通过对抗扰动在基于模型的MRI重建中引发幻觉
Triggering hallucinations in model-based MRI reconstruction via adversarial perturbations
AI总结：
本研究通过对抗扰动揭示了基于模型的MRI重建中生成模型易受幻觉影响的脆弱性，并指出传统检测方法不足，需开发新方法应对
摘要：
生成模型越来越多地被用于提高医学影像的质量，例如磁共振图像和计算机断层扫描的重建。然而，众所周知，此类模型容易产生幻觉：它们可能会在重建的图像中插入实际上不存在于原始图像中的特征。在医疗环境中，这种幻觉可能会危及患者健康，因为它们可能导致错误的诊断。在本工作中，我们旨在量化最先进的生成模型在磁共振图像重建背景下产生幻觉的程度。具体而言，我们为未处理的输入图像设计了类似于随机噪声的对抗扰动，当使用生成模型进行重建时，这些扰动会引发幻觉。我们使用UNet和端到端VarNet架构在fastMRI数据集的脑部和膝关节图像上进行了此评估。我们的结果表明，这些模型对小扰动非常敏感，可以轻易被引导产生幻觉。这种脆弱性可能部分解释了幻觉为何会发生，并表明精心设计的对抗训练流程可能会减少其出现频率。此外，这些幻觉无法可靠地通过传统图像质量指标检测到。因此，需要开发新的方法来检测幻觉是否已经发生。
英文摘要：
Generative models are increasingly used to improve the quality of medical imaging, such as reconstruction of magnetic resonance images and computed tomography. However, it is well-known that such models are susceptible to hallucinations: they may insert features into the reconstructed image which are not actually present in the original image. In a medical setting, such hallucinations may endanger patient health as they can lead to incorrect diagnoses. In this work, we aim to quantify the extent to which state-of-the-art generative models suffer from hallucinations in the context of magnetic resonance image reconstruction. Specifically, we craft adversarial perturbations resembling random noise for the unprocessed input images which induce hallucinations when reconstructed using a generative model. We perform this evaluation on the brain and knee images from the fastMRI data set using UNet and end-to-end VarNet architectures to reconstruct the images. Our results show that these models are highly susceptible to small perturbations and can be easily coaxed into producing hallucinations. This fragility may partially explain why hallucinations occur in the first place and suggests that a carefully constructed adversarial training routine may reduce their prevalence. Moreover, these hallucinations cannot be reliably detected using traditional image quality metrics. Novel approaches will therefore need to be developed to detect when hallucinations have occurred.
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