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arXiv 2108.02160eess.IVcs.CV

用于阿尔茨海默病多模态诊断的基于全局-局部感知GAN（GLA-GAN）的MRI到PET跨模态转换

MRI to PET Cross-Modality Translation using Globally and Locally Aware GAN (GLA-GAN) for Multi-Modal Diagnosis of Alzheimer's Disease

Indian Institute of Technology Ropar（印度理工学院罗帕尔分校）；Oregon State University（俄勒冈州立大学）

Apoorva Sikka, Skand Peri, Jitender Singh Virk, Usma Niyaz, Deepti R. Bathula

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24 pages
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AI总结：

针对医学影像数据限制深度学习效果的问题，提出融合多类损失的GLA-GAN实现从MR到FDG-PET的跨模态合成，提升了阿尔茨海默病诊断的临床效用。

摘要：

医学影像数据集固有维度高、变异性大且样本量小，限制了深度学习算法的有效性。近年来，生成式对抗网络（GANs）具备合成真实图像的能力，作为标准数据增强技术的替代方案展现出巨大潜力。本研究聚焦于利用生成模型从结构磁共振（MR）图像跨模态合成氟脱氧葡萄糖（FDG）正电子发射断层扫描（PET）图像，以助力阿尔茨海默病（AD）的多模态诊断。具体而言，我们提出了一种新颖的端到端、具备全局与局部感知能力的图像到图像转换GAN（GLA-GAN），其采用多路径架构，同时保证全局结构完整性与局部细节保真度。我们进一步在标准对抗损失的基础上，补充了体素级强度损失、多尺度结构相似性（MS-SSIM）损失以及基于感兴趣区域（ROI）的损失分量，三者分别用于降低重建误差、保证不同尺度下的结构一致性，以及捕捉AD相关区域敏感性的差异。实验结果表明，与现有最优模型相比，我们的GLA-GAN不仅能生成图像质量更高的合成FDG-PET扫描图像，还在提升AD诊断方面具有更优的临床效用。最后，我们尝试解读了GAN中与该特定跨模态生成任务密切相关的部分内部单元。

英文摘要：

Medical imaging datasets are inherently high dimensional with large variability and low sample sizes that limit the effectiveness of deep learning algorithms. Recently, generative adversarial networks (GANs) with the ability to synthesize realist images have shown great potential as an alternative to standard data augmentation techniques. Our work focuses on cross-modality synthesis of fluorodeoxyglucose~(FDG) Positron Emission Tomography~(PET) scans from structural Magnetic Resonance~(MR) images using generative models to facilitate multi-modal diagnosis of Alzheimer's disease (AD). Specifically, we propose a novel end-to-end, globally and locally aware image-to-image translation GAN (GLA-GAN) with a multi-path architecture that enforces both global structural integrity and fidelity to local details. We further supplement the standard adversarial loss with voxel-level intensity, multi-scale structural similarity (MS-SSIM) and region-of-interest (ROI) based loss components that reduce reconstruction error, enforce structural consistency at different scales and perceive variation in regional sensitivity to AD respectively. Experimental results demonstrate that our GLA-GAN not only generates synthesized FDG-PET scans with enhanced image quality but also superior clinical utility in improving AD diagnosis compared to state-of-the-art models. Finally, we attempt to interpret some of the internal units of the GAN that are closely related to this specific cross-modality generation task.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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