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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.


Images reconstructed from 256 projections with an inconsistent data model by Algorithm 1. In the top row, the derivative smoothing width is fixed at , and for the bottom row the combination parameter is set to . The gray scale for each of the images is [0.171, 0.233]. As can be seen  has no perceptible effect on these images where the data contain no truncated projections. The derivative smoothing width, on the other hand, has a clear impact on the noise texture.
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f10: Images reconstructed from 256 projections with an inconsistent data model by Algorithm 1. In the top row, the derivative smoothing width is fixed at , and for the bottom row the combination parameter is set to . The gray scale for each of the images is [0.171, 0.233]. As can be seen has no perceptible effect on these images where the data contain no truncated projections. The derivative smoothing width, on the other hand, has a clear impact on the noise texture.

Mentions: Finally, we investigate the response of Algorithm 1 to inconsistent projection data. Two forms of inconsistency are introduced in the data model. First, there is mismatch between the discrete phantom grid, , and reconstructed image grid, . Second, noise is introduced with a Poisson-like Gaussian distribution, i.e., the variance of the Gaussian is set equal to the mean, where the mean is the average transmission at each detector bin assuming photons are incident to each bin prior to passing through the subject. Thus, we are modeling a low-dose CT scan as what is typically proposed for breast CT. Images corresponding to the accurate solution of Eq. (12) with the TV constraint parameter selected to be that of the phantom, , and various values of the parameters and , are displayed in Fig. 10. The parameter clearly impacts noise texture, and here, we only aim to show how image quality changes with this parameter. Optimal setting of can only be determined based on the particular image task of the CT scan. Although has little impact on the reconstructed images for these simulations with untruncated projections and full image representation, this parameter helps to control gray level for ROI imaging.


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)

Images reconstructed from 256 projections with an inconsistent data model by Algorithm 1. In the top row, the derivative smoothing width is fixed at , and for the bottom row the combination parameter is set to . The gray scale for each of the images is [0.171, 0.233]. As can be seen  has no perceptible effect on these images where the data contain no truncated projections. The derivative smoothing width, on the other hand, has a clear impact on the noise texture.
© Copyright Policy
Related In: Results  -  Collection

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

f10: Images reconstructed from 256 projections with an inconsistent data model by Algorithm 1. In the top row, the derivative smoothing width is fixed at , and for the bottom row the combination parameter is set to . The gray scale for each of the images is [0.171, 0.233]. As can be seen has no perceptible effect on these images where the data contain no truncated projections. The derivative smoothing width, on the other hand, has a clear impact on the noise texture.
Mentions: Finally, we investigate the response of Algorithm 1 to inconsistent projection data. Two forms of inconsistency are introduced in the data model. First, there is mismatch between the discrete phantom grid, , and reconstructed image grid, . Second, noise is introduced with a Poisson-like Gaussian distribution, i.e., the variance of the Gaussian is set equal to the mean, where the mean is the average transmission at each detector bin assuming photons are incident to each bin prior to passing through the subject. Thus, we are modeling a low-dose CT scan as what is typically proposed for breast CT. Images corresponding to the accurate solution of Eq. (12) with the TV constraint parameter selected to be that of the phantom, , and various values of the parameters and , are displayed in Fig. 10. The parameter clearly impacts noise texture, and here, we only aim to show how image quality changes with this parameter. Optimal setting of can only be determined based on the particular image task of the CT scan. Although has little impact on the reconstructed images for these simulations with untruncated projections and full image representation, this parameter helps to control gray level for ROI imaging.

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.