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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. 5 except that the projection data contain only 256 views. The gray scale is computed by use of Eq. (15).
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f7: Same as Fig. 5 except that the projection data contain only 256 views. The gray scale is computed by use of Eq. (15).

Mentions: It is also interesting to observe the evolution of the CP algorithm iterates when the sampling ratio is more challenging. Reducing the view number to 256 projections results in a system matrix with a much larger condition number. Intermediate images of the CP algorithm are shown in Fig. 7, where the 256 view data are noiseless and perfectly consistent. We again observe that the first panel shows an image proportional to that of -tomography and as the algorithm proceeds to convergence we observe that the images seem to tend toward the phantom; however, the converged image shows noise-like texture in the images resulting from the insufficiency of the number of projections. Interestingly, however, the absolute gray level appears to be accurately recovered despite the poor overall conditioning of with 256 projections.


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. 5 except that the projection data contain only 256 views. 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

f7: Same as Fig. 5 except that the projection data contain only 256 views. The gray scale is computed by use of Eq. (15).
Mentions: It is also interesting to observe the evolution of the CP algorithm iterates when the sampling ratio is more challenging. Reducing the view number to 256 projections results in a system matrix with a much larger condition number. Intermediate images of the CP algorithm are shown in Fig. 7, where the 256 view data are noiseless and perfectly consistent. We again observe that the first panel shows an image proportional to that of -tomography and as the algorithm proceeds to convergence we observe that the images seem to tend toward the phantom; however, the converged image shows noise-like texture in the images resulting from the insufficiency of the number of projections. Interestingly, however, the absolute gray level appears to be accurately recovered despite the poor overall conditioning of with 256 projections.

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.