RESUMO
A major challenge in neuroscience is to visualize the structure of the human brain at different scales. Traditional histology reveals micro- and meso-scale brain features, but suffers from staining variability, tissue damage and distortion that impedes accurate 3D reconstructions. Here, we present a new 3D imaging framework that combines serial sectioning optical coherence tomography (S-OCT) with a deep-learning digital staining (DS) model. We develop a novel semi-supervised learning technique to facilitate DS model training on weakly paired images. The DS model performs translation from S-OCT to Gallyas silver staining. We demonstrate DS on various human cerebral cortex samples with consistent staining quality. Additionally, we show that DS enhances contrast across cortical layer boundaries. Furthermore, we showcase geometry-preserving 3D DS on cubic-centimeter tissue blocks and visualization of meso-scale vessel networks in the white matter. We believe that our technique offers the potential for high-throughput, multiscale imaging of brain tissues and may facilitate studies of brain structures.
RESUMO
BACKGROUND: Atherosclerosis is an arterial vessel wall disease characterized by slow, progressive lipid accumulation, smooth muscle disorganization, and inflammatory infiltration. Atherosclerosis often remains subclinical until extensive inflammatory injury promotes vulnerability of the atherosclerotic plaque to rupture with luminal thrombosis, which can cause the acute event of myocardial infarction or stroke. Current bioimaging techniques are unable to capture the pathognomonic distribution of cellular elements of the plaque and thus cannot accurately define its structural disorganization. METHODS: We applied cardiovascular magnetic resonance spectroscopy (CMRS) and diffusion weighted CMR (DWI) with generalized Q-space imaging (GQI) analysis to architecturally define features of atheroma and correlated these to the microscopic distribution of vascular smooth muscle cells (SMC), immune cells, extracellular matrix (ECM) fibers, thrombus, and cholesteryl esters (CE). We compared rabbits with normal chow diet and cholesterol-fed rabbits with endothelial balloon injury, which accelerates atherosclerosis and produces advanced rupture-prone plaques, in a well-validated rabbit model of human atherosclerosis. RESULTS: Our methods revealed new structural properties of advanced atherosclerosis incorporating SMC and lipid distributions. GQI with tractography portrayed the locations of these components across the atherosclerotic vessel wall and differentiated multi-level organization of normal, pro-inflammatory cellular phenotypes, or thrombus. Moreover, the locations of CE were differentiated from cellular constituents by their higher restrictive diffusion properties, which permitted chemical confirmation of CE by high field voxel-guided CMRS. CONCLUSIONS: GQI with tractography is a new method for atherosclerosis imaging that defines a pathological architectural signature for the atheromatous plaque composed of distributed SMC, ECM, inflammatory cells, and thrombus and lipid. This provides a detailed transmural map of normal and inflamed vessel walls in the setting of atherosclerosis that has not been previously achieved using traditional CMR techniques. Although this is an ex-vivo study, detection of micro and mesoscale level vascular destabilization as enabled by GQI with tractography could increase the accuracy of diagnosis and assessment of treatment outcomes in individuals with atherosclerosis.
