Clinical Potential of a New Approach to MRI Acceleration
Autor: | Gigi Galiana, Nadine L. Dispenza, Maxim Zaitsev, Sebastian Littin, R. Todd Constable |
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Jazyk: | angličtina |
Rok vydání: | 2019 |
Předmět: |
0301 basic medicine
Multidisciplinary Phantoms Imaging Computer science Image quality lcsh:R MathematicsofComputing_NUMERICALANALYSIS Brain lcsh:Medicine Image processing Magnetic Resonance Imaging Article 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Undersampling Image Processing Computer-Assisted Humans lcsh:Q Parallel imaging lcsh:Science Algorithm 030217 neurology & neurosurgery |
Zdroj: | Scientific Reports, Vol 9, Iss 1, Pp 1-10 (2019) Scientific Reports |
ISSN: | 2045-2322 |
Popis: | Fast ROtary Nonlinear Spatial ACquisition (FRONSAC) was recently introduced as a new strategy that applies nonlinear gradients as a small perturbation to improve image quality in highly undersampled MRI. In addition to experimentally showing the previously simulated improvement to image quality, this work introduces the insight that Cartesian-FRONSAC retains many desirable features of Cartesian imaging. Cartesian-FRONSAC preserves the existing linear gradient waveforms of the Cartesian sequence while adding oscillating nonlinear gradient waveforms. Experiments show that performance is essentially identical to Cartesian imaging in terms of (1) resilience to experimental imperfections, like timing errors or off-resonance spins, (2) accommodating scan geometry changes without the need for recalibration or additional field mapping, (3) contrast generation, as in turbo spin echo. Despite these similarities to Cartesian imaging, which provides poor parallel imaging performance, Cartesian-FRONSAC consistently shows reduced undersampling artifacts and better response to advanced reconstruction techniques. A final experiment shows that hardware requirements are also flexible. Cartesian-FRONSAC improves accelerated imaging while retaining the robustness and flexibility critical to real clinical use. |
Databáze: | OpenAIRE |
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