3D Volumetric Super-Resolution in Radiology Using 3D RRDB-GAN
Juhyung Ha Nian Wang Surendra Maharjan Xuhong Zhang
This study introduces the 3D Residual-in-Residual Dense Block GAN (3D
RRDB-GAN) for 3D super-resolution for radiology imagery. A key aspect of 3D
RRDB-GAN is the integration of a 2.5D perceptual loss function, which
contributes to improved volumetric image quality and realism. The effectiveness
of our model was evaluated through 4x super-resolution experiments across
diverse datasets, including Mice Brain MRH, OASIS, HCP1200, and MSD-Task-6.
These evaluations, encompassing both quantitative metrics like LPIPS and FID
and qualitative assessments through sample visualizations, demonstrate the
models effectiveness in detailed image analysis. The 3D RRDB-GAN offers a
significant contribution to medical imaging, particularly by enriching the
depth, clarity, and volumetric detail of medical images. Its application shows
promise in enhancing the interpretation and analysis of complex medical imagery
from a comprehensive 3D perspective.