Currently working with inverse problems, modeling and deep learning in photoacoustic tomography.
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Teemu Sahlström is currently working as a junior researcher in University of Eastern Finland, Department of Applied Physics, Computational Physics and Inverse Problems group. His current research topics include photoacoustic tomography, inverse problems and acoustic modelling.
Photoacoustic tomography is an imaging modality based on the photoacoustic effect caused by the absorption of an externally introduced light pulse. In the inverse problem of photoacoustic tomography, the initial pressure generated through the photoacoustic effect is estimated from a measured photoacoustic time-series utilizing a forward model for u...
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...
There has been an increasing interest in utilizing machine learning methods in inverse problems and imaging. Most of the work has, however, concentrated on image reconstruction problems, and the number of studies regarding the full solution of the inverse problem is limited. In this work, we study a machine learning based approach for the Bayesian...
Photoacoustic tomography (PAT) is an imaging modality developed during the past few decades. In the inverseproblem of PAT, the aim is to estimate the spatial distribution of an initial pressurep0generated by thephotoacoustic effect, when photoacoustic time-seriesptmeasured on the boundary of the imaged target are given.To produce accurate photoacou...
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 simulations.