Limits...
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.


Iterative image reconstruction from noiseless projection data containing 1024 views by use of a CP algorithm designed to minimize . The image sequence shows intermediate iterations, and the iteration number is indicated in each panel. The gray scale window appears above each image. The gray scale is computed by use of Eq. (15).
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4326078&req=5

f5: Iterative image reconstruction from noiseless projection data containing 1024 views by use of a CP algorithm designed to minimize . The image sequence shows intermediate iterations, and the iteration number is indicated in each panel. The gray scale window appears above each image. The gray scale is computed by use of Eq. (15).

Mentions: The next set of IIR images result from a CP algorithm designed to minimize the proposed data fidelity with and . The same pseudocode from Algorithm 2 of Ref. 9 can be employed to minimize this data fidelity. Similar to gradient descent, one can show that the first iteration of this CP algorithm is proportional to a -tomography image if a zero initial estimate is used. Intermediate images of this algorithm are shown in Fig. 5, where the 1024-view data are noiseless and perfectly consistent. Indeed, we 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 tend toward the phantom. Minimization of both and results in accurate recovery of the test phantom, but the approach to this solution is quite different; the first iterate in minimizing already has much of the tissue structure information relative to that of minimizing . Although the image appearance of the early iterations in Fig. 5 has been explained, it is not obvious that the phantom should be recovered accurately at convergence—in light of the finite-differencing filter applied to both system matrix and data in .


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)

Iterative image reconstruction from noiseless projection data containing 1024 views by use of a CP algorithm designed to minimize . The image sequence shows intermediate iterations, and the iteration number is indicated in each panel. The gray scale window appears above each image. 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

f5: Iterative image reconstruction from noiseless projection data containing 1024 views by use of a CP algorithm designed to minimize . The image sequence shows intermediate iterations, and the iteration number is indicated in each panel. The gray scale window appears above each image. The gray scale is computed by use of Eq. (15).
Mentions: The next set of IIR images result from a CP algorithm designed to minimize the proposed data fidelity with and . The same pseudocode from Algorithm 2 of Ref. 9 can be employed to minimize this data fidelity. Similar to gradient descent, one can show that the first iteration of this CP algorithm is proportional to a -tomography image if a zero initial estimate is used. Intermediate images of this algorithm are shown in Fig. 5, where the 1024-view data are noiseless and perfectly consistent. Indeed, we 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 tend toward the phantom. Minimization of both and results in accurate recovery of the test phantom, but the approach to this solution is quite different; the first iterate in minimizing already has much of the tissue structure information relative to that of minimizing . Although the image appearance of the early iterations in Fig. 5 has been explained, it is not obvious that the phantom should be recovered accurately at convergence—in light of the finite-differencing filter applied to both system matrix and data in .

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.