Modelling of errors and uncertainties in photoacoustic tomography using a Bayesian framework

Photoacoustic tomography is studied in the framework of Bayesian inverse problems. Modelling of errors and uncertainties using Bayesian approximation error modelling is investigated. The approach is tested with simulation

Computationally Efficient Forward Operator for Photoacoustic Tomography Based on Coordinate Transformations

IEEE Photoacoustic tomography (PAT) is an imaging modality that utilizes the photoacoustic effect. In PAT, a photoacoustic image is computed from measured data by modeling ultrasound propagation in the imaged domain and solving an inverse problem utilizing a discrete forward operator. However, in realistic measurement geometries with several ultrasound transducers and relatively large imaging volume, an explicit formation and use of the forward operator can be computationally prohibitively expensive. In this work, we propose a transformation based approach for efficient modeling of photoacoustic signals and reconstruction of photoacoustic images. In the approach, the forward operator is constructed for a reference ultrasound transducer and expanded into a general measurement geometry using transformations that map the formulated forward operator in local coordinates to the global coordinates of the measurement geometry. The inverse problem is solved using a Bayesian framework. The approach is evaluated with numerical simulations and experimental data. The results show that the proposed approach produces accurate three-dimensional photoacoustic images with a significantly reduced computational cost both in memory requirements and in time. In the studied cases, depending on the computational factors such as discretization, over 30-fold reduction in memory consumption and was achieved without a reduction in image quality compared to a conventional approach

Fluorescence measurements show stronger cold inhibition of photosynthetic light reactions in Scots pine compared to Norway spruce as well as during spring compared to autumn

We studied the photosynthetic activity of Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies [L.] Karst) in relation to air temperature changes from March 2013 to February 2014. We measured the chlorophyll fluorescence of approximately 50 trees of each species growing in southern Finland. Fluorescence was measured 13 times per week. We began by measuring shoots present in late winter (i.e., March 2013) before including new shoots once they started to elongate in spring. By July, when the spring shoots had achieved similar fluorescence levels to the older ones, we proceeded to measure the new shoots only.We analysed the data by fitting a sigmoidal model containing four parameters to link sliding averages of temperature and fluorescence. A parameter defining the temperature range over which predicted fluorescence increased most rapidly was the most informative with in describing temperature dependence of fluorescence.The model generated similar fluorescence patterns for both species, but differences were observed for critical temperature and needle age. Down regulation of the light reaction was stronger in spring than in autumn. Pine showed more conservative control of the photosynthetic light reactions, which were activated later in spring and more readily attenuated in autumn. Under the assumption of a close correlation of fluorescence and photosynthesis, spruce should therefore benefit more than pine from the increased photosynthetic potential during warmer springs, but be more likely to suffer frost damage with a sudden cooling following a warm period. The winter of 20132014 was unusually mild and similar to future conditions predicted by global warming models. During the mild winter, the activity of photosynthetic light reactions of both conifers, especially spruce, remained high. Because light levels during winter are too low for photosynthesis, this activity may translate to a net carbon loss due to respiration

Utilising the radiative transfer equation in quantitative photoacoustic tomography

Quantitative photoacoustic tomography is an emerging imaging technique aimed at estimating optical parameters inside tissue from photoacoustic images. This optical parameter estimation problem is an ill-posed inverse problem, and thus it is sensitive to measurement and modelling errors. Therefore, light propagation in quantitative photoacoustic tomography needs to be accurately modelled. A widely accepted model for light propagation in biological tissue is the radiative transfer equation. In this work, the radiative transfer equation is utilised in quantitative photoacoustic tomography. Estimating absorption and scattering distributions in quantitative photoacoustic tomography using various illuminations is investigated

Substorm occurrence during quiet solar wind driving

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Characterization of ultrasound fields using a potential optical flow based synthetic schlieren tomography

Synthetic schlieren tomography is an optical imaging method for characterization of ultrasound fields based on observing bending of light due to acousto-optic effect. In this work, potential optical flow based pressure estimation method is introduced

Image reconstruction in quantitative photoacoustic tomography using adaptive optical Monte Carlo

In quantitative photoacoustic tomography (QPAT), distributions of optical parameters inside the target are reconstructed from photoacoustic images. In this work, we utilize the Monte Carlo (MC) method for light transport in the image reconstruction of QPAT. Modeling light transport accurately with the MC requires simulating a large number of photon packets, which can be computationally expensive. On the other hand, too low number of photon packets results in a high level of stochastic noise, which can lead to significant errors in reconstructed images. In this work, we use an adaptive approach, where the number of simulated photon packets is adjusted during an iterative image reconstruction. It is based on a norm test where the expected relative error of the minimization direction is controlled. The adaptive approach automatically determines the number of simulated photon packets to provide sufficiently accurate light transport modeling without unnecessary computational burden. The presented approach is studied with two-dimensional simulations

Estimation and uncertainty quantification of optical properties directly from the photoacoustic time series

Quantitative photoacoustic tomography seeks to estimate the optical parameters of a target given photoacoustic measurements as a data. Conventionally the problem is split into two steps: 1) the acoustical inverse problem of estimating the acoustic initial pressure distribution from the acoustical time series data; 2) the optical inverse problem of estimating the optical absorption and scattering from the initial pressure distributions. In this work, an approach for estimating the optical absorption and scattering directly from the acoustical time series is investigated with simulations. The work combines a homogeneous acoustical forward model, based on the Green's function solution of the wave equation, and a finite element method based diffusion approximation model of light propagation into a single forward model. This model maps the optical parameters of interest into a time domain signal. The model is used with a Bayesian approach to ill-posed inverse problems to form estimates of the posterior distributions for the parameters of interest. In addition to being able to provide point estimates of the parameters of interest, i.e. reconstruct the absorption and scattering distributions, the approach can be used to derive information on the uncertainty associated with the estimates