Non-Invasive Anatomical Level Cerebrovascular Imaging of Mice Using Diffusion Model-Enhanced Fluorescence Imaging

Abstract

In vivo fluorescence imaging, particularly indocyanine green (ICG)-based imaging, has gained traction for cerebrovascular imaging due to its real-time dynamics, free radiation, and accessibility. However, the presence of the scalp and skull significantly hampers imaging quality, often necessitating invasive procedures or biotoxic probes to achieve adequate depth and resolution. This limitation restricts the broader clinical/preclinical application of fluorescence imaging techniques. To address this, a novel approach is introduced that utilizes deep learning techniques to enhance ICG-based imaging, achieving high-resolution cerebrovascular imaging without invasive methods or biotoxic probes. By leveraging diffusion models, a connection between trans-scalp (TS) and trans-cranial (TC) ICG fluorescence images are establish in the latent space. This allows the transformation of blurred TS images into high-resolution images resembling TC images. Notably, intracerebral vascular structures and microvascular branches are unambiguously observed, achieving an anatomical resolution of 20.1 µm and a 1.7-fold improvement in spatial resolution. Validation also in a mouse model of middle cerebral artery occlusion demonstrates effective and sensitive identification of ischemic stroke sites. This advancement offers a non-invasive, cost-efficient alternative to current expensive imaging methods, paving the way for more advanced fluorescence imaging techniques.