论文标题
U-PET：基于MRI的痴呆检测，合成FDG-PET图像联合产生
U-PET: MRI-based Dementia Detection with Joint Generation of Synthetic FDG-PET Images
论文作者
论文摘要
阿尔茨海默氏病（AD）是痴呆症的最常见原因。早期检测对于减慢疾病并减轻与进展相关的风险至关重要。虽然MRI和FDG-PET的组合是诊断的最佳基于图像的工具，但FDG-PET并不总是可用。仅MRI对阿尔茨海默氏病的可靠检测可能是有益的，尤其是在FDG-PET可能对所有患者负担不起的地区。为此，我们提出了一种基于U-NET的多任务方法，该方法将T1加权MR图像作为产生合成FDG-PET图像的输入，并将患者的痴呆症进展分为认知正常（CN），认知障碍（MCI）和AD。两个任务头中使用的注意门可以可视化大脑中最相关的部分，指导检查员并增加可解释性。结果表明，合成FDG-PET图像的成功产生以及幼稚单任务基线的疾病分类的性能提高。
Alzheimer's disease (AD) is the most common cause of dementia. An early detection is crucial for slowing down the disease and mitigating risks related to the progression. While the combination of MRI and FDG-PET is the best image-based tool for diagnosis, FDG-PET is not always available. The reliable detection of Alzheimer's disease with only MRI could be beneficial, especially in regions where FDG-PET might not be affordable for all patients. To this end, we propose a multi-task method based on U-Net that takes T1-weighted MR images as an input to generate synthetic FDG-PET images and classifies the dementia progression of the patient into cognitive normal (CN), cognitive impairment (MCI), and AD. The attention gates used in both task heads can visualize the most relevant parts of the brain, guiding the examiner and adding interpretability. Results show the successful generation of synthetic FDG-PET images and a performance increase in disease classification over the naive single-task baseline.
