Multi-resolution Guided 3D GANs for Medical Image Translation

TL;DR

This paper presents a multi-resolution 3D GAN framework for medical image translation, improving volumetric image quality and demonstrating potential clinical utility across various modalities and patient groups.

Contribution

Introduces a novel multi-resolution guided 3D GAN architecture with specialized loss functions for enhanced medical image translation.

Findings

Achieves high-quality synthetic images across multiple modalities.

Demonstrates robustness across different body regions and age groups.

Shows potential for clinical applications like segmentation.

Abstract

Medical image translation is the process of converting from one imaging modality to another, in order to reduce the need for multiple image acquisitions from the same patient. This can enhance the efficiency of treatment by reducing the time, equipment, and labor needed. In this paper, we introduce a multi-resolution guided Generative Adversarial Network (GAN)-based framework for 3D medical image translation. Our framework uses a 3D multi-resolution Dense-Attention UNet (3D-mDAUNet) as the generator and a 3D multi-resolution UNet as the discriminator, optimized with a unique combination of loss functions including voxel-wise GAN loss and 2.5D perception loss. Our approach yields promising results in volumetric image quality assessment (IQA) across a variety of imaging modalities, body regions, and age groups, demonstrating its robustness. Furthermore, we propose a synthetic-to-real applicability assessment as an additional evaluation to assess the effectiveness of synthetic data in downstream applications such as segmentation. This comprehensive evaluation shows that our method produces synthetic medical images not only of high-quality but also potentially useful in clinical applications. Our code is available at github.com/juhha/3D-mADUNet.