Deep learning-driven MRI for accurate brain volumetry in murine models of neurodegenerative diseases

Brain atrophy as assessed by magnetic resonance imaging (MRI) is a key measure of neurodegeneration and a predictor of disability progression in Alzheimer's disease and multiple sclerosis (MS) patients. While MRI-based brain volumetry is valuable for analyzing neurodegeneration in murine models as well, achieving high spatial resolution at sufficient signal-to-noise ratio is challenging due to the small size of the mouse brain. <i>In vivo</i> MRI allows for longitudinal studies and repeated assessments, enhancing statistical power and enabling pharmacological evaluations. However, the need for anesthesia necessitates compromises in acquisition times and voxel sizes. In this work we present the application of a deep-learning-based segmentation approach to the reliable quantification of total brain and brain sub region volumes, such as the <i>hippocampus</i>, <i>caudate putamen</i>, and <i>cerebellum</i>, from T₂-weighted images with a pixel volume of 78x78x250 μm³ acquired in 4.3 min at 7 Tesla using a conventional radiofrequency coil. The reproducibility of the fully automatic segmentation pipeline was validated in healthy C57BL/6 J mice and subsequently applied to models of amyotrophic lateral sclerosis, cuprizone-induced demyelination, and MS. Our approach offers a robust and efficient method for <i>in vivo</i> brain volumetry in preclinical mouse studies, facilitating the evaluation of neurodegenerative processes and therapeutic interventions. The dramatic reduction in acquisition time achieved with our AI-based approach significantly enhances animal welfare (3R). This advancement allows brain volumetry to be seamlessly integrated into additional analyses, providing comprehensive insights without substantially increasing study duration.