Manual Segmentation of MS Cortical Lesions Using MRI: A Comparison of 3 MRI Reading Protocols

Imaging cortical multiple sclerosis lesions with ultra-high field MRI

BACKGROUND

Cortical lesions are abundant in multiple sclerosis (MS), yet difficult to visualize in vivo. Ultra-high field (UHF) MRI at 7 T and above provides technological advances suited to optimize the detection of cortical lesions in MS.

PURPOSE

To provide a narrative and quantitative systematic review of the literature on UHF MRI of cortical lesions in MS.

METHODS

A systematic search of all literature on UHF MRI of cortical lesions in MS published before September 2020. Quantitative outcome measures included cortical lesion numbers reported using 3 T and 7 T MRI and between 7 T MRI sequences, along with sensitivity of UHF MRI towards cortical lesions verified by histopathology.

RESULTS

7 T MRI detected on average 52 ± 26% (mean ± 95% confidence interval) more cortical lesions than the best performing image contrast at 3 T, with the largest increase in type II-IV intracortical lesion detection. Across all studies, the mean cortical lesion number was 17 ± 6 per patient. In progressive MS cohorts, approximately four times more cortical lesions were reported than in CIS/early RRMS, and RRMS. Yet, there was no difference in lesion type ratio between these MS subtypes. Furthermore, superiority of one MRI sequence over another could not be established from available data. Post-mortem lesion detection with UHF MRI agreed only modestly with pathological examinations. Mean pro- and retrospective sensitivity was 33 ± 6% and 71 ± 10%, respectively, with the highest sensitivity towards type I and type IV lesions.

CONCLUSION

UHF MRI improves cortical lesion detection in MS considerably compared to 3 T MRI, particularly for type II-IV lesions. Despite modest sensitivity, 7 T MRI is still capable of visualizing all aspects of cortical lesion pathology and could potentially aid clinicians in diagnosing and monitoring MS, and progressive MS in particular. However, standardization of acquisition and segmentation protocols is needed.

Conversion of diffusely abnormal white matter to focal lesions is linked to progression in secondary progressive multiple sclerosis

Background:

Diffusely abnormal white matter (DAWM) regions are observed in magnetic resonance images of secondary progressive multiple sclerosis (SPMS) patients. However, their role in clinical progression is still not established.

Objectives:

To characterize the longitudinal volumetric and intensity evolution of DAWM and focal white matter lesions (FWML) and assess their associations with clinical outcomes and progression in SPMS.

Methods:

Data include 589 SPMS participants followed up for 3 years (3951 time points). FWML and DAWM were automatically segmented. Screening DAWM volumes that transformed into FWML at the last visit (DAWM-to-FWML) and normalized T1-weighted intensities (indicating severity of damage) in those voxels were calculated.

Results:

FWML volume increased and DAWM volume decreased with an increase in disease duration ( p < 0.001). The Expanded Disability Status Scale (EDSS) was positively associated with FWML volumes ( p = 0.002), but not with DAWM. DAWM-to-FWML volume was higher in patients who progressed (2.75 cm3 vs. 1.70 cm3; p < 0.0001). Normalized T1-weighted intensity of DAWM-to-FWML was negatively associated with progression ( p < 0.00001).

Conclusion:

DAWM transformed into FWML over time, and this transformation was associated with clinical progression. DAWM-to-FWML voxels had greater normalized T1-weighted intensity decrease over time, in keeping with relatively greater tissue damage. Evaluation of DAWM in progressive multiple sclerosis provides a useful measure for therapies aiming to protect this at-risk tissue with the potential to slow progression.

Comparison of Multiple Sclerosis Cortical Lesion Types Detected by Multicontrast 3T and 7T MRI

BACKGROUND AND PURPOSE

Our aims were the following: 1) to compare multicontrast cortical lesion detection using 3T and 7T MR imaging, 2) to compare cortical lesion type frequency in relapsing-remitting and secondary-progressive MS, and 3) to assess whether detectability is related to the magnetization transfer ratio, an imaging marker sensitive to myelin content.

