Accurate assessment and prediction of noise in clinical CT images

Authors

  • Tian Xiaoyu,

    1. Carl E. Ravin Advanced Imaging Laboratories, Department of Biomedical Engineering, Department of Radiology, Duke University, Durham, North Carolina 27705
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    • a)

      Author to whom correspondence should be addressed. Electronic mail: xt3@duke.edu

  • Samei Ehsan

    1. Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Medical Physics Graduate Program, Departments of Physics and Biomedical Engineering, Duke University Medical Center, Durham, North Carolina 27705
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Abstract

Purpose:

The objectives of this study were (a) to devise a technique for measuring quantum noise in clinical body computed tomography (CT) images and (b) to develop a model for predicting that noise with high accuracy.

Methods:

The study included 83 clinical image sets at two dose levels (clinical and 50% reduced dose levels). The quantum noise in clinical images was measured by subtracting sequential slices and filtering out edges. Noise was then measured in the resultant uniform area. The noise measurement technique was validated using 17 clinical image cases and a turkey phantom. With a validated method to measure noise in clinical images, this noise was predicted by establishing the correlation between water-equivalent diameter (Dw) and noise in a variable-sized phantom and ascribing a noise level to the patient based on Dw estimated from CT image. The accuracy of this prediction model was validated using 66 clinical image sets.

Results:

The error in noise measurement was within 1.5 HU across two reconstruction algorithms. In terms of noise prediction, across the 83 clinical image sets, the average discrepancies between predicted and measured noise were 6.9% and 6.6% for adaptive statistical iterative reconstruction and filtered back projection reconstruction, respectively.

Conclusions:

This study proposed a practically applicable method to assess quantum noise in clinical images. The image-based measurement technique enables automatic quality control monitoring of image noise in clinical practice. Further, a phantom-based model can accurately predict quantum noise level in patient images. The prediction model can be used to quantitatively optimize individual protocol to achieve targeted noise level in clinical images.

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