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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.


Same as Fig. 8 except that the simulated projection data contain only 64 views. In this case, the low iteration images are contaminated by view-angle undersampling artifacts, which appear in the form of streaks. As the image estimates approach convergence, we observe that the phantom is again accurately recovered. The gray scale is computed by use of Eq. (15).
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f9: Same as Fig. 8 except that the simulated projection data contain only 64 views. In this case, the low iteration images are contaminated by view-angle undersampling artifacts, which appear in the form of streaks. As the image estimates approach convergence, we observe that the phantom is again accurately recovered. The gray scale is computed by use of Eq. (15).

Mentions: In order to further test the robustness against view angle undersampling, we reduce the ideal data set to only 64 projections. The resulting image sequence appears in Fig. 9. We observe that Algorithm 1 can accurately recover the test phantom as the image estimates near convergence. The -tomography interpretation of the low iteration images, however, suffers due to the streaks from the view angle undersampling. Nevertheless, the insensitivity of the proposed data discrepancy term to absolute gray level does not appear to hinder the ability of Algorithm 1 to recover the phantom from sparse view data.


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)

Same as Fig. 8 except that the simulated projection data contain only 64 views. In this case, the low iteration images are contaminated by view-angle undersampling artifacts, which appear in the form of streaks. As the image estimates approach convergence, we observe that the phantom is again accurately recovered. The gray scale is computed by use of Eq. (15).
© Copyright Policy
Related In: Results  -  Collection

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

f9: Same as Fig. 8 except that the simulated projection data contain only 64 views. In this case, the low iteration images are contaminated by view-angle undersampling artifacts, which appear in the form of streaks. As the image estimates approach convergence, we observe that the phantom is again accurately recovered. The gray scale is computed by use of Eq. (15).
Mentions: In order to further test the robustness against view angle undersampling, we reduce the ideal data set to only 64 projections. The resulting image sequence appears in Fig. 9. We observe that Algorithm 1 can accurately recover the test phantom as the image estimates near convergence. The -tomography interpretation of the low iteration images, however, suffers due to the streaks from the view angle undersampling. Nevertheless, the insensitivity of the proposed data discrepancy term to absolute gray level does not appear to hinder the ability of Algorithm 1 to recover the phantom from sparse view data.

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.