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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.


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.
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f16: 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.

Mentions: To demonstrate application of the proposed algorithm to actual scanner data, we employ an XCounter CT scan of a rat. The data set contains 1000 projections with a detector array where the detection elements are 0.1 mm in width. In order to test the ROI capability, we truncate the projection data to include only transmission rays passing through a cylindrical subvolume of radius 8 mm and height 18 mm. This volume contains part of the rat jaw and a fore paw. For reference, we employed FBP to reconstruct the complete untruncated data set, and display slices that traverse the chosen ROI in Fig. 16.


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)

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.
© Copyright Policy
Related In: Results  -  Collection

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

f16: 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.
Mentions: To demonstrate application of the proposed algorithm to actual scanner data, we employ an XCounter CT scan of a rat. The data set contains 1000 projections with a detector array where the detection elements are 0.1 mm in width. In order to test the ROI capability, we truncate the projection data to include only transmission rays passing through a cylindrical subvolume of radius 8 mm and height 18 mm. This volume contains part of the rat jaw and a fore paw. For reference, we employed FBP to reconstruct the complete untruncated data set, and display slices that traverse the chosen ROI in Fig. 16.

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.