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Wavelet denoising for quantum noise removal in chest digital tomosynthesis
Tsutomu Gomi1, Masahiro Nakajima, Tokuo Umeda
1School of Allied Health Sciences, Kitasato University, Kitasato, 1-15-1 Minami-ku, Sagamihara, Kanagawa, 252-0373, Japan, gomi@kitasato-u.ac.jp.
Purpose:
Quantum noise impairs image quality in chest digital tomosynthesis (DT). A wavelet denoising processing algorithm for selectively removing quantum noise was developed and tested.
Methods:
A wavelet denoising technique was implemented on a DT system and experimentally evaluated using chest phantom measurements including spatial resolution. Comparison was made with an existing post-reconstruction wavelet denoising processing algorithm reported by Badea et al. (Comput Med Imaging Graph 22:309-315, 1998). The potential DT quantum noise decrease was evaluated using different exposures with our technique (pre-reconstruction and post-reconstruction wavelet denoising processing via the balance sparsity-norm method) and the existing wavelet denoising processing algorithm. Wavelet denoising processing algorithms such as the contrast-to-noise ratio (CNR), root mean square error (RMSE) were compared with and without wavelet denoising processing. Modulation transfer functions (MTF) were evaluated for the in-focus plane. We performed a statistical analysis (multi-way analysis of variance) using the CNR and RMSE values.
Results:
Our wavelet denoising processing algorithm significantly decreased the quantum noise and improved the contrast resolution in the reconstructed images (CNR and RMSE: pre-balance sparsity-norm wavelet denoising processing versus existing wavelet denoising processing, P<0.05; post-balance sparsity-norm wavelet denoising processing versus existing wavelet denoising processing, P<0.05; CNR: with versus without wavelet denoising processing, P<0.05). The results showed that although MTF did not vary (thus preserving spatial resolution), the existing wavelet denoising processing algorithm caused MTF deterioration.
Conclusions:
A balance sparsity-norm wavelet denoising processing algorithm for removing quantum noise in DT was demonstrated to be effective for certain classes of structures with high-frequency component features. This denoising approach may be useful for a variety of clinical applications for chest digital tomosynthesis when quantum noise is present.
