Our study introduces a label-free imaging and quantitative analysis approach for investigating lipofuscin aggregates in human brain tissue. Leveraging the colocalization of lipofuscin with cell soma, our novel method accurately identifies and counts cells, especially large neurons. Achieving an impressive 92% accuracy at submicron resolution, our label-free approach outperforms the commonly used Nissl stain. We develop a robust segmentation technique for lipofuscin aggregates, revealing layered structures in the cortical gray matter, potentially associated with cell distribution. Furthermore, we validate our results using state-of-the-art techniques, including fluorescence lifetime imaging microscope and sub-micron resolution two photon imaging. Our findings contribute valuable insights into neurodegenerative diseases and hold promise for future diagnostic advancements.
Cerebral microvascular alterations are increasingly recognized as important contributors to neurodegenerative diseases, such as Alzheimer's Disease (AD) and chronic traumatic encephalopathy (CTE). To characterize these changes, we used a custom serial-sectioning, polarization-sensitive optical coherence tomography system to collect volumetric images of brain tissue volumes from the dorsolateral frontal lobe of normal controls and subjects with pathologically confirmed AD and CTE. We performed vasculature segmentation on each tissue volume and then analyzed the geometric properties of the vascular networks. The preliminary results suggest reduced microvascular density in AD compared to both CTE and NC.
Serial Sectioning Optical Coherence Tomography(serial sectioning OCT) has been widely used to investigate the structural and pathological features of brain samples. OCT is an optical imaging technique that provides both the 3D structure of the tissue as well as the optical properties including the scattering coefficient (μs) and back-scattering coefficient (μb). Serial sectioning OCT allows the reconstruction of distortion-free volumetric images at high contrast and high resolution, which has proven to be useful for the detection of cancerous tissue boundaries, visualizing 3D vascular structures and measuring neuron density. The tissue optical properties extracted from the OCT depth profile has been shown to be related to myelin content and neuron density. However, no quantitative correlation of the tissue optical properties with myelin content and neuron density has been reported. Establishing a quantitative relationship will potentially benefit the segmentation of anatomical layers and the characterization of demyelination and neuron loss, which are related to neurological diseases such as Alzheimer’s and Chronic Traumatic Encephalopathy(CTE). Here, we demonstrate using block-face imaging with optical coherence tomography (OCT) to quantitatively measure myelin content and neuron density in the human brain. By correlating the OCT measurements of tissue optical properties with the ground truth of myelin content and neuron density provided by histology, we found that the scattering coefficient possesses a linear relationship with the myelin content across different regions of the human brain, while the neuron density only slightly modulate the overall tissue scattering properties
The signal of optical coherence tomography (OCT) decays exponentially in depth due to tissue scattering, resulting in indistinct tissue features in three-dimension. Moreover, due to limited light penetration depth, extensive volumetric investigation is usually constrained for large-scale biological samples. By integrating serial sectioning technology with block-face imaging, we establish a volumetric OCT acquisition and reconstruction pipeline that incorporates depth-resolved attenuation coefficient estimation, volumetric stitching and filtering, and feature enhancement visualization. We demonstrate this pipeline on ex vivo human brain volumes of several cubic centimeters with 5 um isotropic resolution.
Significance: The optical properties of biological samples provide information about the structural characteristics of the tissue and any changes arising from pathological conditions. Optical coherence tomography (OCT) has proven to be capable of extracting tissue’s optical properties using a model that combines the exponential decay due to tissue scattering and the axial point spread function that arises from the confocal nature of the detection system, particularly for higher numerical aperture (NA) measurements. A weakness in estimating the optical properties is the inter-parameter cross-talk between tissue scattering and the confocal parameters defined by the Rayleigh range and the focus depth.
Aim: In this study, we develop a systematic method to improve the characterization of optical properties with high-NA OCT.
Approach: We developed a method that spatially parameterizes the confocal parameters in a previously established model for estimating the optical properties from the depth profiles of high-NA OCT.
Results: The proposed parametrization model was first evaluated on a set of intralipid phantoms and then validated using a low-NA objective in which cross-talk from the confocal parameters is negligible. We then utilize our spatially parameterized model to characterize optical property changes introduced by a tissue index matching process using a simple immersion agent, 2,2’-thiodiethonal.
Conclusions: Our approach improves the confidence of parameter estimation by reducing the degrees of freedom in the non-linear fitting model.
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