  Denoising of magnetic resonance images is beneficial in improving the quality
of low signal-to-noise ratio images. Recently, denoising using deep neural
networks has demonstrated promising results. Most of these networks, however,
utilize supervised learning, which requires large training images of
noise-corrupted and clean image pairs. Obtaining training images, particularly
clean images, is expensive and time-consuming. Hence, methods such as
Noise2Noise (N2N) that require only pairs of noise-corrupted images have been
developed to reduce the burden of obtaining training datasets. In this study,
we propose a new self-supervised denoising method, Coil2Coil (C2C), that does
not require the acquisition of clean images or paired noise-corrupted images
for training. Instead, the method utilizes multichannel data from phased-array
coils to generate training images. First, it divides and combines multichannel
coil images into two images, one for input and the other for label. Then, they
are processed to impose noise independence and sensitivity normalization such
that they can be used for the training images of N2N. For inference, the method
inputs a coil-combined image (e.g., DICOM image), enabling a wide application
of the method. When evaluated using synthetic noise-added images, C2C shows the
best performance against several self-supervised methods, reporting comparable
outcomes to supervised methods. When testing the DICOM images, C2C successfully
denoised real noise without showing structure-dependent residuals in the error
maps. Because of the significant advantage of not requiring additional scans
for clean or paired images, the method can be easily utilized for various
clinical applications.