MATERIALS AND METHODS

Multicontrast 3T and 7T MR images from 10 participants with relapsing-remitting MS and 10 with secondary-progressive MS. We used the following 3T contrast sequences: 3D-T1-weighted, quantitative T1, FLAIR, magnetization-transfer, and 2D proton-density- and T2-weighted. We used the following 7T contrast sequences: 3D-T1-weighted, quantitative T1, and 2D-T2*-weighted.

RESULTS

Cortical lesion counts at 7T were the following: 720 total cortical lesions, 420 leukocortical lesions (58%), 27 intracortical lesions (4%), and 273 subpial lesions (38%). Cortical lesion counts at 3T were the following: 424 total cortical, 393 leukocortical (93%), zero intracortical, and 31 subpial (7%) lesions. Total, intracortical, and subpial 3T lesion counts were significantly lower than the 7T counts (P < .002). Leukocortical lesion counts were not significantly different between scanners. Total and leukocortical lesion counts were significantly higher in secondary-progressive MS, at 3T and 7T (P ≤ .02). Subpial lesions were significantly higher in secondary-progressive MS at 7T (P = .006). The magnetization transfer ratio values of leukocortical lesions visible on both scanners were significantly lower than the magnetization transfer ratio values of leukocortical lesions visible only at 3T. No significant difference was found in magnetization transfer ratio values between subpial lesions visible only at 7T and subpial lesions visible on both 3T and 7T.

CONCLUSIONS

Detection of leukocortical lesions at 3T is comparable with that at 7T MR imaging. Imaging at 3T is less sensitive to intracortical and subpial lesions. Leukocortical lesions not visible on 7T T2*-weighted MRI may be associated with less demyelination than those that are visible. Detectability of subpial lesions does not appear to be related to the degree of demyelination.

Detection of Leukocortical Lesions in Multiple Sclerosis and Their Association with Physical and Cognitive Impairment: A Comparison of Conventional and Synthetic Phase-Sensitive Inversion Recovery MRI

BACKGROUND AND PURPOSE

Cortical lesions are common in multiple sclerosis and are included in the latest diagnostic criteria. The limited sensitivity of cortical MS lesions on conventional MR imaging can be improved by phase-sensitive inversion recovery. Synthetic MR imaging could provide phase-sensitive inversion recovery without additional scanning, but the use of synthetic phase-sensitive inversion recovery remains to be validated. We aimed to compare the ability and clinical value of detecting leukocortical lesions with conventional and synthetic phase-sensitive inversion recovery in MS.

MATERIALS AND METHODS

Twenty-one patients with MS prospectively underwent conventional and synthetic phase-sensitive inversion recovery, 3D T1-weighted, and T2 FLAIR imaging. Two neuroradiologists independently performed blinded phase-sensitive inversion recovery lesion assessments; a consensus rating with all sequences was considered the criterion standard. Lesion volumes were segmented. All participants underwent standardized cognitive and physical examinations and Fatigue Severity Scale assessment. Results were analyzed with multiple linear regressions.

RESULTS

Interrater and criterion standard agreement for leukocortical lesions was excellent for both conventional and synthetic phase-sensitive inversion recovery (intraclass correlation coefficient = 0.79-0.97). Leukocortical lesion volumes for both sequences were associated with lower information-processing speed (P ≤ .01) and verbal fluency (P ≤ .02). Both phase-sensitive inversion recovery sequences showed a positive effect on the association when combining volumes of leukocortical lesions and white matter lesions with information-processing speed (P ≤ .005) and verbal fluency (P ≤ .03). No associations were found between leukocortical lesion volumes and physical disability or fatigue.

CONCLUSIONS

Synthetic and conventional phase-sensitive inversion recovery have a sensitivity similar to that of leukocortical MS lesions. The detected leukocortical lesions are associated with cognitive dysfunction and thus provide clinically relevant information, which encourages assessment of cortical MS involvement at conventional field strengths.

