  Inter-scanner and inter-protocol discrepancy in MRI datasets are known to
lead to significant quantification variability. Hence image-to-image or
scanner-to-scanner translation is a crucial frontier in the area of medical
image analysis with a lot of potential applications. Nonetheless, a significant
percentage of existing algorithms cannot explicitly exploit and preserve
texture details from target scanners and offers individual solutions towards
specialized task-specific architectures. In this paper, we design a multi-scale
texture transfer to enrich the reconstruction images with more details.
Specifically, after calculating textural similarity, the multi-scale texture
can adaptively transfer the texture information from target images or reference
images to restored images. Different from the pixel-wise matching space as done
by previous algorithms, we match texture features in a multi-scale scheme
implemented in the neural space. The matching mechanism can exploit multi-scale
neural transfer that encourages the model to grasp more semantic-related and
lesion-related priors from the target or reference images. We evaluate our
multi-scale texture GAN on three different tasks without any task-specific
modifications: cross-protocol super-resolution of diffusion MRI, T1-Flair, and
Flair-T2 modality translation. Our multi-texture GAN rehabilitates more
high-resolution structures (i.e., edges and anatomy), texture (i.e., contrast
and pixel intensities), and lesion information (i.e., tumor). The extensively
quantitative and qualitative experiments demonstrate that our method achieves
superior results in inter-protocol or inter-scanner translation over
state-of-the-art methods.
