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

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.