In this paper, we propose a method for denoising diffusion-weighted images (DWI) of the brain using a convolutional neural network trained on realistic, synthetic MR data. We compare our results to averaging of repeated scans, a widespread method used in clinics to improve signal-to-noise ratio of MR images. To obtain training data for transfer learning, we model, in a data-driven fashion, the effects of echo-planar imaging (EPI): Nyquist ghosting and ramp sampling. We introduce these effects to the digital phantom of brain anatomy (BrainWeb). Instead of simulating pseudo-random noise with a defined probability distribution, we perform noise scans with a brain-DWI-designed protocol to obtain realistic noise maps. We combine them with the simulated, noise-free EPI images. We also measure the Point Spread Function in a DW image of an AJR-approved geometrical phantom and inter-scan movement in a brain scan of a healthy volunteer. Their influence on image denoising and averaging of repeated images is investigated at different signal-to-noise ratio levels. Denoising performance is evaluated quantitatively using the simulated EPI images and qualitatively in real EPI DWI of the brain. We show that the application of our method allows for a significant reduction in scan time by lowering the number of repeated scans. Visual comparisons made in the acquired brain images indicate that the denoised single-repetition images are less noisy than multi-repetition averaged images. We also analyse the convolutional neural network denoiser and point out the challenges accompanying this denoising method.

翻译：在本文中,我们提出一种方法,用经过现实、合成的MR数据培训的进化神经网络,将大脑的传播加权图像(DWI)降级。我们比较我们的结果,以平均重复扫描,这是诊所用来改善MR图像的信号对噪音比例的广泛方法。为了获得用于转移学习的培训数据,我们以数据驱动的方式,模拟回声-平面成像(EPI)的影响:Nyquist 幽灵和斜坡取样。我们将这些影响引入大脑解剖的多直观图像(BrainWeb)的数字比对脑解剖(BrainWeb)的多直观图像(DWI)进行数字比较。我们不是模拟假随机的噪音,而是将重复的扫描结果与大脑-DWI设计的程序平均平均进行。我们将它们与模拟的无噪音 EPI 图像组合在一起,我们还用AJR 核准的测深光光光图和内部运动的大脑扫描中的变化数字。我们对重复图像的消化和平均图像的影响也表明,在模拟EPI的降压水平中,我们对这个图像的降压的图像的测算方法进行了测量。