The present survey describes the state-of-the-art techniques for dynamic cardiac magnetic
resonance image reconstruction. Additionally, clinical relevance, main challenges, and …

This paper presents a framework for compressed sensing that bridges a gap between
existing theory and the current use of compressed sensing in many real-world applications …

Dynamic magnetic resonance imaging (MRI) is used in multiple clinical applications, but can
still benefit from higher spatial or temporal resolution. A dynamic MR image reconstruction …

We introduce and analyze a framework and corresponding method for compressed sensing
in infinite dimensions. This extends the existing theory from finite-dimensional vector spaces …

This paper introduces a framework for the reconstruction of magnetic resonance images in
the presence of rigid motion. The rationale behind our proposal is to make use of the partial …

Diffusion tensor histology holds great promise for quantitative characterization of structural
connectivity in mouse models of neurological and psychiatric conditions. There has been …

This paper demonstrates how new principles of compressed sensing, namely asymptotic
incoherence, asymptotic sparsity and multilevel sampling, can be utilised to better …

Purpose To enable rigid body motion‐tolerant parallel volumetric magnetic resonance
imaging by retrospective head motion correction on a variety of spatiotemporal scales and …

We study the qualitative properties of optimal regularisation parameters in variational
models for image restoration. The parameters are solutions of bilevel optimisation problems …

Many inverse problems in signal processing deal with the robust estimation of unknown data
from underdetermined linear observations. Low-dimensional models, when combined with …