用多分辨率引导的3D GAN实现高质量医学影像转换，提升临床应用潜力。
Multi-resolution Guided 3D GANs for Medical Image Translation
- 设计3D多分辨率注意力UNet生成器与判别器，结合体素级GAN损失和2.5D感知损失。
- 在多种模态、部位和年龄组上均实现高保真图像质量，体积评估指标表现优异。
- 验证合成数据在分割等下游任务中的实用性，适合医疗影像生成与临床落地场景。
医学影像转换旨在将一种成像模态转化为另一种，以减少患者重复扫描的需求，从而提高治疗效率并降低时间、设备与人力成本。本文提出一种基于3D多分辨率引导生成对抗网络（GAN）的医学影像转换框架。该框架采用3D多分辨率密集注意力UNet（3D-mDAUNet）作为生成器，3D多分辨率UNet作为判别器，并结合体素级GAN损失与2.5D感知损失进行优化。实验在多种成像模态、身体区域及年龄组上展示了出色的体积图像质量评估表现，证明了方法的鲁棒性。此外，引入合成到真实适用性评估，用于检验合成数据在分割等下游任务中的有效性。结果表明，该方法不仅能生成高质量合成医学图像，且具备潜在的临床应用价值。代码已开源：github.com/juhha/3D-mADUNet。
原文摘要 · Abstract (English)
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.
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