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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.


Analytic reconstruction from noiseless data comprised of 1024 projections. (a) Reconstruction by filtered back-projection (FBP), using a pure ramp filter, and (b) -tomography where the data are convolved with a finite differencing approximation to the second derivative prior to back-projection. The -tomography image aids in the interpretation of the images at low iteration numbers of the presented iterative image reconstruction (IIR) algorithm. The gray scale is computed by use of Eq. (15).
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f3: Analytic reconstruction from noiseless data comprised of 1024 projections. (a) Reconstruction by filtered back-projection (FBP), using a pure ramp filter, and (b) -tomography where the data are convolved with a finite differencing approximation to the second derivative prior to back-projection. The -tomography image aids in the interpretation of the images at low iteration numbers of the presented iterative image reconstruction (IIR) algorithm. The gray scale is computed by use of Eq. (15).

Mentions: To assist in the interpretation of the CT IIR results, we show FBP and -tomography images in Fig. 3. The -tomography image shows the expected enhancement at tissue borders and loss of the absolute gray level. Again, the reason -tomography might be an interesting choice in addition to the edge enhancement is that the filter is local, allowing for projection truncation.


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)

Analytic reconstruction from noiseless data comprised of 1024 projections. (a) Reconstruction by filtered back-projection (FBP), using a pure ramp filter, and (b) -tomography where the data are convolved with a finite differencing approximation to the second derivative prior to back-projection. The -tomography image aids in the interpretation of the images at low iteration numbers of the presented iterative image reconstruction (IIR) algorithm. 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

f3: Analytic reconstruction from noiseless data comprised of 1024 projections. (a) Reconstruction by filtered back-projection (FBP), using a pure ramp filter, and (b) -tomography where the data are convolved with a finite differencing approximation to the second derivative prior to back-projection. The -tomography image aids in the interpretation of the images at low iteration numbers of the presented iterative image reconstruction (IIR) algorithm. The gray scale is computed by use of Eq. (15).
Mentions: To assist in the interpretation of the CT IIR results, we show FBP and -tomography images in Fig. 3. The -tomography image shows the expected enhancement at tissue borders and loss of the absolute gray level. Again, the reason -tomography might be an interesting choice in addition to the edge enhancement is that the filter is local, allowing for projection truncation.

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.