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.

通过引入 3D Residual-in-Residual Dense Block GAN（3D RRDB-GAN）进行放射学图像的 3D 超分辨率研究，结合 2.5D 感知损失函数提升体积图像的质量和真实感，通过定量（如 LPIPS 和 FID）和定性评估展示了模型在详细图像分析方面的有效性，该模型在丰富医学图像的深度，清晰度和体积细节方面对医学成像做出了重要贡献，从全面的 3D 视角提高了复杂医学图像的解释和分析能力。