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Evaluation of mA Switching Method
with Penalized Weighted Least-Square
Noise Reduction for Low-dose CT
Yunjeong Lee, Hyekyun Chung, and Seungryong Cho
Department of Nuclear and Quantum Engineering, KAIST
Introduction
Results
● Computed Tomography (CT) has been increasingly used in clinics
for many purposes including diagnosis, intervention, and prognosis.
● Its image quality should be guaranteed for successful completion of
such medical tasks, but at minimal cost of radiation dose to patients.
● In this work, we investigated whether mAs switching during a scan
improves image quality after the penalized-weighted least-square
(PWLS) noise reduction under a constraint of constant total
exposure.
● Reconstructed images of the phantom
(a)
(b)
(c)
Fig. 2 (a) : from projection images acquired with 9 mAs scan
(b) : from PWLS noise reduction for 9 mAs projection images
(c) : from PWLS noise reduction for the shuffled 4 mAs and 14 mAs projection images
Methods
Acquire the data sample
● The XCAT phantom was used to simulate a human torso and we
focused our study on the reconstruction of 2 dimensional slice of
the abdominal region.
● We numerically simulated an mA switching in CT and acquired
projections: one at higher tube current time product setting (e.g. 14
mAs) and the other at lower tube current time product setting (e.g.
4 mAs) in an alternating fashion. For comparison, we also acquired
projections at a constant tube current setting (e.g. 9 mAs).
● 1 D line profile of the reconstructed image and noise-resolution trade-off.
Penalized Weighted Least-Squares (PWLS) noise reduction
● PWLS algorithm was adapted to study whether the mAs switching
method can produce better images than the constant mAs method.
The PWLS criterion can be used to estimate the corresponding ideal
sinogram by minimizing the following cost function.
Fig. 3. 2D line profile along the horizontal midline of the reconstructed image in Fig. 2.,
and noise-resolution trade-off curve for 9 mAs CT scan and modulated CT scan with PWLS
● The root mean square error and relative standard deviation in every
region of interest
TABLE 1. RMSE for regions of interest
ŷ: system-calibrated and log-transformed projection measurements
q: vector of ideal projection data
∑: diagonal variance matrix
R: roughness penalty
β : smoothing parameter
ROI 1
10.695
6.501
6.666
9 mAs
9 mAs, PWLS
mixed, PWLS
ROI 2
8.551
5.426
5.203
ROI 3
10.246
7.133
8.289
ROI 4
8.644
4.697
4.841
TABLE 2. RSD for regions of interest ( x 102 )
● The iterative Gauss-Seidal (GS) update algorithm was adapted and
the update rule follows the below equation.
n: iterative number
Ni1: upper and left pixels of qi
Ni2: right and lower pixels of qi
σi: variance
Wim: weight parameter
Root mean square error & Relative standard deviation
● For a quantitative analysis of image quality, the reconstructed image
noise was characterized by the root mean square error (RMSE) and
the relative standard deviation (RSD) of uniform regions of the
phantom as shown in the Fig. 1.
f: reconstructed image
fr: reference image
N: number of pixels
σ: variance of pixel values
μ: mean of pixel values
Fig. 1 Four regions of interest of the reconstructed image.
9 mAs
9 mAs, PWLS
mixed, PWLS
ROI 1
1.357
0.825
0.845
ROI 2
1.123
0.712
0.684
ROI 3
1.305
0.909
1.068
ROI 4
1.078
0.586
0.604
● Comparing the line profiles of the reconstructed images, we observed
that the image reconstructed from the mAs switching method has
different noise patterns from the one with the constant mAs method.
● The relative standard deviation and root mean square error at the
selected ROIs did not show practical advantage of the mAs switching
method in terms of denoising.
● The resolution-noise tradeoff curves implied that the PWLS denoising
method does not selectively work better for the data acquired by mAs
switching.
Conclusions
● Denoised images were obtained from both the contrast and alternating
mAs scanned data after the PWLS algorithm were applied. However,
alternating scanning dose not seem to show better performance compared
to the constant scanning method.
References
1. Patrick J. La Rivière, “Penalized-likelihood sinogram smoothing for low-dose CT,” Med. Phys. vol. 32, no.
6, pp. 1676-1683, June. 2005.
2. Jing Wang, Tianfang Li, Hongbing Lu, and Zhengrong Liang, “Penalized weighted least-square approach
to sinogram noise reduction and image reconstruction for low-dose x-ray computed tomography,” IEEE
Trans. Medical Imaging, vol. 25, no. 10, pp. 1272-1283, Oct. 2006.
3. Trlet Le, Rick Chartrand, and Thomas J. Asaki, “A variational approach to reconstructing images corrupted
by poisson noise,” J Math Imaging Vis 27, pp. 257-263, 2007.