More Like this

1. Layer-based, depth-resolved computation of attenuation coefficients and backscattering fractions in tissue using optical coherence tomography

   https://doi.org/10.1364/BOE.427833  

   Cannon, Taylor M. ; Bouma, Brett E. ; Uribe-Patarroyo, Néstor ( July 2021 , Biomedical Optics Express)

   Structural optical coherence tomography (OCT) images of tissue stand to benefit from greater functionalization and quantitative interpretation. The OCT attenuation coefficientµ, an analogue of the imaged sample’s scattering coefficient, offers potential functional contrast based on the relationship ofµto sub-resolution physical properties of the sample. Attenuation coefficients are computed either by fitting a representativeµover several depth-wise pixels of a sample’s intensity decay, or by using previously-developed depth-resolved attenuation algorithms by Girardet al.[Invest. Ophthalmol. Vis. Sci.52,7738(2011).10.1167/iovs.10-6925] and Vermeeret al.[Biomed. Opt. Express5,322(2014).10.1364/BOE.5.000322]. However, the former method sacrifices axial information in the tomogram, while the latter relies on the stringent assumption that the sample’s backscattering fraction, another optical property, does not vary along depth. This assumption may be violated by layered tissues commonly observed in clinical imaging applications. Our approach preserves the full depth resolution of the attenuation map but removes its dependence on backscattering fraction by performing signal analysis inside individual discrete layers over which the scattering properties (e.g., attenuation and backscattering fraction) vary minimally. Although this approach necessitates the detection of these layers, it removes the constant-backscattering-fraction assumption that has constrained quantitative attenuation coefficient analysis in the past, and additionally yields a layer-resolved backscattering fraction, providing complementary scattering information to the attenuation coefficient. We validate our approach using automated layer detection in layered phantoms, for which the measured optical properties were in good agreement with theoretical values calculated with Mie theory, and show preliminary results in tissue alongside corresponding histological analysis. Together, accurate backscattering fraction and attenuation coefficient measurements enable the estimation of both particle density and size, which is not possible from attenuation measurements alone. We hope that this improvement to depth-resolved attenuation coefficient measurement, augmented by a layer-resolved backscattering fraction, will increase the diagnostic power of quantitative OCT imaging.

    

   more » « less
2. Spectral attenuation coefficients from measurements of light transmission in bare ice on the Greenland Ice Sheet

   https://doi.org/10.5194/tc-15-1931-2021  

   Cooper, Matthew G. ; Smith, Laurence C. ; Rennermalm, Asa K. ; Tedesco, Marco ; Muthyala, Rohi ; Leidman, Sasha Z. ; Moustafa, Samiah E. ; Fayne, Jessica V. ( January 2021 , The Cryosphere)

   Abstract. Light transmission into bare glacial ice affects surfaceenergy balance, biophotochemistry, and light detection and ranging (lidar)laser elevation measurements but has not previously been reported for theGreenland Ice Sheet. We present measurements of spectral transmittance at350–900 nm in bare glacial ice collected at a field site in the westernGreenland ablation zone (67.15∘ N, 50.02∘ W). Empirical irradianceattenuation coefficients at 350–750 nm are ∼ 0.9–8.0 m−1 for ice at 12–124 cm depth. The absorption minimum is at∼ 390–397 nm, in agreement with snow transmissionmeasurements in Antarctica and optical mapping of deep ice at the SouthPole. From 350–530 nm, our empirical attenuation coefficients are nearly1 order of magnitude larger than theoretical values for optically pureice. The estimated absorption coefficient at 400 nm suggests the ice volumecontained a light-absorbing particle concentration equivalent to∼ 1–2 parts per billion (ppb) of black carbon, which is similar topre-industrial values found in remote polar snow. The equivalent mineraldust concentration is ∼ 300–600 ppb, which is similar to values forNorthern Hemisphere warm periods with low aeolian activity inferred from icecores. For a layer of quasi-granular white ice (weathering crust)extending from the surface to ∼ 10 cm depth, attenuationcoefficients are 1.5 to 4 times larger than for deeper bubbly ice. Owing tohigher attenuation in this layer of near-surface granular ice, opticalpenetration depth at 532 nm is 14 cm (20 %) lower than asymptoticattenuation lengths for optically pure bubbly ice. In addition to thetraditional concept of light scattering on air bubbles, our results implythat the granular near-surface ice microstructure of weathering crust isan important control on radiative transfer in bare ice on the Greenland IceSheet ablation zone, and we provide new values of flux attenuation,absorption, and scattering coefficients to support model development andvalidation. 

