In situ time-resolved motion of a tethered Pachnoda marginata, AI-correlated using μMRI and optical imaging

Microscopic magnetic resonance imaging (μMRI) is a versatile, non-invasive imaging modality and a potential candidate for studying the internal biomechanics associated with locomotion of small, freely behaving invertebrate model organisms. However, conventional μMRI acquisition is inherently sequential and relatively slow, limiting its ability to capture the locomotion and physiological dynamics of insects that occur on much faster timescales. In our previous work, we introduced an in situ spherical treadmill setup with integrated optical imaging system compatible with an ultra-high-field magnet, enabling motion-compensated imaging of tethered active insects. Here, we extend this platform by developing an accelerated μMRI acquisition scheme for dynamic insect biomechanics. As a proof-of-concept, we evaluate the feasibility of optimizing a RARE (fast spin-echo) sequence for imaging the relatively slow dynamics of a behaving sun beetle (Pachnoda marginata). The proposed method uses a dynamic undersampling strategy to accelerate acquisition, combined with retrospective view-sharing to partially fill k-space and exploit temporal redundancies. A deep learning module further refines the images by correcting the resultant undersampling artifacts. This integrated framework enables correlated time-resolved μMRI and optical imaging of a tethered active insect, broadening the capabilities of non-invasive biomechanical studies in small, behaving organisms.