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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 inconsistent noisy data containing 256 projections. Each of the projections is truncated so that the field-of-view (FOV) of the scan is a centered circle of diameter 9 cm and the image representation is restricted so that all pixels are contained in the FOV. The array of images results from accurate solution to Eq. (12) for  and various values of  and the TV constraint parameter is set according to Eq. (18). The values of , see Eq. (18), indicated in the left most images remain constant for each row, and the values of  indicated in the bottom row of images remain constant for each column. The image gray scale is [0, 0.35], wide enough to include all images for the sake of comparison. For individual images, of course, a narrower gray scale may be employed to improve contrast. The test phantom is displayed in the lower, right panel for reference.
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f14: Images reconstructed from inconsistent noisy data containing 256 projections. Each of the projections is truncated so that the field-of-view (FOV) of the scan is a centered circle of diameter 9 cm and the image representation is restricted so that all pixels are contained in the FOV. The array of images results from accurate solution to Eq. (12) for and various values of and the TV constraint parameter is set according to Eq. (18). The values of , see Eq. (18), indicated in the left most images remain constant for each row, and the values of indicated in the bottom row of images remain constant for each column. The image gray scale is [0, 0.35], wide enough to include all images for the sake of comparison. For individual images, of course, a narrower gray scale may be employed to improve contrast. The test phantom is displayed in the lower, right panel for reference.

Mentions: For the final simulations, we again employ the ROI-only representation, but model inconsistent projection data with grid mismatch and measurement noise, corresponding to photons incident to each detector bin. Again, the data acquisition configuration is chosen so that the FOV coincides with the 9-cm diameter ROI. In the grid of images shown in Figs. 14 and 15, results are shown for various values of and , while is fixed to one. The value of is parametrized in these figures with the multiplicative factor γ=γfγFOV,(18)where is the TV of the phantom within the FOV. The same results are shown in both figures, because Fig. 14, with its wide all encompassing gray scale window, provides a sense of the gray level shifting, while Fig. 15 depicts contrast and noise texture more clearly.


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 inconsistent noisy data containing 256 projections. Each of the projections is truncated so that the field-of-view (FOV) of the scan is a centered circle of diameter 9 cm and the image representation is restricted so that all pixels are contained in the FOV. The array of images results from accurate solution to Eq. (12) for  and various values of  and the TV constraint parameter is set according to Eq. (18). The values of , see Eq. (18), indicated in the left most images remain constant for each row, and the values of  indicated in the bottom row of images remain constant for each column. The image gray scale is [0, 0.35], wide enough to include all images for the sake of comparison. For individual images, of course, a narrower gray scale may be employed to improve contrast. The test phantom is displayed in the lower, right panel for reference.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4326078&req=5

f14: Images reconstructed from inconsistent noisy data containing 256 projections. Each of the projections is truncated so that the field-of-view (FOV) of the scan is a centered circle of diameter 9 cm and the image representation is restricted so that all pixels are contained in the FOV. The array of images results from accurate solution to Eq. (12) for and various values of and the TV constraint parameter is set according to Eq. (18). The values of , see Eq. (18), indicated in the left most images remain constant for each row, and the values of indicated in the bottom row of images remain constant for each column. The image gray scale is [0, 0.35], wide enough to include all images for the sake of comparison. For individual images, of course, a narrower gray scale may be employed to improve contrast. The test phantom is displayed in the lower, right panel for reference.
Mentions: For the final simulations, we again employ the ROI-only representation, but model inconsistent projection data with grid mismatch and measurement noise, corresponding to photons incident to each detector bin. Again, the data acquisition configuration is chosen so that the FOV coincides with the 9-cm diameter ROI. In the grid of images shown in Figs. 14 and 15, results are shown for various values of and , while is fixed to one. The value of is parametrized in these figures with the multiplicative factor γ=γfγFOV,(18)where is the TV of the phantom within the FOV. The same results are shown in both figures, because Fig. 14, with its wide all encompassing gray scale window, provides a sense of the gray level shifting, while Fig. 15 depicts contrast and noise texture more clearly.

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.