Image Quality Evaluation in Clinical Research: A Case Study on Brain and Cardiac MRI Images in Multi-Center Clinical Trials

Magnetic resonance imaging (MRI) system images are important components in the development of drugs because it can reveal the underlying pathology in diseases. Unfortunately, the processes of image acquisition, storage, transmission, processing, and analysis can influence image quality with the risk of compromising the reliability of MRI-based data. Therefore, it is necessary to monitor image quality throughout the different stages of the imaging workflow. This report describes a new approach to evaluate the quality of an MRI slice in multi-center clinical trials. The design philosophy assumes that an MRI slice, such as all natural images, possess statistical properties that can describe different levels of contrast degradation. A unique set of pixel configuration is assigned to each possible level of contrast-distorted MRI slice. Invocation of the central limit theorem results in two separate Gaussian distributions. The central limit theorem says that the mean and standard deviation of pixel configuration assigned to each possible level of contrast degradation will follow a normal distribution. The mean of each normal distribution corresponds to the mean and standard deviation of the underlying ideal image. Quality prediction processes for a test image can be summarized into four steps. The first step extracts local contrast feature image from the test image. The second step computes the mean and standard deviation of the feature image. The third step separately standardizes each normal distribution using the mean and standard deviation computed from the feature image. This gives two separate z-scores. The fourth step predicts the lightness contrast quality score and the texture contrast quality score from cumulative distribution function of the appropriate normal distribution. The proposed method was evaluated objectively on brain and cardiac MRI volume data using four different types and levels of degradation. The four types of degradation are Rician noise, circular blur, motion blur, and intensity nonuniformity also known as bias fields. Objective evaluation was validated using a proposed variation of difference of mean opinion scores. Results from performance evaluation show that the proposed method will be suitable to monitor and standardize image quality throughout the different stages of imaging workflow in large clinical trials. MATLAB implementation of the proposed objective quality evaluation method can be downloaded from (https://github.com/ezimic/Image-Quality-Evaluation).

3D double inversion recovery MR imaging: Clinical applications and usefulness in a wide spectrum of central nervous system diseases

Double inversion recovery (DIR) imaging provides two inversion pulses that attenuate signals from cerebrospinal fluid and normal white matter. This review was undertaken to describe the principle of the DIR sequence, the clinical applications of 3D DIR in various central nervous system diseases and the clinical benefits of the 3D DIR compared with those of other MR sequences. 3D DIR imaging provides better lesion conspicuity and topography than other MR techniques. It is particularly useful for diagnosing the following disease entities: cortical and subcortical abnormalities such as multiple sclerosis, cortical microinfarcts and cortical development anomalies; sulcal abnormalities such as meningitis and subacute/chronic subarachnoid hemorrhage; and optic neuritis caused by multiple sclerosis or neuromyelitis optica.

Voxel-Wise Logistic Regression and Leave-One-Source-Out Cross Validation for white matter hyperintensity segmentation

Many algorithms have been proposed for automated segmentation of white matter hyperintensities (WMH) in brain MRI. Yet, broad uptake of any particular algorithm has not been observed. In this work, we argue that this may be due to variable and suboptimal validation data and frameworks, precluding direct comparison of methods on heterogeneous data. As a solution, we present Leave-One-Source-Out Cross Validation (LOSO-CV), which leverages all available data for performance estimation, and show that this gives more realistic (lower) estimates of segmentation algorithm performance on data from different scanners. We also develop a FLAIR-only WMH segmentation algorithm: Voxel-Wise Logistic Regression (VLR), inspired by the open-source Lesion Prediction Algorithm (LPA). Our variant facilitates more accurate parameter estimation, and permits intuitive interpretation of model parameters. We illustrate the performance of the VLR algorithm using the LOSO-CV framework with a dataset comprising freely available data from several recent competitions (96 images from 7 scanners). The performance of the VLR algorithm (median Similarity Index of 0.69) is compared to its LPA predecessor (0.58), and the results of the VLR algorithm in the 2017 WMH Segmentation Competition are also presented.

Detection and clinical correlation of leukocortical lesions in pediatric-onset multiple sclerosis on multi-contrast MRI

Objective:

To determine the frequency of cortical lesions (CLs) in patients with pediatric-onset multiple sclerosis (POMS) using multi-contrast magnetic resonance imaging (MRI), and the relationship between frontal CL load and upper limb dexterity assessed with the Nine-Hole Peg Test (9-HPT).

