Iterative raw measurements restoration method with penalized weighted least squares approach for low-dose CT

Hisashi Takahashi; Taiga Goto; Koichi Hirokawa; Osamu Miyazaki

doi:10.1117/12.2042984

Abstract

Statistical iterative reconstruction and post-log data restoration algorithms for CT noise reduction have been widely studied and these techniques have enabled us to reduce irradiation doses while maintaining image qualities. In low dose scanning, electronic noise becomes obvious and it results in some non-positive signals in raw measurements. The nonpositive signal should be converted to positive signal so that it can be log-transformed. Since conventional conversion methods do not consider local variance on the sinogram, they have difficulty of controlling the strength of the filtering. Thus, in this work, we propose a method to convert the non-positive signal to the positive signal by mainly controlling the local variance. The method is implemented in two separate steps. First, an iterative restoration algorithm based on penalized weighted least squares is used to mitigate the effect of electronic noise. The algorithm preserves the local mean and reduces the local variance induced by the electronic noise. Second, smoothed raw measurements by the iterative algorithm are converted to the positive signal according to a function which replaces the non-positive signal with its local mean. In phantom studies, we confirm that the proposed method properly preserves the local mean and reduce the variance induced by the electronic noise. Our technique results in dramatically reduced shading artifacts and can also successfully cooperate with the post-log data filter to reduce streak artifacts.

Citation Download Citation

Hisashi Takahashi, Hisashi Takahashi, Taiga Goto, Taiga Goto, Koichi Hirokawa, Koichi Hirokawa, Osamu Miyazaki, Osamu Miyazaki, "Iterative raw measurements restoration method with penalized weighted least squares approach for low-dose CT", Proc. SPIE 9033, Medical Imaging 2014: Physics of Medical Imaging, 90333D (19 March 2014); doi: 10.1117/12.2042984; https://doi.org/10.1117/12.2042984

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