   more » « less
3. Diffusion Equation-Assisted Markov Chain Monte Carlo Methods for the Inverse Radiative Transfer Equation

   https://doi.org/10.3390/e21030291  

   Li, Qin ; Newton, Kit ( March 2019 , Entropy)

   Optical tomography is the process of reconstructing the optical properties of biological tissue using measurements of incoming and outgoing light intensity at the tissue boundary. Mathematically, light propagation is modeled by the radiative transfer equation (RTE), and optical tomography amounts to reconstructing the scattering coefficient in the RTE using the boundary measurements. In the strong scattering regime, the RTE is asymptotically equivalent to the diffusion equation (DE), and the inverse problem becomes reconstructing the diffusion coefficient using Dirichlet and Neumann data on the boundary. We study this problem in the Bayesian framework, meaning that we examine the posterior distribution of the scattering coefficient after the measurements have been taken. However, sampling from this distribution is computationally expensive, since to evaluate each Markov Chain Monte Carlo (MCMC) sample, one needs to run the RTE solvers multiple times. We therefore propose the DE-assisted two-level MCMC technique, in which bad samples are filtered out using DE solvers that are significantly cheaper than RTE solvers. This allows us to make sampling from the RTE posterior distribution computationally feasible. 

   more » « less
4. Vertical profiles of light absorption and scattering associated with black carbon particle fractions in the springtime Arctic above 79° N

   https://doi.org/10.5194/acp-20-10545-2020  

   Leaitch, W. Richard ; Kodros, John K. ; Willis, Megan D. ; Hanna, Sarah ; Schulz, Hannes ; Andrews, Elisabeth ; Bozem, Heiko ; Burkart, Julia ; Hoor, Peter ; Kolonjari, Felicia ; et al ( January 2020 , Atmospheric Chemistry and Physics)

   null (Ed.)

   Abstract. Despite the potential importance of black carbon (BC) for radiative forcing of the Arctic atmosphere, vertically resolved measurements of the particle light scattering coefficient (σsp) and light absorption coefficient (σap) in the springtime Arctic atmosphere are infrequent, especially measurements at latitudes at or above 80∘ N. Here, relationships among vertically distributed aerosol optical properties (σap, σsp and single scattering albedo or SSA), particle microphysics and particle chemistry are examined for a region of the Canadian archipelago between 79.9 and 83.4∘ N from near the surface to 500 hPa. Airborne data collected during April 2015 are combined with ground-based observations from the observatory at Alert, Nunavut and simulations from the Goddard Earth Observing System (GEOS) model, GEOS-Chem, coupled with the TwO-Moment Aerosol Sectional (TOMAS) model (collectively GEOS-Chem–TOMAS; Kodros et al., 2018) to further our knowledge of the effects of BC on light absorption in the Arctic troposphere. The results are constrained for σsp less than 15 Mm−1, which represent 98 % of the observed σsp, because the single scattering albedo (SSA) has a tendency to be lower at lower σsp, resulting in a larger relative contribution to Arctic warming. At 18.4 m2 g−1, the average BC mass absorption coefficient (MAC) from the combined airborne and Alert observations is substantially higher than the two averaged modelled MAC values (13.6 and 9.1 m2 g−1) for two different internal mixing assumptions, the latter of which is based on previous observations. The higher observed MAC value may be explained by an underestimation of BC, the presence of small amounts of dust and/or possible differences in BC microphysics and morphologies between the observations and model. In comparing the observations and simulations, we present σap and SSA, as measured, and σap∕2 and the corresponding SSA to encompass the lower modelled MAC that is more consistent with accepted MAC values. Median values of the measured σap, rBC and the organic component of particles all increase by a factor of 1.8±0.1, going from near-surface to 750 hPa, and values higher than the surface persist to 600 hPa. Modelled BC, organics and σap agree with the near-surface measurements but do not reproduce the higher values observed between 900 and 600 hPa. The differences between modelled and observed optical properties follow the same trend as the differences between the modelled and observed concentrations of the carbonaceous components (black and organic). Model-observation discrepancies may be mostly due to the modelled ejection of biomass burning particles only into the boundary layer at the sources. For the assumption of the observed MAC value, the SSA range between 0.88 and 0.94, which is significantly lower than other recent estimates for the Arctic, in part reflecting the constraint of σsp<15 Mm−1. The large uncertainties in measuring optical properties and BC, and the large differences between measured and modelled values here and in the literature, argue for improved measurements of BC and light absorption by BC and more vertical profiles of aerosol chemistry, microphysics and other optical properties in the Arctic. 

