# 3D Volumetric Super-Resolution in Radiology Using 3D RRDB-GAN

arXiv: https://arxiv.org/abs/2402.04171 · HTML: https://mlsanity.com/paper/2402.04171/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 21 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Builds on (most cited first, 11 of 11)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](https://mlsanity.com/paper/2107.10833/) (2021)
- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [A large annotated medical image dataset for the development and evaluation of segmentation algorithms](https://mlsanity.com/paper/1902.09063/) (2019)
- [Deeply-Recursive Convolutional Network for Image Super-Resolution](https://mlsanity.com/paper/1511.04491/) (2015)
- [Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks](https://mlsanity.com/paper/1801.02728/) (2018)

## Built on by (most cited first, 2 of 2)

- [Multi-resolution Guided 3D GANs for Medical Image Translation](https://mlsanity.com/paper/2412.00575/) (2024)
- [Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET](https://mlsanity.com/paper/2406.12632/) (2024)

## Abstract (arXiv)

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.