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Identification of Descriptive Parameters of the Soil Pore Structure using Experiments and CT Data
(2010)
Quantitative PA tomography of high resolution 3-D images: experimental validation in tissue phantoms
(2020)
Quantitative photoacoustic tomography aims recover the spatial distribution of absolute chromophore concentrations and their ratios from deep tissue, high-resolution images. In this study, a model-based inversion scheme based on a Monte-Carlo light transport model is experimentally validated on 3-D multispectral images of a tissue phantom acquired using an all-optical scanner with a planar detection geometry. A calibrated absorber allowed scaling of the measured data during the inversion, while an acoustic correction method was employed to compensate the effects of limited view detection. Chromophore- and fluence-dependent step sizes and Adam optimization were implemented to achieve rapid convergence. High resolution 3-D maps of absolute concentrations and their ratios were recovered with high accuracy. Potential applications of this method include quantitative functional and molecular photoacoustic tomography of deep tissue in preclinical and clinical studies.
Quantitative photoacoustic tomography aims to recover maps of the local concentrations of tissue chromophores from multispectral images. While model-based inversion schemes are promising approaches, major challenges to their practical implementation include the unknown fluence distribution and the scale of the inverse problem. This paper describes an inversion scheme based on a radiance Monte Carlo model and an adjoint-assisted gradient optimization that incorporates fluence-dependent step sizes and adaptive moment estimation. The inversion is shown to recover absolute chromophore concentrations, blood oxygen saturation and the Grüneisen parameter from in silico 3D phantom images for different radiance approximations. The scattering coefficient was assumed to be homogeneous and known a priori.
Quantitative photoacoustic tomography aims to recover maps of the local concentrations of tissue chromophores from multispectral images. While model-based inversion schemes are promising approaches, major challenges to their practical implementation include the unknown fluence distribution and the scale of the inverse problem. This paper describes an inversion scheme based on a radiance Monte Carlo model and an adjoint-assisted gradient optimization that incorporates fluence-dependent step sizes and adaptive moment estimation. The inversion is shown to recover absolute chromophore concentrations, blood oxygen saturation and the Grüneisen parameter from in silico 3D phantom images for different radiance approximations. The scattering coefficient was assumed to be homogeneous and known a priori.
Quantitative PA tomography of high resolution 3-D images: experimental validation in tissue phantoms
(2019)
Quantitative photoacoustic tomography aims recover the spatial distribution of absolute chromophore concentrations and their ratios from deep tissue, high-resolution images. In this study, a model-based inversion scheme based on a Monte-Carlo light transport model is experimentally validated on 3-D multispectral images of a tissue phantom acquired using an all-optical scanner with a planar detection geometry. A calibrated absorber allowed scaling of the measured data during the inversion, while an acoustic correction method was employed to compensate the effects of limited view detection. Chromophore- and fluence-dependent step sizes and Adam optimization were implemented to achieve rapid convergence. High resolution 3-D maps of absolute concentrations and their ratios were recovered with high accuracy. Potential applications of this method include quantitative functional and molecular photoacoustic tomography of deep tissue in preclinical and clinical studies.
Quantitative photoacoustic tomography (qPAT) aims to extract physiological parameters, such as blood oxygen saturation (sO2), from measured multi-wavelength image data sets. The challenge of this approach lies in the inherently nonlinear fluence distribution in the tissue, which has to be accounted for by using an appropriate model, and the large scale of the inverse problem. In addition, the accuracy of experimental and scanner-specific parameters, such as the wavelength dependence of the incident fluence, the acoustic detector response, the beam profile and divergence, needs to be considered. This study aims at quantitative imaging of blood sO2, as it has been shown to be a more robust parameter compared to absolute concentrations. We propose a Monte-Carlo–based inversion scheme in conjunction with a reduction in the number of variables achieved using image segmentation. The inversion scheme is experimentally validated in tissue-mimicking phantoms consisting of polymer tubes suspended in a scattering liquid. The tubes were filled with chromophore solutions at different concentration ratios. 3-D multi-spectral image data sets were acquired using a Fabry-Perot based PA scanner. A quantitative comparison of the measured data with the output of the forward model is presented. Parameter estimates of chromophore concentration ratios were found to be within 5 % of the true values.
We present a level of detail method for trees based on ellipsoids and lines. We leverage the Expectation Maximization algorithm with a Gaussian Mixture Model to create a hierarchy of high-quality leaf clusterings, while the branches are simplified using agglomerative bottom-up clustering to preserve the connectivity. The simplification runs in a preprocessing step and requires no human interaction. For a fly by over and through a scene of 10k trees, our method renders on average at 40 ms/frame, up to 6 times faster than billboard clouds with comparable artifacts.
Neural circuit mapping is generating datasets of tens of thousands of labeled neurons. New computational tools are needed to search and organize these data. We present NBLAST, a sensitive and rapid algorithm, for measuring pairwise neuronal similarity. NBLAST considers both position and local geometry, decomposing neurons into short segments; matched segments are scored using a probabilistic scoring matrix defined by statistics of matches and non-matches. We validated NBLAST on a published dataset of 16,129 single Drosophila neurons. NBLAST can distinguish neuronal types down to the finest level (single identified neurons) without a priori information. Cluster analysis of extensively studied neuronal classes identified new types and unreported topographical features. Fully automated clustering organized the validation dataset into 1,052 clusters, many of which map onto previously described neuronal types. NBLAST supports additional query types, including searching neurons against transgene expression patterns. Finally, we show that NBLAST is effective with data from other invertebrates and zebrafish.