Ultrafast Brain MRI at 3 T for MS: Evaluation of a 51-Second Deep Learning-Enhanced T2-EPI-FLAIR Sequence

• Published in: Diagnostics (Basel) Vol. 14; no. 17; p. 1841
• Main Authors: Schuhholz, Martin, Ruff, Christer, Bürkle, Eva, Feiweier, Thorsten, Clifford, Bryan, Kowarik, Markus, Bender, Benjamin
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 23.08.2024
• Subjects: Artificial intelligence; Brain research; Consent; Deep learning; image acceleration; image enhancement; Machine learning; Magnetic resonance imaging; multi-shot EPI; Multiple sclerosis; Patients; ultrafast brain MRI; ultrafast FLAIR; deep learning; image enhancement; ultrafast FLAIR; multiple sclerosis; ultrafast brain MRI; inflammatory brain lesions; multi-shot EPI; image acceleration
• DOI: 10.3390/diagnostics14171841
• ISSN: 2075-4418

In neuroimaging, there is no equivalent alternative to magnetic resonance imaging (MRI). However, image acquisitions are generally time-consuming, which may limit utilization in some cases, e.g., in patients who cannot remain motionless for long or suffer from claustrophobia, or in the event of extensive waiting times. For multiple sclerosis (MS) patients, MRI plays a major role in drug therapy decision-making. The purpose of this study was to evaluate whether an ultrafast, T2-weighted (T2w), deep learning-enhanced (DL), echo-planar-imaging-based (EPI) fluid-attenuated inversion recovery (FLAIR) sequence (FLAIR ) that has targeted neurological emergencies so far might even be an option to detect MS lesions of the brain compared to conventional FLAIR sequences. Therefore, 17 MS patients were enrolled prospectively in this exploratory study. Standard MRI protocols and ultrafast acquisitions were conducted at 3 tesla (T), including three-dimensional (3D)-FLAIR, turbo/fast spin-echo (TSE)-FLAIR, and FLAIR . Inflammatory lesions were grouped by size and location. Lesion conspicuity and image quality were rated on an ordinal five-point Likert scale, and lesion detection rates were calculated. Statistical analyses were performed to compare results. Altogether, 568 different lesions were found. Data indicated no significant differences in lesion detection (sensitivity and positive predictive value [PPV]) between FLAIR and axially reconstructed 3D-FLAIR (lesion size ≥3 mm × ≥2 mm) and no differences in sensitivity between FLAIR and TSE-FLAIR (lesion size ≥3 mm total). Lesion conspicuity in FLAIR was similar in all brain regions except for superior conspicuity in the occipital lobe and inferior conspicuity in the central brain regions. Further findings include location-dependent limitations of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) as well as artifacts such as spatial distortions in FLAIR . In conclusion, FLAIR could potentially be an expedient alternative to conventional methods for brain imaging in MS patients since the acquisition can be performed in a fraction of time while maintaining good image quality.