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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.


ROIs reconstructed by, from bottom row to top row, FBP, -tomography, ROI IIR with  and  and same for . The constraint parameters  and  correspond to average gradient magnitudes of  and , respectively. From left to right are a transaxial slice, normal to the -axis, and vertical slices normal to the - and -axes. The display plane resolution is 0.1 mm. In order to reduce noise, three 0.1-mm thick slices along the view axis are averaged. The display gray scale that was set by eye is given above each image.
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f17: ROIs reconstructed by, from bottom row to top row, FBP, -tomography, ROI IIR with and and same for . The constraint parameters and correspond to average gradient magnitudes of and , respectively. From left to right are a transaxial slice, normal to the -axis, and vertical slices normal to the - and -axes. The display plane resolution is 0.1 mm. In order to reduce noise, three 0.1-mm thick slices along the view axis are averaged. The display gray scale that was set by eye is given above each image.

Mentions: From left to right are a transaxial slice, normal to the -axis, and vertical slices normal to the - and -axes reconstructed from the XCounter CT scan of a rat by a standard FBP implementation. The display plane resolution is 0.1 mm. In order to reduce noise, three 0.1-mm thick slices along the view axis are averaged. The display gray scale is . The boxed regions indicate the display bounds of Fig. 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)

ROIs reconstructed by, from bottom row to top row, FBP, -tomography, ROI IIR with  and  and same for . The constraint parameters  and  correspond to average gradient magnitudes of  and , respectively. From left to right are a transaxial slice, normal to the -axis, and vertical slices normal to the - and -axes. The display plane resolution is 0.1 mm. In order to reduce noise, three 0.1-mm thick slices along the view axis are averaged. The display gray scale that was set by eye is given above each image.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4326078&req=5

f17: ROIs reconstructed by, from bottom row to top row, FBP, -tomography, ROI IIR with and and same for . The constraint parameters and correspond to average gradient magnitudes of and , respectively. From left to right are a transaxial slice, normal to the -axis, and vertical slices normal to the - and -axes. The display plane resolution is 0.1 mm. In order to reduce noise, three 0.1-mm thick slices along the view axis are averaged. The display gray scale that was set by eye is given above each image.
Mentions: From left to right are a transaxial slice, normal to the -axis, and vertical slices normal to the - and -axes reconstructed from the XCounter CT scan of a rat by a standard FBP implementation. The display plane resolution is 0.1 mm. In order to reduce noise, three 0.1-mm thick slices along the view axis are averaged. The display gray scale is . The boxed regions indicate the display bounds of Fig. 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.