There is a significant interest in exploring non-linear associations among
multiple images derived from diverse imaging modalities. While there is a
growing literature on image-on-image regression to delineate predictive
inference of an image based on multiple images, existing approaches have
limitations in efficiently borrowing information between multiple imaging
modalities in the prediction of an image. Building on the literature of
Variational Auto Encoders (VAEs), this article proposes a novel approach,
referred to as Integrative Variational Autoencoder (\texttt{InVA}) method,
which borrows information from multiple images obtained from different sources
to draw predictive inference of an image. The proposed approach captures
complex non-linear association between the outcome image and input images,
while allowing rapid computation. Numerical results demonstrate substantial
advantages of \texttt{InVA} over VAEs, which typically do not allow borrowing
information between input images. The proposed framework offers highly accurate
predictive inferences for costly positron emission topography (PET) from
multiple measures of cortical structure in human brain scans readily available
from magnetic resonance imaging (MRI).

该研究论文介绍了一种名为 Integrative Variational Autoencoder（InVA）的方法，通过借用来自不同来源的多个图像的信息，捕捉了结果图像与输入图像之间的复杂非线性关联，并实现了快速计算，从而提供了在人脑磁共振成像（MRI）中从多个皮层结构测量中获得代价高昂的正电子发射断层摄影（PET）的高精度预测推断。