Methods:

Participants completed the 9-HPT and were imaged on a 3T MRI scanner to collect T1-weighted three-dimensional (3D) magnetization prepared rapid gradient echo (MPRAGE), proton density–weighted, T2-weighted and fluid-attenuated inversion recovery (FLAIR) images. CLs were manually segmented using all MRI contrasts.

Results:

We enrolled 24 participants with POMS (mean (standard deviation) age at first symptom: 13.3 (±2.7) years; mean age at scan: 18.8 (±3) years; mean disease duration of 5 (±3.2) years). A total of 391 CLs (mean, 16.3 ± 27.2; median, 7) were identified in 19 of 24 POMS patients (79%). The total number of CLs was positively associated with white matter lesion volume ( p = 0.04) but not with thalamic volume, age at the time of the scan, or disease duration. The number of frontal CLs was associated with slower performance on the 9-HPT ( p = 0.05).

Conclusion:

Multi-contrast 3T MRI led to a high rate of CL detection, demonstrating that cortical pathology occurs even in pediatric-onset disease. Frontal lobe CL count was associated with reduced manual dexterity, indicating that these CLs are clinically relevant.

Validation of T1w-based segmentations of white matter hyperintensity volumes in large-scale datasets of aging

INTRODUCTION

Fluid-attenuated Inversion Recovery (FLAIR) and dual T2w and proton density (PD) magnetic resonance images (MRIs) are considered to be the optimum sequences for detecting white matter hyperintensities (WMHs) in aging and Alzheimer's disease populations. However, many existing large multisite studies forgo their acquisition in favor of other MRI sequences due to economic and time constraints.

METHODS

In this article, we have investigated whether FLAIR and T2w/PD sequences are necessary to detect WMHs in Alzheimer's and aging studies, compared to using only T1w images. Using a previously validated automated tool based on a Random Forests classifier, WMHs were segmented for the baseline visits of subjects from ADC, ADNI1, and ADNI2/GO studies with and without T2w/PD and FLAIR information. The obtained WMH loads (WMHLs) in different lobes were then correlated with manually segmented WMHLs, each other, age, cognitive, and clinical measures to assess the strength of the correlations with and without using T2w/PD and FLAIR information.

RESULTS

The WMHLs obtained from T1w-Only segmentations correlated with the manual WMHLs (ADNI1: r = .743, p < .001, ADNI2/GO: r = .904, p < .001), segmentations obtained from T1w + T2w + PD for ADNI1 (r = .888, p < .001) and T1w + FLAIR for ADNI2/GO (r = .969, p < .001), age (ADNI1: r = .391, p < .001, ADNI2/GO: r = .466, p < .001), and ADAS13 (ADNI1: r = .227, p < .001, ADNI2/GO: r = .190, p < 0.001), and NPI (ADNI1: r = .290, p < .001, ADNI2/GO: r = 0.144, p < .001), controlling for age.

CONCLUSION

Our results suggest that while T2w/PD and FLAIR provide more accurate estimates of the true WMHLs, T1w-Only segmentations can still provide estimates that hold strong correlations with the actual WMHLs, age, and performance on various cognitive/clinical scales, giving added value to datasets where T2w/PD or FLAIR are not available.

T1-/T2-weighted ratio differs in demyelinated cortex in multiple sclerosis

Detecting cortical demyelination in patients with multiple sclerosis (MS) is difficult. Using magnetic resonance imaging (MRI), ratio maps of T1-weighted (T1w) and T2-weighted (T2w) images may be sensitive to cortical myelin levels. In this MRI-histological study, postmortem in situ scans were acquired from 6 cadavers with MS on a 3T MRI machine. Immunocytochemistry was used to correlate myelin status and cortical T1w/T2w measures. The results showed that the T1w/T2w values significantly differed between demyelinated and myelinated cortex (p < 0.001). The T1w/T2w ratio maps may be a relatively simple, clinically feasible method to assess cortical demyelination. Ann Neurol 2017;82:635-639.

MRI evidence of acute inflammation in leukocortical lesions of patients with early multiple sclerosis

OBJECTIVE

To identify gadolinium-enhancing lesions affecting the cortex of patients with early multiple sclerosis (MS) and to describe the frequency and evolution of these lesions.

