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Local noise estimation in low-dose chest CT images

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

   Image noise in computed tomography (CT) images may have significant local variation due to tissue properties, dose, and location of the X-ray source. We developed and tested an automated tissue-based estimator method for estimating local noise in CT images.

Method

   An automated TBE method for estimating the local noise in CT image in 3 steps was developed: (1) Partition the image into homogeneous and transition regions, (2) For each pixel in the homogeneous regions, compute the standard deviation in a \(15\times 15\times 1\) voxel local region using only pixels from the same homogeneous region, and (3) Interpolate the noise estimate from the homogeneous regions in the transition regions. Noise-aware fat segmentation was implemented. Experiments were conducted on the anthropomorphic phantom and in vivo low-dose chest CT scans to validate the TBE, characterize the magnitude of local noise variation, and determine the sensitivity of noise estimates to the size of the region in which noise is computed. The TBE was tested on all scans from the Early Lung Cancer Action Program public database. The TBE was evaluated quantitatively on the phantom data and qualitatively on the in vivo data.

Results

   The results show that noise can vary locally by over 200 Hounsfield units on low-dose in vivo chest CT scans and that the TBE can characterize these noise variations within 5 %. The new fat segmentation algorithm successfully improved segmentation on all 50 scans tested.

Conclusion

   The TBE provides a means to estimate noise for image quality monitoring, optimization of denoising algorithms, and improvement of segmentation algorithms. The TBE was shown to accurately characterize the large local noise variations that occur due to changes in material, dose, and X-ray source location.

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Conflict of interest

Jennifer Padgett and Alberto Biancardi declare that they have no conflict of interest. Dr. David Yankelevitz is a named inventor on a number of patents and patent applications relating to the evaluation of diseases of the chest including measurement of nodules. Some of these, which are owned by Cornell Research Foundation (CRF), are non-exclusively licensed to General Electric. As an inventor of these patents, Dr. Yankelevitz is entitled to a share of any compensation which CRF may receive from its commercialization of these patents. Dr. Claudia I. Henschke is a named inventor on a number of patents and patent applications relating to the evaluation of pulmonary nodules on CT scans of the chest which are owned by Cornell Research Foundation (CRF). As of April 2009, Dr. Henschke signed away any financial benefit including royalties and any other proceeds related to the patents or patent applications owned by CRF. Dr. Henschke is the President of the Early Diagnosis and Treatment Research Foundation. Dr. Anthony Reeves’s financial and research disclosures are as follows: Financial (1) VisionGate, Inc.: Dr. Reeves is a paid consultant and holds stock in the company. VisionGate is developing optical imaging technology for the analysis of individual cells. (2) General Electric: Dr. Reeves is a co-inventor on a patent and other pending patents owned by Cornell Research Foundation (CRF) which are non-exclusively licensed and related to technology involving computer-aided diagnostic methods, including measurement of pulmonary nodules in CT images. (3) D4Vision Inc.: Dr. Reeves is the owner of D4Vision Inc. a company that licenses software for image analysis. Dr. Reeves receives research support in the form of grants and contracts from: NCI, American Legacy Foundation, Flight Attendants’ Medical Research Institute, AstraZeneca, Inc., GlaxoSmithKline and Carestream Health Inc.

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Authors and Affiliations

  1. School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA

    J. Padgett, A. M. Biancardi & A. P. Reeves

  2. Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    C. I. Henschke & D. Yankelevitz

Authors
  1. J. Padgett
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  2. A. M. Biancardi
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  3. C. I. Henschke
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  4. D. Yankelevitz
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  5. A. P. Reeves
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Corresponding author

Correspondence to J. Padgett.

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Padgett, J., Biancardi, A.M., Henschke, C.I. et al. Local noise estimation in low-dose chest CT images. Int J CARS 9, 221–229 (2014). https://doi.org/10.1007/s11548-013-0930-7

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  • DOI: https://doi.org/10.1007/s11548-013-0930-7

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