Three-dimensional MRI sequences in MS diagnosis and research

Hu, Leo; Rajendran, Luckshi; Lapointe, Elvy; Tam, Roger; Li, David; Traboulsee, Anthony; Rauscher, Alexander

doi:10.1177/1352458519848100

Cited by 11 publications

(7 citation statements)

References 90 publications

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“…Each subject in this study underwent a specialized brain imaging protocol with the two main sequences specific for this study being 3D T1W SPGR and 3D T2 FLAIR; both were later used to quantify the volumes of deep grey matter structures and white matter lesions, respectively. This is consistent with the study by Hu et al [30], stating that 3D MRI sequences are the most commonly used scans for measuring brain volumes and that 3D versions of MRI scans for MS will continue to replace their 2D counterparts, as the 3D scans have a more superior image quality and provide more information.…”

Section: Discussionsupporting

confidence: 90%

Automated quantification of deep grey matter structures and white matter lesions using magnetic resonance imaging in relapsing remission multiple sclerosis

Rizkallah

Hefida

Khalil

et al. 2021

Egypt J Radiol Nucl Med

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Background Brain volume loss (BVL) is widespread in MS and occurs throughout the disease course at a rate considerably greater than in the general population. In MS, brain volume correlates with and predicts future disability, making BVL a relevant measure of diffuse CNS damage leading to clinical disease progression, as well as serving as a useful outcome in evaluating MS therapies. The aim of our study was to evaluate the role of automated segmentation and quantification of deep grey matter structures and white matter lesions in Relapsing Remitting Multiple Sclerosis patients using MR images and to correlate the volumetric results with different degrees of disability based on expanded disability status scale (EDSS) scores. Results All the patients in our study showed relative atrophy of the thalamus and the putamen bilaterally when compared with the normal control group. Statistical analysis was significant for the thalamus and the putamen atrophy (P value < 0.05). On the other hand, statistical analysis was not significant for the caudate and the hippocampus (P value > 0.05); there was a significant positive correlation between the white matter lesions volume and EDSS scores (correlation coefficient of 0.7505). On the other hand, there was a significant negative correlation between the thalamus and putamen volumes, and EDSS scores (correlation coefficients < − 0.9), while the volumes of the caudate and the hippocampus had a very weak and non-significant correlation with the EDSS scores (correlation coefficients > − 0.35). Conclusions The automated segmentation and quantification tools have a great role in the assessment of brain structural changes in RRMS patients, and that it became essential to integrate these tools in the daily medical practice for the great value they add to the current evaluation measures.

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Section: Discussionsupporting

confidence: 90%

Automated quantification of deep grey matter structures and white matter lesions using magnetic resonance imaging in relapsing remission multiple sclerosis

Rizkallah

Hefida

Khalil

et al. 2021

Egypt J Radiol Nucl Med

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“…FTT Average Dominant (median, [range]) † 41 [15,59] 31 [18,42] 27 [15,47] FTT Average Non-Dominant (median, [range]) † 39 [15,54] 26 [12,45] 18 [6,39] MOT Mean Latency (median, [range]) Advance PET tomography (N = 15 due to two participants not consenting to PET scanning). Two PiB-PET data sets were further excluded from subsequent analysis due to low tracer dose or motion artifact (final PET N = 13; 4:5:4 AD:MixD:SVaD).…”

Section: Image Acquisitionmentioning

confidence: 99%

“…MRI Processing -QSM and R2* SWI images were processed using in-house software to produce maps of QSM as well as R2*, which is a related measure that is correlated with iron in deep GM and myelin and iron in WM. 54,55 QSM was reconstructed using a rapid two-step dipole inversion algorithm. 55 Maps of R2* relaxation rates were computed by fitting a mono-exponential decay function to the multi-echo data 56 after correction for signal decay due to background inhomogeneities.…”

Section: Mri Processing -Preprocessing Of T1-weighted Imagesmentioning

confidence: 99%

Brain Imaging Abnormalities in Mixed Alzheimer’s and Subcortical Vascular Dementia

Lee

Wiggermann

Rauscher

et al. 2022

Can. J. Neurol. Sci.

