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International Journal of Computer Assisted Radiology and Surgery
Published in: International Journal of Computer Assisted Radiology and Surgery 12/2017

10-06-2017 | Original Article

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

Authors: Michael Green, Edith M. Marom, Eli Konen, Nahum Kiryati, Arnaldo Mayer

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 12/2017

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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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Metadata
Title
Patient-specific image denoising for ultra-low-dose CT-guided lung biopsies
Authors
Michael Green
Edith M. Marom
Eli Konen
Nahum Kiryati
Arnaldo Mayer
Publication date
10-06-2017
Publisher
Springer International Publishing
Published in
International Journal of Computer Assisted Radiology and Surgery / Issue 12/2017
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI
https://doi.org/10.1007/s11548-017-1621-6

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