METHODS

We performed a retrospective, observational, longitudinal analysis of MRI scans collected as part of the Betaseron vs Copaxone in Multiple Sclerosis with Triple-Dose Gadolinium and 3T MRI Endpoints (BECOME) study. Seventy-five patients with early-stage MS were scanned monthly, over a period of 12-24 months, using 3T MRI after administration of triple-dose gadolinium. A total of 1,188 scans were included in the analysis. A total of 139 were selected using an image pipeline algorithm that integrated the image information from cortical gray matter masks and gadolinium-enhancing lesion masks. These scans were evaluated to identify gadolinium-enhancing lesions affecting the cortex.

RESULTS

The total number of gadolinium-enhancing lesions was 2,044. The number of gadolinium-enhancing lesions affecting the cortex was 120 (6%), 95% of which were leukocortical. The number of patients who showed gadolinium-enhancing lesions affecting the cortex was 27 (36%). The number of gadolinium-enhancing lesions affecting the cortex at baseline was 25 (21%) and the number of new lesions that developed in follow-up scans was 49 (41%). The number of persistent lesions was 46 (38%).

CONCLUSIONS

The presence of enhancing lesions affecting the cortex and adjacent white matter, although transient and not frequent, suggests that at least some cortical lesions are related to blood-brain barrier disruption. Our data support the concept that there may be an acute inflammatory phase in the development of leukocortical MS lesions.

CLINICALTRIALSGOV IDENTIFIER

NCT00176592.

MRI evidence of acute inflammation in leukocortical lesions of patients with early multiple sclerosis

OBJECTIVE

To identify gadolinium-enhancing lesions affecting the cortex of patients with early multiple sclerosis (MS) and to describe the frequency and evolution of these lesions.

METHODS

We performed a retrospective, observational, longitudinal analysis of MRI scans collected as part of the Betaseron vs Copaxone in Multiple Sclerosis with Triple-Dose Gadolinium and 3T MRI Endpoints (BECOME) study. Seventy-five patients with early-stage MS were scanned monthly, over a period of 12-24 months, using 3T MRI after administration of triple-dose gadolinium. A total of 1,188 scans were included in the analysis. A total of 139 were selected using an image pipeline algorithm that integrated the image information from cortical gray matter masks and gadolinium-enhancing lesion masks. These scans were evaluated to identify gadolinium-enhancing lesions affecting the cortex.

RESULTS

The total number of gadolinium-enhancing lesions was 2,044. The number of gadolinium-enhancing lesions affecting the cortex was 120 (6%), 95% of which were leukocortical. The number of patients who showed gadolinium-enhancing lesions affecting the cortex was 27 (36%). The number of gadolinium-enhancing lesions affecting the cortex at baseline was 25 (21%) and the number of new lesions that developed in follow-up scans was 49 (41%). The number of persistent lesions was 46 (38%).

CONCLUSIONS

The presence of enhancing lesions affecting the cortex and adjacent white matter, although transient and not frequent, suggests that at least some cortical lesions are related to blood-brain barrier disruption. Our data support the concept that there may be an acute inflammatory phase in the development of leukocortical MS lesions.

CLINICALTRIALSGOV IDENTIFIER

NCT00176592.

Performance comparison of 10 different classification techniques in segmenting white matter hyperintensities in aging

INTRODUCTION

White matter hyperintensities (WMHs) are areas of abnormal signal on magnetic resonance images (MRIs) that characterize various types of histopathological lesions. The load and location of WMHs are important clinical measures that may indicate the presence of small vessel disease in aging and Alzheimer's disease (AD) patients. Manually segmenting WMHs is time consuming and prone to inter-rater and intra-rater variabilities. Automated tools that can accurately and robustly detect these lesions can be used to measure the vascular burden in individuals with AD or the elderly population in general. Many WMH segmentation techniques use a classifier in combination with a set of intensity and location features to segment WMHs, however, the optimal choice of classifier is unknown.

