23 citations · 26 across the 6 of their papers we have counts for
6 papers
Score-based diffusion models for severely ill-posed problems in diffuse optical tomography
Fabian Schneider, Meghdoot Mozumder, Konstantin Tamarov +4
Score-based diffusion models are a recently developed framework for posterior sampling in Bayesian inverse problems, enabling high-quality reconstructions in inverse problems by le…
Utilising nanosecond sources in diffuse optical tomography
Meghdoot Mozumder, Jarkko Leskinen, Tanja Tarvainen
Diffuse optical tomography (DOT) use near-infrared light for imaging optical properties of biological tissues. Time-domain DOT systems use pulsed lasers and measure time-varying te…
Sparsity promoting reconstructions via hierarchical prior models in diffuse optical tomography
Anssi Manninen, Meghdoot Mozumder, Tanja Tarvainen +1
Diffuse optical tomography (DOT) is a severely ill-posed nonlinear inverse problem that seeks to estimate optical parameters from boundary measurements. In the Bayesian framework,…
A model-based iterative learning approach for diffuse optical tomography
Meghdoot Mozumder, Andreas Hauptmann, Ilkka Nissilä +2
Diffuse optical tomography (DOT) utilises near-infrared light for imaging spatially distributed optical parameters, typically the absorption and scattering coefficients. The image…
Perturbation Monte Carlo Method for Quantitative Photoacoustic Tomography
Aleksi Leino, Tuomas Lunttila, Meghdoot Mozumder +2
Quantitative photoacoustic tomography aims at estimating optical parameters from photoacoustic images that are formed utilizing the photoacoustic effect caused by the absorption of…
Approximate Marginalization of Absorption and Scattering in Fluorescence Diffuse Optical Tomography
Meghdoot Mozumder, Tanja Tarvainen, Simon Arridge +3
In fluorescence diffuse optical tomography (fDOT), the reconstruction of the fluorophore concentration inside the target body is usually carried out using a normalized Born approxi…