Taiga Goto

Improvement of low-contrast detectability in low-dose hepatic multidetector computed tomography using a novel adaptive filter : Evaluation with a computer-simulated liver including tumors

Purpose:The purpose of this study was to investigate how much radiation dose can be reduced without loss of low-contrast detectability with a newly developed adaptive noise reduction filter in hepatic multidetector computed tomography (MDCT) scans by using a computer-simulated liver phantom. Materials and Methods:Simulated CT images, including liver and intrahepatic tumors, were mathematically constructed using a computer workstation to evaluate low-contrast detectability by the observer performance test. Milliampere second for construction of simulated images were 60, 80, 100, and 120 mAs (low dose) and 160 mAs (standard dose) at 120 kVp. Images with 60, 80, 100, and 120 mAs were postprocessed with the adaptive noise reduction filter. A total of 432 images were prepared and receiver operating characteristic (ROC) analysis was performed by 5 radiologists. The detectability of simulated tumor by radiologists was estimated with the area under the ROC curves (Az values). In addition, we visually evaluated CT images of 15 patients with chronic liver damage for graininess of the liver parenchyma, sharpness of the liver contour, conspicuity and marginal sharpness of the liver tumors, and overall image quality. Results:The mean Az value at 0.777 (60 mAs), 0.828 (80 mAs), and 0.844 (100 mAs) without filter was significantly lower than that of 160 mAs without filter (P < 0.001, 60 mAs; P = 0.010, 80 mAs; P = 0.040, 100 mAs). There was no statistical difference between the mean Az value at 80 mAs with and 160 mAs without the adaptive noise reduction filter (P = 0.220) and 100 mAs with and 160 mAs without the adaptive noise reduction filter (P = 0.979). In the visual evaluation of patient livers, there was no statistical difference in the graininess and sharpness of the liver, the conspicuity and marginal sharpness of the tumor, and the overall image quality between standard-dose and filtered low-dose images (Wilcoxon signed rank test, P > 0.05). Conclusion:The radiation dose can be reduced by 50% without loss of nodule detectability by applying the adaptive noise reduction filter to simulated and patient liver images obtained at MDCT.

Radiation dose reduction in hepatic multidetector computed tomography with a novel adaptive noise reduction filter

PurposeThe aim of this study was to optimize a novel adaptive noise reduction filter based on patient body weight and to investigate its utility for improving the image quality of low-dose hepatic computed tomography (CT) scans.Materials and methodsThe tube current-time product was changed from 140 to 180 and from 60 to 100 mAs at standard-and low-dose CT, respectively, based on the body weights of 45 patients. Unenhanced and two-phase contrast-enhanced helical scans were obtained at the standard dose during the hepatic arterial and equilibrium phases. During the equilibrium phase, we obtained low-dose scans of the liver immediately after standard-dose scans. The low-dose CT images were postprocessed with the filter. Two radiologists visually evaluated artifacts in the liver parenchyma and its graininess, the sharpness of the liver contour, tumor conspicuity, homogeneity of the enhancement of the portal vein, and overall image quality.ResultsThere was no statistically significant difference between standard and filtered low-dose images with respect to artifacts in the liver, the graininess of the liver parenchyma, tumor conspicuity, homogeneity of enhancement of the portal vein, or overall image quality.ConclusionThe adaptive noise reduction filter effectively reduced image noise. We confirmed the effectiveness of the filter by examining clinical hepatic images obtained at low-dose CT.

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

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.