Diagnostic value of 3D-FLAIR magnetic resonance sequence in detection of white matter brain lesions in multiple sclerosis

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

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 (FLAIRUF) 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 FLAIRUF. 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 FLAIRUF and axially reconstructed 3D-FLAIR (lesion size ≥3 mm × ≥2 mm) and no differences in sensitivity between FLAIRUF and TSE-FLAIR (lesion size ≥3 mm total). Lesion conspicuity in FLAIRUF 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 FLAIRUF. In conclusion, FLAIRUF 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.

T1 and FLAIR signal intensities are related to tau pathology in dominantly inherited Alzheimer disease

Carriers of mutations responsible for dominantly inherited Alzheimer disease provide a unique opportunity to study potential imaging biomarkers. Biomarkers based on routinely acquired clinical MR images, could supplement the extant invasive or logistically challenging) biomarker studies. We used 1104 longitudinal MR, 324 amyloid beta, and 87 tau positron emission tomography imaging sessions from 525 participants enrolled in the Dominantly Inherited Alzheimer Network Observational Study to extract novel imaging metrics representing the mean (μ) and standard deviation (σ) of standardized image intensities of T1-weighted and Fluid attenuated inversion recovery (FLAIR) MR scans. There was an exponential decrease in FLAIR-μ in mutation carriers and an increase in FLAIR and T1 signal heterogeneity (T1-σ and FLAIR-σ) as participants approached the symptom onset in both supramarginal, the right postcentral and right superior temporal gyri as well as both caudate nuclei, putamina, thalami, and amygdalae. After controlling for the effect of regional atrophy, FLAIR-μ decreased and T1-σ and FLAIR-σ increased with increasing amyloid beta and tau deposition in numerous cortical regions. In symptomatic mutation carriers and independent of the effect of regional atrophy, tau pathology demonstrated a stronger relationship with image intensity metrics, compared with amyloid pathology. We propose novel MR imaging intensity-based metrics using standard clinical T1 and FLAIR images which strongly associates with the progression of pathology in dominantly inherited Alzheimer disease. We suggest that tau pathology may be a key driver of the observed changes in this cohort of patients.

Central vein sign: comparison of multiple sclerosis and leukoaraiosis

BACKGROUND

Leukoaraiosis produces white matter lesions (WML) similar to multiple sclerosis (MS) on brain magnetic resonance imaging (MRI), and the distinction between these two conditions is difficult radiologically. This study aimed to investigate the role of the central vein sign (CVS) in susceptibility-weighted imaging (SWI) sequence in distinguishing MS lesions from leukoaraiosis lesions in Turkish population.

METHODS

In this prospective study, axial SWI and sagittal three-dimensional fluid-attenuated inversion recovery (3DFLAIR) were obtained in 374 consecutive patients. The study consisted of 169 (89 MS patients, 80 patients with leukoaraiosis) patients according to the inclusion and exclusion criteria. Two observers evaluated MR images by consensus, and observers were unaware of the patient's clinical findings. Locations (periventricular, juxtacortical, and deep white matter) and the presence of CVS were investigated for each of the lesions. Differences between patients in the leukoaraiosis and MS groups were investigated using the Mann-Whitney U test or chi-square analysis. In addition, receiver operating characteristic (ROC) analysis was used to analyze the diagnostic performance of CVS.

RESULTS

A total of 1908 WMLs (1265 MS lesions, 643 leukoaraiosis) were detected in 169 patients. The CVS was significantly higher in the MS lesions (p < 0.001). The CVS positivity rate in periventricular WMLs was higher than in juxtacortical WMLs or deep WMLs, both for all patients and for patients with MS (p < 0.001). The area under the curve (AUC) of the ROC analysis was 0.88 (95% confidence interval 0.83-0.93) for CVS in the distinction of MS lesions and leukoaraiosis.

DISCUSSION

The presence of CVS in the SWI sequence can be used as an auxiliary finding for the diagnosis of MS in the differentiation of MS and leukoaraiosis lesions.

Predictive MRI Biomarkers in MS—A Critical Review

Background and Objectives: In this critical review, we explore the potential use of MRI measurements as prognostic biomarkers in multiple sclerosis (MS) patients, for both conventional measurements and more novel techniques such as magnetization transfer, diffusion tensor, and proton spectroscopy MRI. Materials and Methods: All authors individually and comprehensively reviewed each of the aspects listed below in PubMed, Medline, and Google Scholar. Results: There are numerous MRI metrics that have been proven by clinical studies to hold important prognostic value for MS patients, most of which can be readily obtained from standard 1.5T MRI scans. Conclusions: While some of these parameters have passed the test of time and seem to be associated with a reliable predictive power, some are still better interpreted with caution. We hope this will serve as a reminder of how vast a resource we have on our hands in this versatile tool—it is up to us to make use of it.

Clinical correlates of white matter lesions in Parkinson's disease using automated multi-modal segmentation measures

BACKGROUND

Age-related white matter lesions (WML) are common, impact neuronal connectivity, and affect motor function and cognition. In addition to pathological nigrostriatal losses, WML are also common co-morbidities in Parkinson's disease (PD) that affect postural stability and gait. Automated brain volume measures are increasingly incorporated into the clinical reporting workflow to facilitate precision in medicine. Recently, multi-modal segmentation algorithms have been developed to overcome challenges with WML quantification based on single-modality input.

OBJECTIVE

We evaluated WML volumes and their distribution in a case-control cohort of PD patients to predict the domain-specific clinical severity using a fully automated multi-modal segmentation algorithm.

METHODS

Fifty-five subjects comprising of twenty PD patients and thirty-five age- and gender-matched control subjects underwent standardized motor/gait and cognitive assessments and brain MRI. Spatially differentiated WML obtained using automated segmentation algorithms on multi-modal MPRAGE and FLAIR images were used to predict domain-specific clinical severity. Preliminary statistical analysis focused on describing the relationship between WML and clinical scores, and the distribution of WML by brain regions. Subsequent stepwise regressions were performed to predict each clinical score using WML volumes in different brain regions, while controlling for age.

RESULTS

WML volume strongly correlates with both motor and cognitive dysfunctions in PD patients (p < 0.05), with differential impact in the frontal lobe and periventricular regions on cognitive domains (p < 0.01) and severity of motor deficits (p < 0.01), respectively.

CONCLUSION

Automated multi-modal segmentation algorithms may facilitate precision medicine through regional WML load quantification, which show potential as imaging biomarkers for predicting domain-specific disease severity in PD.