Three-Dimensional PET Imaging Reveals Canal-like Networks for Amyloid Beta Clearance to the Peripheral Lymphatic...

¹⁸F-Florbetapir PET imaging is widely used to assess amyloid-β (Aβ) burden in the brain, particularly in the context of Alzheimer's disease (AD). Conventional assessments typically rely on selected individual slices, which may limit spatial accuracy and are prone to image blurring. In the present study, we introduce novel techniques to enhance the spatial resolution and clarity of Aβ signal visualization in individuals pretreated with ¹⁸F-florbetapir. PET scans were retrospectively obtained from the Imaging and Data Archive for twelve individuals, including six cognitively unimpaired subjects and six diagnosed with AD. Each dataset consisted of 346 raw images, comprising 90 axial, 128 coronal, and 128 sagittal slices. Images were reconstructed into a single 3D volume using the 3D Slicer platform. Crucially, we applied artificial intelligence or AI-driven signal enhancement techniques to suppress background noise and amplify Aβ signals. This AI-enhanced processing improved image clarity and enabled visualization of subtle and spatially organized signal patterns. To verify anatomical location, Aβ PET signals were registered with MRI. This integrated workflow allowed us to visualize Aβ signals across regions of interest, including the brain parenchyma, skull, and cervical tissues. Our analytical approaches revealed that Aβ signals are highly concentrated and confined within non-CNS fluid compartments, forming canal-like networks that extend from the brain parenchyma toward the skull base, particularly the occipital clivus, and connect to the cervical lymph nodes. Additional Aβ signals were observed along the internal carotid plexus. These findings suggest that, when reconstruction in 3D and enhanced with AI, ¹⁸F-florbetapir PET imaging may not only reflect Aβ plaque burden in the brain but also visualize soluble Aβ species concentrated within anatomical clearance pathways leading to the peripheral lymphatic system. This approach offers a new dimension to PET signal interpretation and highlights the potential of AI-enhanced 3D in advancing neuroimaging analysis.