Inverse problems deal with the quest for unknown causes of observed consequences,
based on predictive models, known as the forward models, that associate the former …

The subject of inverse problems in differential equations is of enormous practical
importance, and has also generated substantial mathematical and computational …

Electrical impedance tomography (EIT) is developed to investigate the internal conductivity
changes of an object through a series of boundary electrodes, and has become increasingly …

Reliable EEG source analysis depends on sufficiently detailed and accurate head models.
In this study, we investigate how uncertainties inherent to the experimentally determined …

Solving inverse problems with sparsity promoting regularizing penalties can be recast in the
Bayesian framework as finding a maximum a posteriori (MAP) estimate with sparsity …

Sparse recovery seeks to estimate the support and the non-zero entries of a sparse signal
from possibly incomplete noisy observations, with,. It has been shown that under various …

Purpose To estimate multiple components within a single voxel in magnetic resonance
fingerprinting when the number and types of tissues comprising the voxel are not known a …

This study concerns reconstructing brain activity at various depths based on non-invasive
EEG (electroencephalography) scalp measurements. We aimed at demonstrating the …

Fifteen years ago, when the idea of using probability to model unknown parameters to be
estimated computationally was a less commonly accepted idea than it is today, writing a …

The estimation of the activity-related ion currents by measuring the induced electromagnetic
fields at the head surface is a challenging and severely ill-posed inverse problem. This is …