论文标题
用于有限的数据光声断层扫描的辛克图超分辨率和降解卷积神经网络（SRCN）
Sinogram super-resolution and denoising convolutional neural network (SRCN) for limited data photoacoustic tomography
论文作者
论文摘要
重建的光声图像的质量在很大程度上取决于可用的光声（PA）边界数据的数量，而光声（PA）边界数据又与所使用的检测器数量成正比。如果数据有限（由于成本/仪器限制而导致的检测器数量较少），则重建的PA图像遭受伪影，并且通常很吵。在这项工作中，第一次开发了基于深度学习的模型，以超级解决并降低光声辛图数据。将所提出的方法与现有最近的邻居插值和基于小波的denoising技术进行了比较，并显示出在数值和体内病例中的表现均优于它们。使用基于拟议的神经网络方法的超级分辨和降解的sinogragry数据，在均方根误差（RMSE）和峰信号与噪声比（PSNR）中获得的改进分别高达41.70％和6.93 DB，以利用有限的犯罪分子数据，该数据分别高达41.70％和6.93 DB。
The quality of the reconstructed photoacoustic image largely depends on the amount of photoacoustic (PA) boundary data available, which in turn is proportional to the number of detectors employed. In case of limited data (owing to less number of detectors due to cost/instrumentation constraints), the reconstructed PA images suffer from artifacts and are often noisy. In this work, for the first time, a deep learning based model was developed to super resolve and denoise the photoacoustic sinogram data. The proposed method was compared with existing nearest neighbor interpolation and wavelet based denoising techniques and was shown to outperform them both in numerical and in-vivo cases. The improvement obtained in Root Mean Square Error (RMSE) and Peak Signal to Noise Ratio (PSNR) for the reconstructed PA image using the sinogram data that was super-resolved and denoised using proposed neural network based method was as high as 41.70 % and 6.93 dB respectively compared to utilizing limited sinogram data.
