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...

Image Reconstruction for a Handheld Low-Field MRI Scanner via Deep Learning

MRI has been used worldwide as an imaging modality for decades due to its ability to distinguish between soft tissues. However, MRIs cannot be used in all situations due to their extensive hard- and software. For this reason, research in the field of low-field MRI has been conducted. Low-field MRI systems do not use superconducting magnets but...

Image Reconstruction for Multicoil Low-field MRI: Reconstructing MR images based on incomplete multicoil data and object support information

Magnetic Resonance Imaging (MRI) is a non-invasive, non-ionizing imaging modality that is commonly used in the clinic today. However, it is an expensive technique. The high purchase, operational and maintenance costs, as well as the need for trained staff with technical expertise, put this technique out of reach for a large part of the world...

Low-field magnetic resonance imaging using multiplicative regularization

In this paper we present a magnetic resonance imaging (MRI) technique that is based on multiplicative regularization. Instead of adding a regularizing objective function to a data fidelity term, we multiply by such a regularizing function. By following this approach, no regularization parameter needs to be determined for each new data set...

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...

Conjugate gradient variants for ℓ _p -regularized image reconstruction in low-field MRI

We consider the MRI physics in a low-field MRI scanner, in which permanent magnets are used to generate a magnetic field in the millitesla range. A model describing the relationship between measured signal and image is derived, resulting in an ill-posed inverse problem. In order to solve it, a regularization penalty is added to the least...