Wavelet denoising for quantum noise removal in chest digital tomosynthesis
Wavelet denoising for quantum noise removal in chest digital tomosynthesis
复制标题
用于胸部数字断层合成中量子噪声消除的小波去噪
DOI：
10.1007/s11548-014-1003-2
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发表时间：
2015
影响因子：
3
通讯作者：
Tokuo Umeda
中科院分区：
文献类型：
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作者：
Tsutomu Gomi;Masahiro Nakajima;Tokuo Umeda
目的胸部数字断层合成（DT）中的量子噪声会影响图像质量。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。方法在DT系统上实现了小波去噪技术，并使用包括空间分辨率在内的胸部体模测量进行了实验评估。与Badea等人（Comput Med Imaging Graph 22：309-315，1998）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（通过平衡稀疏范数方法的预重建和后重建小波去噪处理）和现有的小波去噪处理算法，使用不同的曝光来评估潜在的DT量子噪声降低。比较了小波去噪处理前后对比度噪声比（CNR）、均方根误差（RMSE）等小波去噪处理算法。调制传递函数（MTF）进行了评估的焦平面。我们进行了统计分析结果本文提出的小波去噪处理算法明显降低了重建图像中的量子噪声，提高了重建图像的对比度分辨率（CNR和RMSE：预平衡稀疏范数小波去噪处理与现有小波去噪处理;后平衡稀疏范数小波去噪处理与现有小波去噪处理; CNR：有与没有小波去噪处理）。结果表明，虽然MTF没有变化（从而保持空间分辨率），现有的小波去噪处理算法造成MTF恶化。ConclusionsA平衡稀疏范数小波去噪处理算法，以消除量子噪声DT被证明是有效的某些类的结构与高频分量的功能。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。
PurposeQuantum noise impairs image quality in chest digital tomosynthesis (DT). A wavelet denoising processing algorithm for selectively removing quantum noise was developed and tested.MethodsA 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.ResultsOur 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,; post-balance sparsity-norm wavelet denoising processing versus existing wavelet denoising processing,; CNR: with versus without wavelet denoising processing,). The results showed that although MTF did not vary (thus preserving spatial resolution), the existing wavelet denoising processing algorithm caused MTF deterioration.ConclusionsA 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.