Optical coherence tomography (OCT) is a volumetric imaging technique that has been widely used to image the microstructure of biological samples.1 The OCT signal originates from back-scattered photons and provides image contrast that reflects intrinsic differences in tissue properties within the sample. The depth profile of the OCT signal is determined by five factors: the refraction index of the sample, the optical attenuation coefficient, the back-scattering coefficient, the numerical aperture (NA) of focusing optics, and the depth of the focus in the sample.2 The attenuation coefficient, i.e., the sum of the tissue scattering and absorption coefficients, is a spatially varying intrinsic property of the sample and is independent of the optics and the incident power of the system. For low-NA OCT systems, where the lateral resolution is typically larger than and the confocal parameter (i.e., two times the Rayleigh range) is comparable to or larger than the imaging depth, a single-scattering exponential model is appropriate to describe OCT signal attenuation versus depth.3,4 Previous studies have shown that extracting the attenuation coefficient from low-NA OCT measurements based on Beer’s law is useful in quantifying the optical properties of the sample for applications, including detecting cancerous tissue in skin, bladder, and brain,5 – 7 monitoring blood glucose concentration,8 characterizing atherosclerosis plaques3,9 and correlating collagen content with histological staining.10 However, for high-NA OCT systems where the lateral resolution reaches a few microns or less, the shape of the axial point spread function (PSF) plays an important role in modulating the OCT signal depth profile, and thus the effect of the confocal parameter and focus depth cannot be neglected when estimating the optical properties of the sample.4,11 We have previously shown that OCT with a lateral resolution is able to differentiate laminar structures in the neocortex of human brain samples using the average intensity projection (AIP) over the effective Rayleigh range.2,12 Moreover, we showed that estimating the intrinsic optical properties of the sample could provide additional information to differentiate brain structures, albeit with high inter-parameter correlations that resulted in large variance.2