Gomi Tsutomu, Nakajima Masahiro, Umeda Tokuo
School of Allied Health Sciences, Kitasato University, Kitasato, 1-15-1 Minami-ku, Sagamihara, Kanagawa, 252-0373, Japan,
Int J Comput Assist Radiol Surg. 2015 Jan;10(1):75-86. doi: 10.1007/s11548-014-1003-2. Epub 2014 Apr 20.
Quantum noise impairs image quality in chest digital tomosynthesis (DT). A wavelet denoising processing algorithm for selectively removing quantum noise was developed and tested.
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.
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.
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.
量子噪声会损害胸部数字断层合成（DT）的图像质量。开发并测试了一种用于选择性去除量子噪声的小波去噪处理算法。
在DT系统上实施小波去噪技术，并通过包括空间分辨率在内的胸部体模测量进行实验评估。与Badea等人（《计算机医学成像与图形学》22:309 - 315，1998年）报道的现有重建后小波去噪处理算法进行比较。使用我们的技术（通过平衡稀疏度 - 范数方法进行重建前和重建后小波去噪处理）和现有的小波去噪处理算法，评估不同曝光下潜在的DT量子噪声降低情况。比较有无小波去噪处理时的对比度噪声比（CNR）、均方根误差（RMSE）等小波去噪处理算法。评估焦平面内的调制传递函数（MTF）。我们使用CNR和RMSE值进行了统计分析（多因素方差分析）。
我们的小波去噪处理算法显著降低了重建图像中的量子噪声并提高了对比度分辨率（CNR和RMSE：平衡稀疏度 - 范数小波去噪处理前与现有小波去噪处理比较，P<0.05；平衡稀疏度 - 范数小波去噪处理后与现有小波去噪处理比较，P<0.05；CNR：有小波去噪处理与无小波去噪处理比较，P<0.05）。结果表明，虽然MTF没有变化（从而保留了空间分辨率），但现有的小波去噪处理算法导致MTF恶化。
用于去除DT中量子噪声的平衡稀疏度 - 范数小波去噪处理算法被证明对具有高频分量特征的某些类型结构有效。当存在量子噪声时，这种去噪方法可能对胸部数字断层合成的各种临床应用有用。
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