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Background: A large proportion of Alzheimer’s disease (AD) patients have coexisting subcortical vascular dementia (SVaD), a condition referred to as mixed dementia (MixD). Brain imaging features of MixD presumably include those of cerebrovascular disease and AD pathology, but are difficult to characterize due to their heterogeneity. Objective: To perform an exploratory analysis of conventional and non-conventional structural magnetic resonance imaging (MRI) abnormalities in MixD and to compare them to those observed in AD and SVaD. Methods: We conducted a cross-sectional, region-of-interest-based analysis of 1) hyperintense white-matter signal abnormalities (WMSA) on T2-FLAIR and hypointense WMSA on T1-weighted MRI; 2) diffusion tensor imaging; 3) quantitative susceptibility mapping; and 4) effective transverse relaxation rate (R2*) in N = 17 participants (AD:5, SVaD:5, MixD:7). General linear model was used to explore group differences in these brain imaging measures. Results: Model findings suggested imaging characteristics specific to our MixD group, including 1) higher burden of WMSAs on T1-weighted MRI (versus both AD and SVaD); 2) frontal lobar preponderance of WMSAs on both T2-FLAIR and T1-weighted MRI; 3) higher fractional anisotropy values within normal-appear white-matter tissues (versus SVaD, but not AD); and 4) lower R2* values within the T2-FLAIR WMSA areas (versus both AD and SVaD). Conclusion: These findings suggest a preliminary picture of the location and type of brain imaging characteristics associated with MixD. Future imaging studies may employ region-specific hypotheses to distinguish MixD more rigorously from AD or SVaD.

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“…Current guidelines for standardized brain and spinal cord MRI for the diagnosis and follow-up of MS include 3D-T2-FLAIR and 3D-T2-weighted images [ 13 , 23 ]. It was recently shown that the combination of 3D-T2 weighted images with 3D-FLAIR, referred to as 3D-FLAIR 2 , leads to a better contrast-to-noise ratio and white–gray-matter contrast, while still suppressing CSF signals and thereby to improved lesion visualization without the need for additional scan time [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

FLAIR2 post-processing: improving MS lesion detection in standard MS imaging protocols

et al. 2021

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Background Technical improvements in magnetic resonance imaging (MRI) acquisition, such as higher field strength and optimized sequences, lead to better multiple sclerosis (MS) lesion detection and characterization. Multiplication of 3D-FLAIR with 3D-T2 sequences (FLAIR2) results in isovoxel images with increased contrast-to-noise ratio, increased white–gray-matter contrast, and improved MS lesion visualization without increasing MRI acquisition time. The current study aims to assess the potential of 3D-FLAIR2 in detecting cortical/leucocortical (LC), juxtacortical (JC), and white matter (WM) lesions. Objective To compare lesion detection of 3D-FLAIR2 with state-of-the-art 3D-T2-FLAIR and 3D-T2-weighted images. Methods We retrospectively analyzed MRI scans of thirteen MS patients, showing previously noted high cortical lesion load. Scans were acquired using a 3 T MRI scanner. WM, JC, and LC lesions were manually labeled and manually counted after randomization of 3D-T2, 3D-FLAIR, and 3D-FLAIR2 scans using the ITK-SNAP tool. Results LC lesion visibility was significantly improved by 3D-FLAIR2 in comparison to 3D-FLAIR (4 vs 1; p = 0.018) and 3D-T2 (4 vs 1; p = 0.007). Comparing LC lesion detection in 3D-FLAIR2 vs. 3D-FLAIR, 3D-FLAIR2 detected on average 3.2 more cortical lesions (95% CI − 9.1 to 2.8). Comparing against 3D-T2, 3D-FLAIR2 detected on average 3.7 more LC lesions (95% CI 3.3–10.7). Conclusions 3D-FLAIR2 is an easily applicable time-sparing MR post-processing method to improve cortical lesion detection. Larger sampled studies are warranted to validate the sensitivity and specificity of 3D-FLAIR2.

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Three-dimensional MRI sequences in MS diagnosis and research

Cited by 11 publications

References 90 publications

Automated quantification of deep grey matter structures and white matter lesions using magnetic resonance imaging in relapsing remission multiple sclerosis

Automated quantification of deep grey matter structures and white matter lesions using magnetic resonance imaging in relapsing remission multiple sclerosis

Brain Imaging Abnormalities in Mixed Alzheimer’s and Subcortical Vascular Dementia

FLAIR2 post-processing: improving MS lesion detection in standard MS imaging protocols

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