title: Wavelet denoising for quantum noise removal in chest digital tomosynthesis.
abstract: OBJECTIVE
  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.
authors.affiliation: School of Allied Health Sciences, Kitasato University, Kitasato, 1-15-1 Minami-ku, Sagamihara, Kanagawa, 252-0373, Japan, gomi@kitasato-u.ac.jp.
authors.forename: Tsutomu
authors.initials: T
authors.lastname: Gomi
authors.forename: Masahiro
authors.initials: M
authors.lastname: Nakajima
authors.forename: Tokuo
authors.initials: T
authors.lastname: Umeda
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D019047:Phantoms, Imaging; D013902:Radiography, Thoracic / Q000379:methods*; D059629:Signal-To-Noise Ratio
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