METHODS

We compare 10 different linear and nonlinear classification techniques to identify WMHs from MRI data. Each classifier is trained and optimized based on a set of features obtained from co-registered MR images containing spatial location and intensity information. We further assess the performance of the classifiers using different combinations of MRI contrast information. The performances of the different classifiers were compared on three heterogeneous multi-site datasets, including images acquired with different scanners and different scan-parameters. These included data from the ADC study from University of California Davis, the NACC database and the ADNI study. The classifiers (naïve Bayes, logistic regression, decision trees, random forests, support vector machines, k-nearest neighbors, bagging, and boosting) were evaluated using a variety of voxel-wise and volumetric similarity measures such as Dice Kappa similarity index (SI), Intra-Class Correlation (ICC), and sensitivity as well as computational burden and processing times. These investigations enable meaningful comparisons between the performances of different classifiers to determine the most suitable classifiers for segmentation of WMHs. In the spirit of open-source science, we also make available a fully automated tool for segmentation of WMHs with pre-trained classifiers for all these techniques.

RESULTS

Random Forests yielded the best performance among all classifiers with mean Dice Kappa (SI) of 0.66±0.17 and ICC=0.99 for the ADC dataset (using T1w, T2w, PD, and FLAIR scans), SI=0.72±0.10, ICC=0.93 for the NACC dataset (using T1w and FLAIR scans), SI=0.66±0.23, ICC=0.94 for ADNI1 dataset (using T1w, T2w, and PD scans) and SI=0.72±0.19, ICC=0.96 for ADNI2/GO dataset (using T1w and FLAIR scans). Not using the T2w/PD information did not change the performance of the Random Forest classifier (SI=0.66±0.17, ICC=0.99). However, not using FLAIR information in the ADC dataset significantly decreased the Dice Kappa, but the volumetric correlation did not drastically change (SI=0.47±0.21, ICC=0.95).

CONCLUSION

Our investigations showed that with appropriate features, most off-the-shelf classifiers are able to accurately detect WMHs in presence of FLAIR scan information, while Random Forests had the best performance across all datasets. However, we observed that the performances of most linear classifiers and some nonlinear classifiers drastically decline in absence of FLAIR information, with Random Forest still retaining the best performance.

Reliability of cortical lesion detection on double inversion recovery MRI applying the MAGNIMS-Criteria in multiple sclerosis patients within a 16-months period

PURPOSE

In patients with multiple sclerosis (MS), Double Inversion Recovery (DIR) magnetic resonance imaging (MRI) can be used to identify cortical lesions (CL). We sought to evaluate the reliability of CL detection on DIR longitudinally at multiple subsequent time-points applying the MAGNIMs scoring criteria for CLs.

METHODS

26 MS patients received a 3T-MRI (Siemens, Skyra) with DIR at 12 time-points (TP) within a 16 months period. Scans were assessed in random order by two different raters. Both raters separately marked all CLs on each scan and total lesion numbers were obtained for each scan-TP and patient. After a retrospective re-evaluation, the number of consensus CLs (conL) was defined as the total number of CLs, which both raters finally agreed on. CLs volumes, relative signal intensities and CLs localizations were determined. Both ratings (conL vs. non-consensus scoring) were compared for further analysis.

RESULTS

A total number of n = 334 CLs were identified by both raters in 26 MS patients with a first agreement of both raters on 160 out of 334 of the CLs found (κ = 0.48). After the retrospective re-evaluation, consensus agreement increased to 233 out of 334 CL (κ = 0.69). 93.8% of conL were visible in at least 2 consecutive TP. 74.7% of the conL were visible in all 12 consecutive TP. ConL had greater mean lesion volumes and higher mean signal intensities compared to lesions that were only detected by one of the raters (p<0.05). A higher number of CLs in the frontal, parietal, temporal and occipital lobe were identified by both raters than the number of those only identified by one of the raters (p<0.05).

CONCLUSIONS

After a first assessment, slightly less than a half of the CL were considered as reliably detectable on longitudinal DIR images. A retrospective re-evaluation notably increased the consensus agreement. However, this finding is narrowed, considering the fact that retrospective evaluation steps might not be practicable in clinical routine. Lesions that were not reliably identifiable by both raters seem to be characterized by lower signal intensity and smaller size, or located in distinct anatomical brain regions.