponding gradient magnitude image (GMI). (a) The linear attenuation map of the phantom in the gray scale window . The attenuation values for the tissues, fat, and fibro-glandular, simulated in the phantom are 0.194 and , respectively. These values model a monochromatic x-ray source at 50 keV. (b) The GMI  illustrating the test phantom’s sparsity in the GMI.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4326078&req=5

f2: Breast phantom for computed tomography (CT) and its corresponding gradient magnitude image (GMI). (a) The linear attenuation map of the phantom in the gray scale window . The attenuation values for the tissues, fat, and fibro-glandular, simulated in the phantom are 0.194 and , respectively. These values model a monochromatic x-ray source at 50 keV. (b) The GMI illustrating the test phantom’s sparsity in the GMI.
Mentions: The simulated 2-D CT data are based on the breast CT imaging application. The computer generated phantom shown in Fig. 2 models fat and fibro-glandular tissues. The complex structure is obtained by generating an image with power-law filtered white noise, followed by thresholding. The structure model is described in Ref. 13. The scanning geometry is 2-D fan-beam CT with full circular coverage, as what would be available in the midplane of an actual breast CT scan. The source-to-isocenter distance is 36 cm, and the source-to-detector distance is 72 cm. A linear detector with 1024 bins is modeled, and for the majority of the simulations, 256 projection views are taken. The exceptions are 1024 views are used for the studies without TV regularization, and 64 views are employed in one study examining the possibility of sparse-view sampling. In this initial series of characterization studies, we examine image reconstruction from ideal noiseless data, where the projections are generated from a phantom defined on the same grid used for the image reconstruction. We also perform studies for inconsistent CT data with noise modeling a typical breast CT scan, and projection operating on mismatched grids. The phantom projections are generated from a grid and the image estimate projections are generated from a grid.

Bottom Line: In order to characterize this optimization problem, we apply it to computer-simulated two-dimensional fan-beam CT data, using both ideal noiseless data and realistic data containing a level of noise comparable to that of the breast CT application.The proposed method is demonstrated for both complete field-of-view and ROI imaging.To demonstrate the potential utility of the proposed ROI imaging method, it is applied to actual CT scanner data.

View Article: PubMed Central - PubMed

Affiliation: University of Chicago, Department of Radiology, 5841 South Maryland Avenue, Chicago, Illinois 60637, United States.

ABSTRACT

One of the challenges for iterative image reconstruction (IIR) is that such algorithms solve an imaging model implicitly, requiring a complete representation of the scanned subject within the viewing domain of the scanner. This requirement can place a prohibitively high computational burden for IIR applied to x-ray computed tomography (CT), especially when high-resolution tomographic volumes are required. In this work, we aim to develop an IIR algorithm for direct region-of-interest (ROI) image reconstruction. The proposed class of IIR algorithms is based on an optimization problem that incorporates a data fidelity term, which compares a derivative of the estimated data with the available projection data. In order to characterize this optimization problem, we apply it to computer-simulated two-dimensional fan-beam CT data, using both ideal noiseless data and realistic data containing a level of noise comparable to that of the breast CT application. The proposed method is demonstrated for both complete field-of-view and ROI imaging. To demonstrate the potential utility of the proposed ROI imaging method, it is applied to actual CT scanner data.

No MeSH data available.