### Abstract

The noise of low-dose computed tomography (CT) sinogram follows approximately a Gaussian distribution with nonlinear dependence between the sample mean and variance. The noise is statistically uncorrelated among detector bins at any view angle. However the correlation coefficient matrix of data signal indicates a strong signal correlation among neighboring views. Based on above observations, Karhunen-Loève (KL) transform can be used to de-correlate the signal among the neighboring views. In each KL component, a penalized weighted least-squares (PWLS) objective function can be constructed and optimal sinogram can be estimated by minimizing the objective function, followed by filtered backprojection (FBP) for CT image reconstruction. In this work, we compared the KL-PWLS method with an iterative image reconstruction algorithm, which uses the Gauss-Seidel iterative calculation to minimize the PWLS objective function in image domain. We also compared the KL-PWLS with an iterative sinogram smoothing algorithm, which uses the iterated conditional mode calculation to minimize the PWLS objective function in sinogram space, followed by FBP for image reconstruction. Phantom experiments show a comparable performance of these three PWLS methods in suppressing the noise-induced artifacts and preserving resolution in reconstructed images. Computer simulation concurs with the phantom experiments in terms of noise-resolution tradeoff and detectability in low contrast environment. The KL-PWLS noise reduction may have the advantage in computation for low-dose CT imaging, especially for dynamic high-resolution studies.

Original language | English (US) |
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Title of host publication | Progress in Biomedical Optics and Imaging - Proceedings of SPIE |

Volume | 6142 III |

DOIs | |

State | Published - 2006 |

Event | Medical Imaging 2006: Physics of Medical Imaging - San Diego, CA, United States Duration: Feb 12 2006 → Feb 16 2006 |

### Other

Other | Medical Imaging 2006: Physics of Medical Imaging |
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Country | United States |

City | San Diego, CA |

Period | 2/12/06 → 2/16/06 |

### Fingerprint

### Keywords

- KL transform
- Low-dose CT
- Noise-resolution tradeoff
- Penalized weighted least-squares (PWLS)
- ROC curve

### ASJC Scopus subject areas

- Engineering(all)

### Cite this

*Progress in Biomedical Optics and Imaging - Proceedings of SPIE*(Vol. 6142 III). [614247] https://doi.org/10.1117/12.653903

**Penalized weighted least-squares approach for low-dose X-ray computed tomography.** / Wang, Jing; Li, Tianfang; Lu, Hongbing; Liang, Zhengrong.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Progress in Biomedical Optics and Imaging - Proceedings of SPIE.*vol. 6142 III, 614247, Medical Imaging 2006: Physics of Medical Imaging, San Diego, CA, United States, 2/12/06. https://doi.org/10.1117/12.653903

}

TY - GEN

T1 - Penalized weighted least-squares approach for low-dose X-ray computed tomography

AU - Wang, Jing

AU - Li, Tianfang

AU - Lu, Hongbing

AU - Liang, Zhengrong

PY - 2006

Y1 - 2006

N2 - The noise of low-dose computed tomography (CT) sinogram follows approximately a Gaussian distribution with nonlinear dependence between the sample mean and variance. The noise is statistically uncorrelated among detector bins at any view angle. However the correlation coefficient matrix of data signal indicates a strong signal correlation among neighboring views. Based on above observations, Karhunen-Loève (KL) transform can be used to de-correlate the signal among the neighboring views. In each KL component, a penalized weighted least-squares (PWLS) objective function can be constructed and optimal sinogram can be estimated by minimizing the objective function, followed by filtered backprojection (FBP) for CT image reconstruction. In this work, we compared the KL-PWLS method with an iterative image reconstruction algorithm, which uses the Gauss-Seidel iterative calculation to minimize the PWLS objective function in image domain. We also compared the KL-PWLS with an iterative sinogram smoothing algorithm, which uses the iterated conditional mode calculation to minimize the PWLS objective function in sinogram space, followed by FBP for image reconstruction. Phantom experiments show a comparable performance of these three PWLS methods in suppressing the noise-induced artifacts and preserving resolution in reconstructed images. Computer simulation concurs with the phantom experiments in terms of noise-resolution tradeoff and detectability in low contrast environment. The KL-PWLS noise reduction may have the advantage in computation for low-dose CT imaging, especially for dynamic high-resolution studies.

AB - The noise of low-dose computed tomography (CT) sinogram follows approximately a Gaussian distribution with nonlinear dependence between the sample mean and variance. The noise is statistically uncorrelated among detector bins at any view angle. However the correlation coefficient matrix of data signal indicates a strong signal correlation among neighboring views. Based on above observations, Karhunen-Loève (KL) transform can be used to de-correlate the signal among the neighboring views. In each KL component, a penalized weighted least-squares (PWLS) objective function can be constructed and optimal sinogram can be estimated by minimizing the objective function, followed by filtered backprojection (FBP) for CT image reconstruction. In this work, we compared the KL-PWLS method with an iterative image reconstruction algorithm, which uses the Gauss-Seidel iterative calculation to minimize the PWLS objective function in image domain. We also compared the KL-PWLS with an iterative sinogram smoothing algorithm, which uses the iterated conditional mode calculation to minimize the PWLS objective function in sinogram space, followed by FBP for image reconstruction. Phantom experiments show a comparable performance of these three PWLS methods in suppressing the noise-induced artifacts and preserving resolution in reconstructed images. Computer simulation concurs with the phantom experiments in terms of noise-resolution tradeoff and detectability in low contrast environment. The KL-PWLS noise reduction may have the advantage in computation for low-dose CT imaging, especially for dynamic high-resolution studies.

KW - KL transform

KW - Low-dose CT

KW - Noise-resolution tradeoff

KW - Penalized weighted least-squares (PWLS)

KW - ROC curve

UR - http://www.scopus.com/inward/record.url?scp=33745357597&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33745357597&partnerID=8YFLogxK

U2 - 10.1117/12.653903

DO - 10.1117/12.653903

M3 - Conference contribution

AN - SCOPUS:33745357597

SN - 0819461857

SN - 9780819461858

VL - 6142 III

BT - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

ER -