A method to extract image noise level from patient images in CT

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Image quality in computed tomography (CT) is usually not quantified in real time from patient scans. Rather, phantom scans are employed to measure the contrast, noise, and spatial resolution properties of a given system. This approach, however, is not ideal because many factors are difficult to represent using phantoms like variations in patient size, composition, and position within the gantry. Several methods for measuring the noise found within patient scans have been proposed. All of these methods rely on segmenting out relatively uniform regions within the patient in order to differentiate between morphological variations and pixel differences due to photon noise. In order to avoid this segmentation step, we propose to analyze the noise signal in the air surrounding the patient. We will demonstrate that the air signal surrounding the patient acts as a surrogate for the noise within the contours of the patient.


Our work builds off the global noise index (GNI) method. In the GNI method, adjacent axial CT image slices are subtracted to remove the majority of the morphological variations in the data. Remaining morphological variations are removed with an edge-finding algorithm. Then the image is divided up into small regions of interest (ROI) and the pixel standard deviation is computed for each ROI. Only those ROIs not containing bone or air are then used to make a histogram of the standard deviation within the image. The mode of this histogram is referred to as the GNI. We refer to this as the traditional GNI (tradGNI). Our modification to this workflow is to apply this metric to just the air signal surrounding the patient. We evaluate the correlation between using the air signal and the in-phantom metric value for titration over: dose level, image slice thickness, kernel sharpness, iterative reconstruction level, model based iterative reconstruction, reconstruction field of view, and reconstruction interval. 373 patient abdomen pelvis exams were collected and the air based noise estimation metrics applied. Liver standard deviation values were measured on 40 of the patients and correlated with air GNI calculations.


Our results show excellent linear correlation (R2 > 0.99) between the tradGNI being applied inside and outside of a phantom object. Our results are also shown to still be predictive of the noise under all scan parameters studied, including iterative and model based reconstruction. Fits of tradGNI versus dose level exhibited the expected square root dependence. Air based GNI metrics were predictive of human patient noise level and also correlated with manual liver standard deviation measurement (R2 = 0.73).


Our results demonstrate the signal in the air surrounding an imaging object can accurately be used as a surrogate for the image noise within the object. Our method should enable faster and more robust patient specific image quality assessment due to the lack of the need to segment noise from morphological variations within a patient.

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