A deep learning classification task for accurate brain navigation during functional ultrasound imaging

Abstract

Positioning and navigation are essential components of neuroimaging as they improve the quality and reliability of data acquisition, leading to advances in diagnosis, treatment outcomes, and fundamental understanding of the brain. Functional ultrasound (fUS) imaging is an emerging technology providing high-resolution images of the brain vasculature, allowing for the monitoring of brain activity. However, as the technology is relatively new, there is no standardized tool for inferring the position in the brain from the vascular images. This study presents a deep learning-based framework designed to address this challenge. Our approach uses an image classification task coupled with a regression on the resulting probabilities to determine the position of a single image. We conducted experiments using a dataset of 51 rat brain scans to evaluate its performance. The training positions were extracted at intervals of 375 {micro}m, resulting in a positioning error of 176 {micro}m. Further GradCAM analysis revealed that the predictions were primarily driven by subcortical vascular structures. Finally, we assessed the robustness of our method in a cortical stroke where the brain vasculature is severely impaired. Remarkably, no specific increase in the number of misclassifications was observed, confirming the methods reliability in challenging conditions. Overall, our framework provides accurate and flexible positioning, not relying on a pre-registered reference but on conserved vascular patterns.