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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.


Images reconstructed from truncated, ideal noiseless data. The shown images on the left column are converged solutions to Eq. (12) with  and , and those on the right column are converged solutions of the same optimization without the weighting , i.e., TV-constrained Euclidean data discrepancy minimization. The value of  indicated in each panel determines the TV constraint parameter  by Eq. (17). The horizontal lines indicate the location of the image profiles plotted in Fig. 12. The gray scale is set to [0.165, 0.265].
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f11: Images reconstructed from truncated, ideal noiseless data. The shown images on the left column are converged solutions to Eq. (12) with and , and those on the right column are converged solutions of the same optimization without the weighting , i.e., TV-constrained Euclidean data discrepancy minimization. The value of indicated in each panel determines the TV constraint parameter by Eq. (17). The horizontal lines indicate the location of the image profiles plotted in Fig. 12. The gray scale is set to [0.165, 0.265].

Mentions: First, we investigate the impact of truncated data while keeping the image representation complete, i.e., the image pixels cover the region where the object support is nonzero. The simulated projection data are ideal and noiseless, containing 256 projections. Each of the projections is truncated so that the FOV of the scan is a centered circle of diameter 9 cm, i.e., half the width of the image array. The left column of images in Fig. 11 corresponds to converged solutions of Eq. (12) for and , and for comparison, the right column of images results from the same optimization problem without the proposed weighting . For this configuration, it is not clear what is the optimal choice of the TV constraint parameter , even though we know what is the test phantom. Using the TV of the phantom within the FOV, and the TV of the entire phantom as lower and upper bounds on , respectively, we reconstruct images for four intermediate values of parametrized by γ=(1−γw)γFOV+γwγ0.(17)


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)

Images reconstructed from truncated, ideal noiseless data. The shown images on the left column are converged solutions to Eq. (12) with  and , and those on the right column are converged solutions of the same optimization without the weighting , i.e., TV-constrained Euclidean data discrepancy minimization. The value of  indicated in each panel determines the TV constraint parameter  by Eq. (17). The horizontal lines indicate the location of the image profiles plotted in Fig. 12. The gray scale is set to [0.165, 0.265].
© Copyright Policy
Related In: Results  -  Collection

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

f11: Images reconstructed from truncated, ideal noiseless data. The shown images on the left column are converged solutions to Eq. (12) with and , and those on the right column are converged solutions of the same optimization without the weighting , i.e., TV-constrained Euclidean data discrepancy minimization. The value of indicated in each panel determines the TV constraint parameter by Eq. (17). The horizontal lines indicate the location of the image profiles plotted in Fig. 12. The gray scale is set to [0.165, 0.265].
Mentions: First, we investigate the impact of truncated data while keeping the image representation complete, i.e., the image pixels cover the region where the object support is nonzero. The simulated projection data are ideal and noiseless, containing 256 projections. Each of the projections is truncated so that the FOV of the scan is a centered circle of diameter 9 cm, i.e., half the width of the image array. The left column of images in Fig. 11 corresponds to converged solutions of Eq. (12) for and , and for comparison, the right column of images results from the same optimization problem without the proposed weighting . For this configuration, it is not clear what is the optimal choice of the TV constraint parameter , even though we know what is the test phantom. Using the TV of the phantom within the FOV, and the TV of the entire phantom as lower and upper bounds on , respectively, we reconstruct images for four intermediate values of parametrized by γ=(1−γw)γFOV+γwγ0.(17)

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.