This study considers a joint modeling framework for simultaneously examining the dynamic pattern of longitudinal and ultrahigh-dimensional images and their effects on the survival of interest. A functional mixed effects model is considered to describe the trajectories of longitudinal images.A high-dimensional functional principal component analysis (HD-FPCA) is adopted to extract the principal eigenimages to reduce the ultrahigh dimensionality of the imaging data. Finally, a Cox regression model is used to examine the effects of the longitudinal images and other covariates on the hazards of interest. A theoretical justification shows that a naive two-stage procedure that separately analyzes each part of the joint model produces biased estimation. We develop a Bayesian joint estimation method coupled with efficient Markov chain Monte Carlo sampling schemes to perform statistical inference for the proposed joint model. Moreover, a Monte Carlo dynamic prediction procedure is proposed to predict the survival probabilities of future subjects given their historical longitudinal images. The proposed method is assessed through simulation studies and applied to the study of Alzheimer's Disease Neuroimaging Initiative. New insights into the early diagnosis and prevention of Alzheimer's disease are obtained.