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Patient-specific image denoising for ultra-low-dose CT-guided lung biopsies

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

Abstract

Purpose

Low-dose CT screening of the lungs is becoming a reality, triggering many more CT-guided lung biopsies. During these biopsies, the patient is submitted to repeated guiding scans with substantial cumulated radiation dose. Extension of the dose reduction to the biopsy procedure is therefore necessary. We propose an image denoising algorithm that specifically addresses the setup of CT-guided lung biopsies. It minimizes radiation exposure while keeping the image quality appropriate for navigation to the target lesion.

Methods

A database of high-SNR CT patches is used to filter noisy pixels in a non-local means framework, while explicitly enforcing local spatial consistency in order to preserve fine image details and structures. The patch database may be created from a multi-patient set of high-SNR lung scans. Alternatively, the first scan, acquired at high-SNR right before the needle insertion, can provide a convenient patient-specific patch database.

Results

The proposed algorithm is compared to state-of-the-art denoising algorithms for a dataset of 43 real CT-guided biopsy scans. Ultra-low-dose scans were simulated by synthetic noise addition to the sinogram, equivalent to a 96% reduction in radiation dose. The feature similarity score for the proposed algorithm outperformed the compared methods for all the scans in the dataset. The benefit of the patient-specific patch database over the multi-patient one is demonstrated in terms of recovered contrast for a tiny porcine lung nodule, following denoising with both approaches.

Conclusions

The proposed method provides a promising approach to the denoising of ultra-low-dose CT-guided biopsy images.

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Authors and Affiliations

  1. Department of Electrical Engineering, Tel Aviv University, Ramat-Aviv, Israel

    Michael Green & Nahum Kiryati

  2. Diagnostic Imaging Institute, Sheba Medical Center, Affiliated with Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel

    Edith M. Marom, Eli Konen & Arnaldo Mayer

Authors
  1. Michael Green
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  2. Edith M. Marom
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  3. Eli Konen
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  4. Nahum Kiryati
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  5. Arnaldo Mayer
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Corresponding author

Correspondence to Arnaldo Mayer.

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The authors declare that they have no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Green, M., Marom, E.M., Konen, E. et al. Patient-specific image denoising for ultra-low-dose CT-guided lung biopsies. Int J CARS 12, 2145–2155 (2017). https://doi.org/10.1007/s11548-017-1621-6

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  • DOI: https://doi.org/10.1007/s11548-017-1621-6

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