Simon R. Arridge

TL;DR: A new numerical method is proposed to reconstruct simultaneously the boundaries of the tissue regions together with the absorption and diffusion coefficients within these regions within a domain omega which is known to consist of a set of disjoint regions of distinct tissue types.

TL;DR: A Bayesian inference framework is suggested to estimate kinetic parameters (related to the extended Toft model) directly from undersampled (k, t)-space DCE MRI data to improve correspondence with the ground truth kinetic parameters.

TL;DR: The platform offers uncertainty estimation of any image derived statistic to facilitate robust tracking of subtle physiological changes in longitudinal studies and supports the development of new reconstruction and analysis algorithms through restricting the axial field of view to any set of rings covering a region of interest and thus performing fully 3D reconstruction and corrections using real data significantly faster.

TL;DR: In this paper, a 3D residual U-Net was trained using synthetic data, created from a library of 500 high-resolution balanced steady state free precession (WH-bSSFP) images by simulating 50% slice resolution and 50% phase resolution.

TL;DR: Fast three-dimensional localization of an absorbing object based on finite-element analysis reconstruction is demonstrated with experimental data and the improvement of spatial resolution is shown.