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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.
Summary
A new wavelet denoising algorithm effectively reduces quantum noise in chest digital tomosynthesis (DT) images. This method improves contrast resolution without compromising spatial resolution, offering potential clinical benefits.
Area of Science:
- Medical Imaging
- Digital Signal Processing
- Radiology
Background:
- Quantum noise degrades image quality in chest digital tomosynthesis (DT).
- Effective noise reduction is crucial for accurate diagnosis in DT imaging.
Purpose of the Study:
- To develop and evaluate a wavelet denoising processing algorithm for selectively removing quantum noise in chest DT.
- To assess the algorithm's impact on image quality metrics, including spatial resolution.
Main Methods:
- Implemented a novel pre- and post-reconstruction wavelet denoising technique (balance sparsity-norm method) on a DT system.
- Evaluated performance using chest phantom measurements, comparing with an existing algorithm.
- Assessed contrast-to-noise ratio (CNR), root mean square error (RMSE), and modulation transfer function (MTF).
Main Results:
- The proposed wavelet denoising algorithm significantly reduced quantum noise and improved contrast resolution (CNR, RMSE; P<0.05).
- Spatial resolution, evaluated by MTF, was preserved.
- The existing algorithm caused MTF deterioration, unlike the new method.
Conclusions:
- A balance sparsity-norm wavelet denoising algorithm effectively removes quantum noise in chest DT.
- This technique enhances image quality for clinical applications without sacrificing spatial resolution.
