Abstract
We propose a novel multiscale penalized weighted least-squares (PWLS) method for restoration of low-dose computed tomography (CT) sinogram. The method utilizes wavelet transform for the multiscale or multi-resolution analysis on the sinogram. Specifically the Mallat-Zhong's wavelet transform is applied to decompose the sinogram to different resolution levels. At each decomposed resolution level, a PWLS criterion is applied to restore the noise-contaminated wavelet coefficients, where the penalty is adaptive to each resolution scale and the weight is adaptive to each scale and each location. The proposed PWLS method is based on the observation that (1) the noisy sinogram of low-dose CT after logarithm transform can be modeled as signal-dependent Gaussian variables and the sample variance depends on the sample mean; and (2) the noise restoration can be more effective when it is adaptive to different resolution levels. The effectiveness of the proposed multiscale PWLS method is validated by an experimental study. The gain by multiscale approach over single-scale means is quantified by noise-resolution tradeoff measures.
| Original language | English (US) |
|---|---|
| Title of host publication | Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings |
| Pages | 3282-3285 |
| Number of pages | 4 |
| DOIs | |
| State | Published - 2006 |
| Event | 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 - New York, NY, United States Duration: Aug 30 2006 → Sep 3 2006 |
Other
| Other | 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06 |
|---|---|
| Country | United States |
| City | New York, NY |
| Period | 8/30/06 → 9/3/06 |
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ASJC Scopus subject areas
- Bioengineering
Cite this
Multiscale penalized weighted least-squares sinogram restoration for low-dose x-ray computed tomography. / Wang, Jing; Liang, Zhengrong; Lu, Hongbing.
Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings. 2006. p. 3282-3285 4029363.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Multiscale penalized weighted least-squares sinogram restoration for low-dose x-ray computed tomography
AU - Wang, Jing
AU - Liang, Zhengrong
AU - Lu, Hongbing
PY - 2006
Y1 - 2006
N2 - We propose a novel multiscale penalized weighted least-squares (PWLS) method for restoration of low-dose computed tomography (CT) sinogram. The method utilizes wavelet transform for the multiscale or multi-resolution analysis on the sinogram. Specifically the Mallat-Zhong's wavelet transform is applied to decompose the sinogram to different resolution levels. At each decomposed resolution level, a PWLS criterion is applied to restore the noise-contaminated wavelet coefficients, where the penalty is adaptive to each resolution scale and the weight is adaptive to each scale and each location. The proposed PWLS method is based on the observation that (1) the noisy sinogram of low-dose CT after logarithm transform can be modeled as signal-dependent Gaussian variables and the sample variance depends on the sample mean; and (2) the noise restoration can be more effective when it is adaptive to different resolution levels. The effectiveness of the proposed multiscale PWLS method is validated by an experimental study. The gain by multiscale approach over single-scale means is quantified by noise-resolution tradeoff measures.
AB - We propose a novel multiscale penalized weighted least-squares (PWLS) method for restoration of low-dose computed tomography (CT) sinogram. The method utilizes wavelet transform for the multiscale or multi-resolution analysis on the sinogram. Specifically the Mallat-Zhong's wavelet transform is applied to decompose the sinogram to different resolution levels. At each decomposed resolution level, a PWLS criterion is applied to restore the noise-contaminated wavelet coefficients, where the penalty is adaptive to each resolution scale and the weight is adaptive to each scale and each location. The proposed PWLS method is based on the observation that (1) the noisy sinogram of low-dose CT after logarithm transform can be modeled as signal-dependent Gaussian variables and the sample variance depends on the sample mean; and (2) the noise restoration can be more effective when it is adaptive to different resolution levels. The effectiveness of the proposed multiscale PWLS method is validated by an experimental study. The gain by multiscale approach over single-scale means is quantified by noise-resolution tradeoff measures.
UR - http://www.scopus.com/inward/record.url?scp=34047119290&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34047119290&partnerID=8YFLogxK
U2 - 10.1109/IEMBS.2006.260669
DO - 10.1109/IEMBS.2006.260669
M3 - Conference contribution
C2 - 17946172
AN - SCOPUS:34047119290
SN - 1424400325
SN - 9781424400324
SP - 3282
EP - 3285
BT - Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
ER -