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

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

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)2145-2155
Number of pages11
JournalInternational journal of computer assisted radiology and surgery
Issue number12
StatePublished - 1 Dec 2017


  • CT-guided lung biopsy
  • Denoising
  • Low-dose CT
  • Non-local means

All Science Journal Classification (ASJC) codes

  • Surgery
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Health Informatics
  • Computer Graphics and Computer-Aided Design


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