  Two-dimensional (2D) fast spin echo (FSE) techniques play a central role in
the clinical magnetic resonance imaging (MRI) of knee joints. Moreover,
three-dimensional (3D) FSE provides high-isotropic-resolution magnetic
resonance (MR) images of knee joints, but it has a reduced signal-to-noise
ratio compared to 2D FSE. Deep-learning denoising methods are a promising
approach for denoising MR images, but they are often trained using synthetic
noise due to challenges in obtaining true noise distributions for MR images. In
this study, inherent true noise information from 2-NEX acquisition was used to
develop a deep-learning model based on residual learning of convolutional
neural network (CNN), and this model was used to suppress the noise in 3D FSE
MR images of knee joints. The proposed CNN used two-step residual learning over
parallel transporting and residual blocks and was designed to comprehensively
learn real noise features from 2-NEX training data. The results of an ablation
study validated the network design. The new method achieved improved denoising
performance of 3D FSE knee MR images compared with current state-of-the-art
methods, based on the peak signal-to-noise ratio and structural similarity
index measure. The improved image quality after denoising using the new method
was verified by radiological evaluation. A deep CNN using the inherent
spatial-varying noise information in 2-NEX acquisitions was developed. This
method showed promise for clinical MRI assessments of the knee, and has
potential applications for the assessment of other anatomical structures.
