AgGAN: Anatomy-guided generative adversarial network to synthesize arterial spin labeling images for cerebral blood flow measurement under simulated microgravity

Abstract

Microgravity-induced alterations in cerebral blood flow (CBF) may contribute to cognitive decline and neurodegeneration in astronauts. Accurate CBF quantification under microgravity conditions is fundamental for maintaining astronaut health and ensuring the success of human space missions. Arterial spin labeling (ASL) perfusion magnetic resonance imaging (MRI) is currently the only non-invasive, non-radioactive technique to quantitatively assessing global and regional CBF. However, deploying MRI scanners aboard space station remains challenging due to technical, logistical and payload limitations. To address this challenge, we propose an end-to-end Anatomy-guided Generative Adversarial Network (AgGAN) as non-invasive, cost-effective, and accurate tool for estimating CBF by synthesizing ASL images under simulated microgravity conditions from corresponding baseline images. Specifically, inspired by radiologists’ diagnostic pattern, we develop a position-aware module to incorporate brain anatomical prior, and a region-adaptive feature extraction module to capture features of irregular brain regions. We also introduce a region-aware focal loss to enhance the synthesis quality of anatomically complex regions. Furthermore, we propose structure boundary-aware loss to encourage the synthesis network to learn boundary details, effectively avoiding exacerbation of partial volume effect and improving the accuracy of CBF quantification. Experimental results demonstrate the superiority of the proposed AgGAN in ASL image synthesis under simulated microgravity and show excellent subjective image quality evaluation. These findings highlight the potential of our model for CBF prediction in astronauts during spaceflight. Our dataset and code are available at https://github.com/Cai-Linkun/AgGAN.