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Accelerated Compressed Sensing Based CT Image Reconstruction.

Hashemi S, Beheshti S, Gill PR, Paul NS, Cobbold RS - Comput Math Methods Med (2015)

Bottom Line: Therefore, the proposed method not only accelerates the reconstruction, but also removes the rebinning and interpolation errors.Simulation results are shown for phantoms and a patient.Moreover, computation times of less than 30 sec were obtained using a standard desktop computer without numerical optimization.

View Article: PubMed Central - PubMed

Affiliation: Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada M5S 3G9.

ABSTRACT
In X-ray computed tomography (CT) an important objective is to reduce the radiation dose without significantly degrading the image quality. Compressed sensing (CS) enables the radiation dose to be reduced by producing diagnostic images from a limited number of projections. However, conventional CS-based algorithms are computationally intensive and time-consuming. We propose a new algorithm that accelerates the CS-based reconstruction by using a fast pseudopolar Fourier based Radon transform and rebinning the diverging fan beams to parallel beams. The reconstruction process is analyzed using a maximum-a-posterior approach, which is transformed into a weighted CS problem. The weights involved in the proposed model are calculated based on the statistical characteristics of the reconstruction process, which is formulated in terms of the measurement noise and rebinning interpolation error. Therefore, the proposed method not only accelerates the reconstruction, but also removes the rebinning and interpolation errors. Simulation results are shown for phantoms and a patient. For example, a 512 × 512 Shepp-Logan phantom when reconstructed from 128 rebinned projections using a conventional CS method had 10% error, whereas with the proposed method the reconstruction error was less than 1%. Moreover, computation times of less than 30 sec were obtained using a standard desktop computer without numerical optimization.

No MeSH data available.


Related in: MedlinePlus

Effect of EAW on the PSNR of the reconstructed image. Solid red line shows the results when EAW was included and dashed blue line shows the reconstructed image results when EAW is not included.
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fig9: Effect of EAW on the PSNR of the reconstructed image. Solid red line shows the results when EAW was included and dashed blue line shows the reconstructed image results when EAW is not included.

Mentions: To examine the effects of EAW on measurement noise, 128 equiangular projections through the Shepp-Logan phantom were computed and Poisson noise was added to the projections. Figure 9 shows the effect of EAW inclusion for different input peak signal-to-noise ratios (PSNR) on the PSNR of the reconstructed images. Images were reconstructed with the proposed method once with including EAW that is calculated by (9) and once without EAW. As can be seen, the PSNR is improved when the input noise is larger (small input PSNR) and its effect is less when the noise is low (larger input PSNR). The input PSNR was measured from the FBP reconstructed images.


Accelerated Compressed Sensing Based CT Image Reconstruction.

Hashemi S, Beheshti S, Gill PR, Paul NS, Cobbold RS - Comput Math Methods Med (2015)

Effect of EAW on the PSNR of the reconstructed image. Solid red line shows the results when EAW was included and dashed blue line shows the reconstructed image results when EAW is not included.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4489012&req=5

fig9: Effect of EAW on the PSNR of the reconstructed image. Solid red line shows the results when EAW was included and dashed blue line shows the reconstructed image results when EAW is not included.
Mentions: To examine the effects of EAW on measurement noise, 128 equiangular projections through the Shepp-Logan phantom were computed and Poisson noise was added to the projections. Figure 9 shows the effect of EAW inclusion for different input peak signal-to-noise ratios (PSNR) on the PSNR of the reconstructed images. Images were reconstructed with the proposed method once with including EAW that is calculated by (9) and once without EAW. As can be seen, the PSNR is improved when the input noise is larger (small input PSNR) and its effect is less when the noise is low (larger input PSNR). The input PSNR was measured from the FBP reconstructed images.

Bottom Line: Therefore, the proposed method not only accelerates the reconstruction, but also removes the rebinning and interpolation errors.Simulation results are shown for phantoms and a patient.Moreover, computation times of less than 30 sec were obtained using a standard desktop computer without numerical optimization.

View Article: PubMed Central - PubMed

Affiliation: Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada M5S 3G9.

ABSTRACT
In X-ray computed tomography (CT) an important objective is to reduce the radiation dose without significantly degrading the image quality. Compressed sensing (CS) enables the radiation dose to be reduced by producing diagnostic images from a limited number of projections. However, conventional CS-based algorithms are computationally intensive and time-consuming. We propose a new algorithm that accelerates the CS-based reconstruction by using a fast pseudopolar Fourier based Radon transform and rebinning the diverging fan beams to parallel beams. The reconstruction process is analyzed using a maximum-a-posterior approach, which is transformed into a weighted CS problem. The weights involved in the proposed model are calculated based on the statistical characteristics of the reconstruction process, which is formulated in terms of the measurement noise and rebinning interpolation error. Therefore, the proposed method not only accelerates the reconstruction, but also removes the rebinning and interpolation errors. Simulation results are shown for phantoms and a patient. For example, a 512 × 512 Shepp-Logan phantom when reconstructed from 128 rebinned projections using a conventional CS method had 10% error, whereas with the proposed method the reconstruction error was less than 1%. Moreover, computation times of less than 30 sec were obtained using a standard desktop computer without numerical optimization.

No MeSH data available.


Related in: MedlinePlus