Abstract
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 that is acquired. Reconstructions are obtained by iteratively updating the images using short-term conjugate gradient-type update formulas and Polak-Ribière update directions. We show that the algorithm can be used as an image reconstruction algorithm and as a denoising algorithm. We illustrate the performance of the algorithm on two-dimensional simulated low-field MR data that is corrupted by noise and on three-dimensional measured data obtained from a low-field MR scanner. Our reconstruction results show that the algorithm effectively suppresses noise and produces accurate reconstructions even for low-field MR signals with a low signal-to-noise ratio.
Highlights
In magnetic resonance imaging (MRI), the internal structure of the human body is visualized using magnetic fields
In this paper we present a magnetic resonance imaging (MRI) technique that is based on multiplicative reg ularization
We illustrate the performance of the algorithm on two-dimensional simulated low-field MR data that is corrupted by noise and on three-dimensional measured data obtained from a low-field MR scanner
Summary
In magnetic resonance imaging (MRI), the internal structure of the human body is visualized using magnetic fields. To form an image, commercial MR scanners employ strong magnetic background fields with field strengths ranging from 1.5 T to 7 T. Superconducting mag nets are used to generate such strong background fields, which ob viously adds to the cost, size, and infrastructure demands of high-field MR scanners. The overall costs of present day MR scanners are so high that they are essentially out of reach for low-income and middleincome countries. Compared with the commercial scanners mentioned above, a low-field scanner has a much weaker background field (typically in the centi- or millitesla range) and MR signal quality is reduced. A low-field scanner does not require any superconducting magnets and construction and maintenance costs are significantly lower (apart from additional cost reductions that may be achieved). For a review on low-field MRI, the reader is referred to [4]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
