Ultra-low-dose CT image denoising using modified BM3D scheme tailored to data statistics

Abstract

Purpose: It is important to enhance image quality for low-dose CT acquisitions to push the ALARA boundary. Current state-of-the-art block-matching three-dimensional (BM3D) denoising scheme assumes white Gaussian noise (WGN) model. This study proposes a novel filtering module to be incorporated into the BM3D framework for ultra-low-dose CT denoising, by accounting for its specific power spectral properties.

Methods: In the current BM3D algorithm, the Wiener filtering is applied in the transform domain to a post-thresholding signal for enhanced denoising. However, unlike most natural/synthetic images, low-dose CTs do not obey the ideal Gaussian noise model. Based on the specific noise properties of ultra-low-dose CT, we derive the optimal transform-domain coefficients of Wiener filter based on the minimum mean-square-error (MMSE) criterion, taking the noise spectrum and the signal/noise cross spectrum into consideration. In the absence of ground-truth signal, the hard-thresholding denoising module in the previous stage is used as a plug-in estimator. We evaluate the denoising performance on thoracic CT image datasets containing paired full-dose and ultra-low-dose images simulated by a well-validated clinical engine (or pipeline). We also assess its clinical implication by applying the denoising methods to the emphysema quantification task. Our modified BM3D method is compared with the current one, using peak signal-to-noise ratio (PSNR) and emphysema scoring results as evaluation metrics.

Results: The noise in ultra-low-dose CT presented distinct non-Gaussian characteristics and was correlated with image intensity. Performance evaluation showed that the current Wiener filter in basic BM3D algorithm yielded little denoising enhancement on ultra-low-dose CT images. In contrast, the proposed Wiener filter achieved (1.46, 1.91) dB performance gain in mean and median peak signal-to-noise ratio (PSNR) for 5%-dose image denoising and (0.93, 0.95) dB improvement for 10% dose. A paired t-test of the PSNRs between denoising using the current and the proposed Wiener filters demonstrated statistically significant improvement, yielding P-values of 1.45E-12 and 1.34E-7 on 5% and 10%-dose images, respectively. In addition, emphysema quantification on the denoised images using the modified BM3D method also had statistically significant advantage over that using the current BM3D scheme, resulting in a P-value of 6.30E-5 with the commonly used measure.

Conclusions: This work tailors the Wiener filter in BM3D algorithm to data statistics and demonstrates statistically significant performance improvement on ultra-low-dose CT image denoising and a subsequent emphysema quantification task. Such performance gain is more pronounced with a lower dose level. The development and rationale are generally enough for other image denoising tasks when the WGN assumption is violated.

Keywords: BM3D; data statistics; low-dose CT.