Menghua Xia

artificial intelligence–generated content AIGC nuclear medicine imaging NMI hallucination Artificial intelligence–generated content (AIGC) has demonstrated significant potential in nuclear medicine imaging (NMI) over the past decade, achieving state-of-the-art performance across various tasks, based on a range of quantification metrics.

ATTRIBUTIONS AND MITIGATION RECOMMENDATIONS Most AIGC applications in NMI can be formulated as image-to-image estimation tasks, in which the objective is to learn a mapping function from a source domain S to a target domain T, denoted as . Given a training dataset with marginal distributions and , the goal is to identify an optimal approximation: , where is the loss function and H the hypothesis space (21).

This is an early access version, the complete PDF, HTML, and XML versions will be available soon. Open AccessArticle by Tongwaner Chen 1, Menghua Xia 1, Yi Huang 1, Jing Jiao 1 and Yuanyuan Wang 1,2,* 1 Department of Electronic Engineering, Fudan University, Shanghai 200433, China 2 Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention of Shanghai, Shanghai 200032, China * Author to whom correspondence should be addressed.