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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 ideal noiseless data containing 256 projections. The image representation is restricted so that all pixels are contained in a 9-cm diameter ROI. The left image is a converged solution to Eq. (12) with  and , and the the right image is a converged solution of the same optimization without the weighting , i.e., TV-constrained Euclidean data discrepancy minimization. The TV constraint parameter is set to the TV of the phantom within the ROI. The gray scale is computed by use of Eq. (15).
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f13: Images reconstructed from ideal noiseless data containing 256 projections. The image representation is restricted so that all pixels are contained in a 9-cm diameter ROI. The left image is a converged solution to Eq. (12) with and , and the the right image is a converged solution of the same optimization without the weighting , i.e., TV-constrained Euclidean data discrepancy minimization. The TV constraint parameter is set to the TV of the phantom within the ROI. The gray scale is computed by use of Eq. (15).

Mentions: Second, we investigate the impact of employing an incomplete image representation, where pixels cover only the region of the FOV from the previous study, a centered 9-cm diameter circular ROI. Note that in this case it does not matter whether the projections are truncated to this 9 cm ROI or not. For untruncated projections, measurements corresponding to the rays not intersecting the ROI end up not playing any role in the IIR algorithm. Converged images—with and without the weighting—obtained by Algorithm 1 appear in Fig. 13. It is in the use of the smaller ROI representation where we see a possible major advantage of the weighting. The weighting reduces the data discrepancy incurred by using too small an image representation. As a result, the TV constraint can be applied without destroying structure information in the ROI while the use of the TV constraint without the weighting destroys this information. Inspecting the gray scale windows for both plots, we note that the automatically computed gray scale for the weighting image is substantially lower than the values of the actual phantom, while the image without weighting has approximately the correct absolute gray scale (aside from the fact that the structure information is lost). That the absolute gray scale of the weighting is lost is the primary motivation for introducing the parameter .


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 ideal noiseless data containing 256 projections. The image representation is restricted so that all pixels are contained in a 9-cm diameter ROI. The left image is a converged solution to Eq. (12) with  and , and the the right image is a converged solution of the same optimization without the weighting , i.e., TV-constrained Euclidean data discrepancy minimization. The TV constraint parameter is set to the TV of the phantom within the ROI. 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

f13: Images reconstructed from ideal noiseless data containing 256 projections. The image representation is restricted so that all pixels are contained in a 9-cm diameter ROI. The left image is a converged solution to Eq. (12) with and , and the the right image is a converged solution of the same optimization without the weighting , i.e., TV-constrained Euclidean data discrepancy minimization. The TV constraint parameter is set to the TV of the phantom within the ROI. The gray scale is computed by use of Eq. (15).
Mentions: Second, we investigate the impact of employing an incomplete image representation, where pixels cover only the region of the FOV from the previous study, a centered 9-cm diameter circular ROI. Note that in this case it does not matter whether the projections are truncated to this 9 cm ROI or not. For untruncated projections, measurements corresponding to the rays not intersecting the ROI end up not playing any role in the IIR algorithm. Converged images—with and without the weighting—obtained by Algorithm 1 appear in Fig. 13. It is in the use of the smaller ROI representation where we see a possible major advantage of the weighting. The weighting reduces the data discrepancy incurred by using too small an image representation. As a result, the TV constraint can be applied without destroying structure information in the ROI while the use of the TV constraint without the weighting destroys this information. Inspecting the gray scale windows for both plots, we note that the automatically computed gray scale for the weighting image is substantially lower than the values of the actual phantom, while the image without weighting has approximately the correct absolute gray scale (aside from the fact that the structure information is lost). That the absolute gray scale of the weighting is lost is the primary motivation for introducing the parameter .

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.