Workshop on reconstruction schemes for magnetic resonance data: Summary of findings and recommendations

The high-fidelity reconstruction of compressed and lowresolution magnetic resonance (MR) data is essential for simultaneously improving patient care, accuracy in diagnosis and quality in clinical research. Sponsored by the Royal Society through the Newton Mobility Grant Scheme, we held a halfday workshop on reconstruction schemes for MR data...

An Energy-Efficient Multi-Sensor Compressed Sensing System Employing Time-Mode Signal Processing Techniques

This paper presents the design of an ultra-low energy, rakeness-based compressed sensing (CS) system that utilizes time-mode (TM) signal processing (TMSP). To realize TM CS operation, the presented implementation makes use of monostable multivibrator based analog-to-time converters, fixed-width pulse generators, basic digital gates and an...

Adaptive-size dictionary learning using information theoretic criteria for image reconstruction from undersampled k-space data in low field magnetic resonance imaging

Background: Magnetic resonance imaging (MRI) is a safe non-invasive and nonionizing medical imaging modality that is used to visualize the structure of human anatomy. Conventional (high-field) MRI scanners are very expensive to purchase, operate and maintain, which limit their use in many developing countries. This study is part of a project...

Inversion of incomplete spectral data using support information with an application to magnetic resonance imaging

In this paper we discuss an imaging method when the object has known support and its spatial Fourier transform is only known on a certain k-space undersampled pattern. The simple conjugate gradient least squares algorithm applied to the corresponding truncated Fourier transform equation produces reconstructions that are basically of a similar...

Data-driven methods for magnetic signatures

A severe incident with the cargo ship MSC Zoe, which lost hundreds of containers near the Dutch coast, shows how important it can be to detect and localise hidden objects at sea quickly. Also in defence applications localisation of hidden objects in water is important. If the objects are made of magnetic materials, they disturb the Earth...

A Compressed Sensing Algorithm for Magnetic Dipole Localization

This paper proposes an algorithm to localize a magnetic dipole using a limited number of noisy measurements from magnetic field sensors. The algorithm is based on the theory of compressed sensing, and exploits the sparseness of the magnetic dipole in space. Beforehand, a basis consisting of magnetic dipole fields belonging to individual...

State Estimation of Linear Systems With Sparse Inputs and Markov-Modulated Missing Outputs

In this paper, we consider the problem of estimating the states of a linear dynamical system whose inputs are jointly sparse and outputs at a few unknown time instants are missing. We model the missing data mechanism using a Markov chain with two states representing the missing and non-missing data. This mechanism with memory governed by the...

Elimination of traffic induced operational variability with Robust Principal Component Analysis (rPCA)

Regular maintenance of civil engineering structures is essential for their safety. Current maintenance regimes involve periodic inspections at regular time intervals. In the time between inspections, there can be a critical development in the structural integrity of a structure, which can be expensive to repair or could even lead to structural...

Optimally sparse and adaptive far-field sampling and pattern reconstruction: Approach for faster calibration of antenna system using compressed sensing

Radiation pattern measurements is a critical step in characterizing antennas before they are used in any system for a specific application. To identify any defects or to acquire the true radiation characteristics of the antenna under test, densely sampled measurements are desired. However, this results in a time and cost expensive measurement...

Sparse Millimeter Wave Channel Estimation From Partially Coherent Measurements

This project develops a channel estimation technique for millimeter wave (mmWave) communication systems. Our method exploits the sparse structure in mmWave channels for low training overhead and accounts for the phase errors in the channel measurements due to phase noise at the oscillator. Specifically, in IEEE 802.11ad/ay-based mmWave systems,...

Compressed Sensing in Low-Field MRI: Using Multiplicative Regularization

Magnetic resonance imaging (MRI) is a non-invasive tool to image the body’s anatomy and physiology, but suffers from long scan times. Compressed Sensing (CS) is used to accelerate MRI scans by incoherently taking fewer measurements and using a nonlinear optimization algorithm to image the undersampled data. Convex optimization techniques are...

The future of cardiovascular magnetic resonance: All-in-one vs. real-time (Part 1)

Cardiovascular magnetic resonance (CMR) protocols can be lengthy and complex, which has driven the research community to develop new technologies to make these protocols more efficient and patient-friendly. Two different approaches to improving CMR have been proposed, specifically “all-in-one” CMR, where several contrasts and/or motion states...