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

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Emmanuel Ahishakiye ◽  
Martin Bastiaan Van Gijzen ◽  
Julius Tumwiine ◽  
Johnes Obungoloch
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Image Reconstruction ◽  
Dictionary Learning ◽  
Resonance Imaging ◽  
Information Theoretic ◽  
Low Field ◽  
Space Data ◽  
Information Theoretic Criteria

scholarly journals Approaches for Image Reconstruction in Low-Field Magnetic Resonance Imaging

2021 ◽  
Author(s):  
Johnes Obungoloch ◽  
Emmanuel Ahishakiye
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Image Reconstruction ◽  
Data Driven ◽  
Resonance Imaging ◽  
Learning Methods ◽  
Image Reconstruction Techniques ◽  
Low Field ◽  
Low Field Mri ◽  
Magnetic Resonance Imaging Mri

Abstract Background: Magnetic Resonance Imaging (MRI) and spectroscopic techniques are frequently employed for clinical diagnostics as well as basic research in areas like cognitive neuroimaging. MRI is a widely used imaging modality for intracranial diseases. However, conventional MRI is expensive to purchase, maintain and sustain, limiting their use in low-income countries. Low field MRI can provide an economical, long-term, and safe imaging option to high-field MRI and computed tomography (CT) for brain imaging. This paper offers a review of the image reconstruction techniques used in low field magnetic resonance imaging (MRI). It is aimed at familiarizing the readers with the relevant knowledge, literature, and the latest updates on the state-of-art image reconstruction techniques that have been used in low field MRI citing their strengths, and areas for improvement. Methods: An in-depth keyword-based search was undertaken for publications on image reconstruction approaches in low-field MRI in the top scientific databases such as Google Scholar, Wiley, Science Direct, Springer, IEEE, Scopus, Nature, Elsevier, and PubMed throughout this study. This research also contained relevant postgraduate theses. For the selection of relevant research publications, the PRISMA flow diagram and protocol were also used.Results: Studies revealed that Inhomogeneities are present in low field MRI, implying that the traditional method of acquiring the image, using the inverse Fourier Transform, is no longer viable. The image reconstruction techniques reviewed include iterative methods, dictionary learning methods, and deep learning methods. Experimental results from the literature revealed improved image quality of the reconstructed images using data driven and learning based methods (deep learning and dictionary learning methods). Conclusion: The study revealed that there is limited literature on the image reconstruction approaches in low field MRI even if though there are sufficient studies on the subject in high field MRI. Data driven and learning based methods improves image reconstruction quality when compared to analytic and iterative approaches.

scholarly journals Effectiveness of low-field magnetic resonance imaging in diagnosing brachial plexus tumours in dogs – short communication

2015 ◽  
Vol 59 (2) ◽  
pp. 317-319
Author(s):  
Zbigniew Adamiak ◽  
Yauheni Zhalniarovich ◽  
Paulina Przyborowska ◽  
Joanna Głodek ◽  
Adam Przeworski
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Brachial Plexus ◽  
Resonance Imaging ◽  
Gadolinium Contrast ◽  
Gadolinium Contrast Agent ◽  
Low Field ◽  
Mri Sequences ◽  
Magnetic Resonance Imaging Mri ◽  
Brachial Plexus Tumours

AbstractThe aim of the study was to identify magnetic resonance imaging (MRI) sequences that contribute to a quick and reliable diagnosis of brachial plexus tumours in dogs. The tumours were successfully diagnosed in 6 dogs by the MRI with the use of SE, FSE, STIR, Turbo 3 D, 3D HYCE, and GE sequences and the gadolinium contrast agent

Sign in / Sign up

Export Citation Format

Share Document