用定量约束生成不同时期脑部影像，提升阿尔茨海默病预测准确率。
The Age-specific Alzheimer 's Disease Prediction with Characteristic Constraints in Nonuniform Time Span
- 基于量化指标引导序列图像生成，保持疾病特征连续性。
- 引入年龄缩放损失，使合成影像更贴近真实年龄阶段的病理变化。
- 适合关注神经退行性疾病早期预警与个性化诊疗的研究者。
阿尔茨海默病是一种以认知功能衰退为特征的严重神经退行性疾病，早期识别对制定个性化治疗策略至关重要。现有方法在输入影像采集时间间隔不一致时，难以准确表征疾病进展特征。本文提出一种受定量指标约束的序列图像生成方法，确保关键病理特征的保留；同时引入年龄缩放因子，生成具有年龄特异性的MRI图像，以支持晚期疾病的预测。消融实验表明，加入定量指标显著提升了MRI图像生成的准确性；采用年龄缩放像素损失后，迭代生成效果进一步优化。长期预测结果中，结构相似性指数达到0.882，表明合成图像与真实图像具有高度相似性。
原文摘要 · Abstract (English)
Alzheimer's disease is a debilitating disorder marked by a decline in cognitive function. Timely identification of the disease is essential for the development of personalized treatment strategies that aim to mitigate its progression. The application of generated images for the prediction of Alzheimer's disease poses challenges, particularly in accurately representing the disease's characteristics when input sequences are captured at irregular time intervals. This study presents an innovative methodology for sequential image generation, guided by quantitative metrics, to maintain the essential features indicative of disease progression. Furthermore, an age-scaling factor is integrated into the process to produce age-specific MRI images, facilitating the prediction of advanced stages of the disease. The results obtained from the ablation study suggest that the inclusion of quantitative metrics significantly improves the accuracy of MRI image synthesis. Furthermore, the application of age-scaled pixel loss contributed to the enhanced iterative generation of MRI images. In terms of long-term disease prognosis, the Structural Similarity Index reached a peak value of 0.882, indicating a substantial degree of similarity in the synthesized images.
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