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...

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...

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...

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...