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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. 7 except that the image sequence results from use of Algorithm 1, which is designed to solve total variation (TV)-constrained minimization of , see Eq. (12). Note that the first images resemble that of -tomography. We also observe that at convergence the phantom is recovered accurately. The gray scale is computed by use of Eq. (15).
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f8: Same as Fig. 7 except that the image sequence results from use of Algorithm 1, which is designed to solve total variation (TV)-constrained minimization of , see Eq. (12). Note that the first images resemble that of -tomography. We also observe that at convergence the phantom is recovered accurately. The gray scale is computed by use of Eq. (15).

Mentions: As discussed in Sec. 2.3, the view angle undersampling can be dealt with by exploiting GMI sparsity. To demonstrate this, we solve the optimization problem in Eq. (12) using the pseudocode in Algorithm 1 and again model noiseless consistent 256-view projection data. Because the data are ideal, the parameters and are again set to zero. The TV constraint parameter is selected to be that of the phantom, . The resulting sequence of images is shown in Fig. 8, and they resemble those of Fig. 7 except that at iteration 10 and after the noise-like artifacts seem to be removed by the addition steps constraining the image TV. Furthermore, at convergence, the phantom is recovered to high accuracy. The accurate phantom recovery for this example, of course, depends on the fact that we know the correct TV constraint value , but the purpose here is to show that an ideal program can recover the phantom under ideal conditions. That it can do so is not obvious because the data discrepancy objective is different than the standard Euclidean form. Although the -tomography interpretation of the low iteration images is useful, it is important to keep in mind that the specific evolution of the images depends on the values of all of the parameters in Algorithm 1 in addition to parameters of the optimization problem Eq. (12). The converged image, on the other hand, depends only on parameters of the optimization problem.


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. 7 except that the image sequence results from use of Algorithm 1, which is designed to solve total variation (TV)-constrained minimization of , see Eq. (12). Note that the first images resemble that of -tomography. We also observe that at convergence the phantom is recovered accurately. The gray scale is computed by use of Eq. (15).
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4326078&req=5

f8: Same as Fig. 7 except that the image sequence results from use of Algorithm 1, which is designed to solve total variation (TV)-constrained minimization of , see Eq. (12). Note that the first images resemble that of -tomography. We also observe that at convergence the phantom is recovered accurately. The gray scale is computed by use of Eq. (15).
Mentions: As discussed in Sec. 2.3, the view angle undersampling can be dealt with by exploiting GMI sparsity. To demonstrate this, we solve the optimization problem in Eq. (12) using the pseudocode in Algorithm 1 and again model noiseless consistent 256-view projection data. Because the data are ideal, the parameters and are again set to zero. The TV constraint parameter is selected to be that of the phantom, . The resulting sequence of images is shown in Fig. 8, and they resemble those of Fig. 7 except that at iteration 10 and after the noise-like artifacts seem to be removed by the addition steps constraining the image TV. Furthermore, at convergence, the phantom is recovered to high accuracy. The accurate phantom recovery for this example, of course, depends on the fact that we know the correct TV constraint value , but the purpose here is to show that an ideal program can recover the phantom under ideal conditions. That it can do so is not obvious because the data discrepancy objective is different than the standard Euclidean form. Although the -tomography interpretation of the low iteration images is useful, it is important to keep in mind that the specific evolution of the images depends on the values of all of the parameters in Algorithm 1 in addition to parameters of the optimization problem Eq. (12). The converged image, on the other hand, depends only on parameters of the optimization problem.

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.