   more » « less
5. Correcting for filter-based aerosol light absorption biases at the Atmospheric Radiation Measurement program's Southern Great Plains site using photoacoustic measurements and machine learning

   https://doi.org/10.5194/amt-15-4569-2022  

   Kumar, Joshin ; Paik, Theo ; Shetty, Nishit J. ; Sheridan, Patrick ; Aiken, Allison C. ; Dubey, Manvendra K. ; Chakrabarty, Rajan K. ( January 2022 , Atmospheric Measurement Techniques)

   Abstract. Measurement of light absorption of solar radiation byaerosols is vital for assessing direct aerosol radiative forcing, whichaffects local and global climate. Low-cost and easy-to-operate filter-basedinstruments, such as the Particle Soot Absorption Photometer (PSAP), that collect aerosols on a filter and measure light attenuation through thefilter are widely used to infer aerosol light absorption. However,filter-based absorption measurements are subject to artifacts that aredifficult to quantify. These artifacts are associated with the presence ofthe filter medium and the complex interactions between the filter fibers and accumulated aerosols. Various correction algorithms have been introduced to correct for the filter-based absorption coefficient measurements toward predicting the particle-phase absorption coefficient (Babs). However, the inability of these algorithms to incorporate into their formulations the complex matrix of influencing parameters such as particle asymmetry parameter, particle size, and particle penetration depth results in prediction of particle-phase absorption coefficients with relatively low accuracy. The analytical forms of corrections also suffer from a lack of universal applicability: different corrections are required for rural andurban sites across the world. In this study, we analyzed and compared 3 months of high-time-resolution ambient aerosol absorption data collectedsynchronously using a three-wavelength photoacoustic absorption spectrometer (PASS) and PSAP. Both instruments were operated on the same sampling inletat the Department of Energy's Atmospheric Radiation Measurement program's Southern Great Plains (SGP) user facility in Oklahoma. We implemented the two mostcommonly used analytical correction algorithms, namely, Virkkula (2010) and the average of Virkkula (2010) and Ogren (2010)–Bond et al. (1999) as well as a random forest regression (RFR) machine learning algorithm to predict Babs values from the PSAP's filter-based measurements. The predicted Babs was compared against the reference Babs measured by the PASS. The RFR algorithm performed the best by yielding the lowest root mean squareerror of prediction. The algorithm was trained using input datasets from the PSAP (transmission and uncorrected absorption coefficient), a co-locatednephelometer (scattering coefficients), and the Aerosol Chemical Speciation Monitor (mass concentration of non-refractory aerosol particles). A revisedform of the Virkkula (2010) algorithm suitable for the SGP site has beenproposed; however, its performance yields approximately 2-fold errors when compared to the RFR algorithm. To generalize the accuracy and applicabilityof our proposed RFR algorithm, we trained and tested it on a dataset oflaboratory measurements of combustion aerosols. Input variables to thealgorithm included the aerosol number size distribution from the Scanning Mobility Particle Sizer, absorption coefficients from the filter-basedTricolor Absorption Photometer, and scattering coefficients from amultiwavelength nephelometer. The RFR algorithm predicted Babs values within 5 % of the reference Babs measured by the multiwavelength PASS during the laboratory experiments. Thus, we show that machine learningapproaches offer a promising path to correct for biases in long-termfilter-based absorption datasets and accurately quantify their variabilityand trends needed for robust radiative forcing determination. 

   more » « less