Rationale and Objectives
To evaluate clinical feasibility and image quality of a comprehensive ultrafast brain
MRI protocol with multi-shot echo planar imaging and deep learning-enhanced reconstruction
at 1.5 T.
Materials and Methods
Thirty consecutive patients who underwent clinically indicated MRI at a 1.5 T scanner
were prospectively included. A conventional MRI (c-MRI) protocol, including T1-, T2-,
T2*-, T2-FLAIR, and diffusion-weighted images (DWI)-weighted sequences were acquired.
In addition, ultrafast brain imaging with deep learning-enhanced reconstruction and
multi-shot EPI (DLe-MRI) was performed. Subjective image quality was evaluated by
three readers using a 4-point Likert scale. To assess interrater agreement, Fleiss’
kappa (ϰ) was determined. For objective image analysis, relative signal intensity
levels for grey matter, white matter, and cerebrospinal fluid were calculated.
Results
Time of acquisition (TA) of c-MRI protocols added up to 13:55 minutes, whereas the
TA of DLe-MRI-based protocol added up to 3:04 minutes, resulting in a time reduction
of 78%. All DLe-MRI acquisitions yielded diagnostic image quality with good absolute
values for subjective image quality. C-MRI demonstrated slight advantages for DWI
in overall subjective image quality (c-MRI: 3.93 [+/− 0.25] vs DLe-MRI: 3.87 [+/−
0.37], P = .04) and diagnostic confidence (c-MRI: 3.93 [+/− 0.25] vs DLe-MRI: 3.83 [+/− 3.83],
P = .01). For most evaluated quality scores, moderate interobserver agreement was found.
Objective image evaluation revealed comparable results for both techniques.
Conclusion
DLe-MRI is feasible and allows for highly accelerated comprehensive brain MRI within
3 minutes at 1.5 T with good image quality. This technique may potentially strengthen
the role of MRI in neurological emergencies.
Keywords
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Article info
Publication history
Published online: May 19, 2023
Accepted:
April 17,
2023
Received in revised form:
April 14,
2023
Received:
February 27,
2023
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved All rights reserved.
