Abstract：
A DnCNN for image denoising trained with natural images is available in MATLAB. For Tc-99m DMSA images, any loss of clinical details during the denoising process will have serious consequences since denoised image is to be used for diagnosis. The objective of the study was to find whether this pre-trained DnCNN can be used for denoising Tc-99m DMSA images and compare its performance with block matching 3D (BM3D) filter.
Two hundred forty-two Tc-99m DMSA images were denoised using BM3D filter (at sigma = 5, 10, 15, 20, and 25) and DnCNN. The original and denoised images were reviewed by two nuclear medicine physicians and also assessed objectively using the image quality metrics: SSIM, FSIM, MultiSSIM, PIQE, Blur, GCF, and Brightness. Wilcoxon signed-rank test was applied to find the statistically significant difference between the value of image quality metrics of the denoised images and the corresponding original images.
Nuclear medicine physicians observed no loss of clinical information in DnCNN denoised image and superior image quality compared to its original and BM3D denoised images. Edges/boundaries of the scar were found to be well preserved, and doubtful scar became obvious in the denoised image. Objective assessment also showed that the quality of DnCNN denoised images was significantly better than that of original images at P -value <0.0001.
The pre-trained DnCNN available with MATLAB Deep Learning Toolbox can be used for denoising Tc-99m DMSA images, and the performance of DnCNN was found to be superior in comparison with BM3D filter.
Two hundred forty-two Tc-99m DMSA images were denoised using BM3D filter (at sigma = 5, 10, 15, 20, and 25) and DnCNN. The original and denoised images were reviewed by two nuclear medicine physicians and also assessed objectively using the image quality metrics: SSIM, FSIM, MultiSSIM, PIQE, Blur, GCF, and Brightness. Wilcoxon signed-rank test was applied to find the statistically significant difference between the value of image quality metrics of the denoised images and the corresponding original images.
Nuclear medicine physicians observed no loss of clinical information in DnCNN denoised image and superior image quality compared to its original and BM3D denoised images. Edges/boundaries of the scar were found to be well preserved, and doubtful scar became obvious in the denoised image. Objective assessment also showed that the quality of DnCNN denoised images was significantly better than that of original images at P -value <0.0001.
The pre-trained DnCNN available with MATLAB Deep Learning Toolbox can be used for denoising Tc-99m DMSA images, and the performance of DnCNN was found to be superior in comparison with BM3D filter.
摘要：
MATLAB中提供了一个用自然图像进行图像去噪训练的DnCNN。对于Tc-99mDMSA图像，在去噪过程中任何临床细节的丢失都会产生严重的后果，因为去噪图像是用于诊断的。该研究的目的是确定该预训练的DnCNN是否可用于对Tc-99mDMSA图像进行去噪，并将其性能与块匹配3D（BM3D）滤波器进行比较。
使用BM3D滤波器（sigma=5、10、15、20和25）和DnCNN对242张Tc-99mDMSA图像进行了去噪。原始和去噪图像由两名核医学医师进行审查，并使用图像质量度量进行客观评估：SSIM，FSIM,MultiSSIM,PIQE,模糊，GCF,和亮度。应用Wilcoxon符号秩检验来发现去噪图像的图像质量度量值与相应的原始图像之间的统计上的显着差异。
核医学医师观察到，与原始图像和BM3D去噪图像相比，DnCNN去噪图像中的临床信息没有丢失，图像质量更高。疤痕的边缘/边界被发现保存完好，在去噪的图像中，可疑的疤痕变得明显。客观评价还表明，DnCNN去噪图像的质量在P值<0.0001时明显优于原始图像。
MATLABDeepLearningToolbox提供的预训练DnCNN可用于对Tc-99mDMSA图像进行去噪，并且发现DnCNN的性能优于BM3D滤波器。
使用BM3D滤波器（sigma=5、10、15、20和25）和DnCNN对242张Tc-99mDMSA图像进行了去噪。原始和去噪图像由两名核医学医师进行审查，并使用图像质量度量进行客观评估：SSIM，FSIM,MultiSSIM,PIQE,模糊，GCF,和亮度。应用Wilcoxon符号秩检验来发现去噪图像的图像质量度量值与相应的原始图像之间的统计上的显着差异。
核医学医师观察到，与原始图像和BM3D去噪图像相比，DnCNN去噪图像中的临床信息没有丢失，图像质量更高。疤痕的边缘/边界被发现保存完好，在去噪的图像中，可疑的疤痕变得明显。客观评价还表明，DnCNN去噪图像的质量在P值<0.0001时明显优于原始图像。
MATLABDeepLearningToolbox提供的预训练DnCNN可用于对Tc-99mDMSA图像进行去噪，并且发现DnCNN的性能优于BM3D滤波器。
