Denoising of Scintillation Camera Images Using a Deep Convolutional Neural Network: A Monte Carlo Simulation Approach
Denoising of Scintillation Camera Images Using a Deep Convolutional Neural Network: A Monte Carlo Simulation Approach
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DOI：
10.2967/jnumed.119.226613
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发表时间：
2020-02-01
影响因子：
9.3
通讯作者：
Tragardh, Elin
中科院分区：
文献类型：
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作者：
Minarik, David;Enqvist, Olof;Tragardh, Elin
闪烁相机图像包含大量的泊松噪声。我们研究了在全身骨骼扫描中是否可以使用卷积神经网络(CNN)来去除噪声，CNN是通过蒙特卡罗模拟获得的一组有噪声和无噪声的图像来训练的。方法：使用3组不同的训练图像生成3个CNN：模拟骨扫描图像、带有冷热斑点的圆柱形体模图像以及前两种图像的混合。每个训练集由40,000对无噪声和有噪声的图像对组成。用柱状体模的模拟图像和模拟的骨扫描图像对CNN进行评估。以滤波后的图像与真实图像的均方误差作为差值，用变异系数来评价降噪效果。将CNN与高斯滤波和中值滤波进行比较。进行了一项临床评估，将计数减半的CNN和高斯滤波骨扫描检测转移的能力与标准骨扫描进行了比较。结果：最好的CNN使变异系数平均降低了92%，最好的标准滤波器使变异系数平均降低了88%。对于柱状和骨骼扫描图像，最好的CNN给出的均方误差分别比最好的标准过滤器好68%和20%。柱状体模和骨扫描的最佳CNN是专用CNN。在标准骨扫描、CNN骨扫描和高斯滤波骨扫描中，发现转移的能力没有显著差异。结论：无论噪声水平如何，都能有效地去除噪声，分辨率损失很小或没有损失。CNN过滤器可以将扫描时间减少一半，同时仍能获得良好的骨转移评估精度。
Scintillation camera images contain a large amount of Poisson noise. We have investigated whether noise can be removed in whole-body bone scans using convolutional neural networks (CNNs) trained with sets of noisy and noiseless images obtained by Monte Carlo simulation. Methods: Three CNNs were generated using 3 different sets of training images: simulated bone scan images, images of a cylindric phantom with hot and cold spots, and a mix of the first two. Each training set consisted of 40,000 noiseless and noisy image pairs. The CNNs were evaluated with simulated images of a cylindric phantom and simulated bone scan images. The mean squared error between filtered and true images was used as difference metric, and the coefficient of variation was used to estimate noise reduction. The CNNs were compared with gaussian and median filters. A clinical evaluation was performed in which the ability to detect metastases for CNN- and gaussian-filtered bone scans with half the number of counts was compared with standard bone scans. Results: The best CNN reduced the coefficient of variation by, on average, 92%, and the best standard filter reduced the coefficient of variation by 88%. The best CNN gave a mean squared error that was on average 68% and 20% better than the best standard filters, for the cylindric and bone scan images, respectively. The best CNNs for the cylindric phantom and bone scans were the dedicated CNNs. No significant differences in the ability to detect metastases were found between standard, CNN-, and gaussian-filtered bone scans. Conclusion: Noise can be removed efficiently regardless of noise level with little or no resolution loss. The CNN filter enables reducing the scanning time by half and still obtaining good accuracy for bone metastasis assessment.