BACKGROUND AND PURPOSE: Hereditary diffuse leukoencephalopathy with spheroids (HDLS) and multiple sclerosis (MS) are demyelinating and neurodegenerative disorders that can be hard to distinguish clinically and radiologically. HDLS is a rare disorder compared to MS, which has led to occurrent misdiagnosis of HDLS as MS. That is problematic since their prognosis and treatment differ. Both disorders are investigated by MRI, which could help to identify patients with high probability of having HDLS, which could guide targeted genetic testing to confirm the HDLS diagnosis. METHODS: Here, we present a machine learning method based on quantitative MRI that can achieve a robust classification of HDLS versus MS. Four HDLS and 14 age-matched MS patients underwent a quantitative brain MRI protocol (synthetic MRI) at 3 Tesla (T) (scan time '7 minutes). We also performed a repeatability analysis of the predicting features to assess their generalizability by scanning a healthy control with five scan-rescans at 3T and 1.5T. RESULTS: Our predicting features were measured with an average confidence interval of 1.7% (P =.01), at 3T and 2.3% (P =.01) at 1.5T. The model gave a 100% correct classification of the cross-validation data when using 5-11 predicting features. When the maximum measurement noise was inserted in the model, the true positive rate of HDLS was 97.2%, while the true positive rate of MS was 99.6%. CONCLUSIONS: This study suggests that computer-assistance in combination with quantitative MRI may be helpful in aiding the challenging differential diagnosis of HDLS versus MS. 

Colony-stimulating factor 1 receptor (CSF1R)-related leukoencephalopathy is a rare but fatal microgliopathy. The diagnosis is often delayed due to multifaceted symptoms that can mimic several other neurological disorders. Imaging provides diagnostic clues that help identify cases. The objective of this study was to integrate the literature on neuroimaging phenotypes of CSF1R-related leukoencephalopathy. A systematic review and meta-analysis were performed for neuroimaging findings of CSF1R-related leukoencephalopathy via PubMed, Web of Science, and Embase on 25 August 2021. The search included cases with confirmed CSF1R mutations reported under the previous terms hereditary diffuse leukoencephalopathy with spheroids, pigmentary orthochromatic leukodystrophy, and adult-onset leukoencephalopathy with axonal spheroids and pigmented glia. In 78 studies providing neuroimaging data, 195 cases were identified carrying CSF1R mutations in 14 exons and five introns. Women had a statistically significant earlier age of onset (p = 0.041, 40 vs 43 years). Mean delay between symptom onset and neuroimaging was 2.3 years. Main magnetic resonance imaging (MRI) findings were frontoparietal white matter lesions, callosal thinning, and foci of restricted diffusion. The hallmark computed tomography (CT) finding was white matter calcifications. Widespread cerebral hypometabolism and hypoperfusion were reported using positron emission tomography and single-photon emission computed tomography. In conclusion, CSF1R-related leukoencephalopathy is associated with progressive white matter lesions and brain atrophy that can resemble other neurodegenerative/-inflammatory disorders. However, long-lasting diffusion restriction and parenchymal calcifications are more specific findings that can aid the differential diagnosis. Native brain CT and brain MRI (with and without a contrast agent) are recommended with proposed protocols and pictorial examples are provided. 

Objective: Magnetic resonance imaging (MRI) is essential for multiple sclerosis diagnostics but is conventionally not specific to demyelination. Myelin imaging is often hampered by long scanning times, complex postprocessing, or lack of clinical approval. This study aimed to assess the specificity, robustness, and clinical value of Rapid Estimation of Myelin for Diagnostic Imaging, a new myelin imaging technique based on time-efficient simultaneous T1/T2 relaxometry and proton density mapping in multiple sclerosis. Methods: Rapid myelin imaging was applied using 3T MRI ex vivo in 3 multiple sclerosis brain samples and in vivo in a prospective cohort of 71 multiple sclerosis patients and 21 age/sex-matched healthy controls, with scan–rescan repeatability in a subcohort. Disability in patients was assessed by the Expanded Disability Status Scale and the Symbol Digit Modalities Test at baseline and 2-year follow-up. Results: Rapid myelin imaging correlated with myelin-related stains (proteolipid protein immunostaining and Luxol fast blue) and demonstrated good precision. Multiple sclerosis patients had, relative to controls, lower normalized whole-brain and normal-appearing white matter myelin fractions, which correlated with baseline cognitive and physical disability. Longitudinally, these myelin fractions correlated with follow-up physical disability, even with correction for baseline disability. Interpretation: Rapid Estimation of Myelin for Diagnostic Imaging provides robust myelin quantification that detects diffuse demyelination in normal-appearing tissue in multiple sclerosis, which is associated with both cognitive and clinical disability. Because the technique is fast, with automatic postprocessing and US Food and Drug Administration/CE clinical approval, it can be a clinically feasible biomarker that may be suitable to monitor myelin dynamics and evaluate treatments aiming at remyelination.

Background: Multiple sclerosis (MS) is a chronic neuroinflammatory and neurodegenerative disease that targets myelin in the brain and spinal cord. The corpus callosum connects the cerebral hemispheres and is composed of heavily myelinated axons. Atrophy of the corpus callosum has been explored as a more sensitive marker of disease status and neurodegeneration relative to other neuroanatomical structures. However, development of more accurate, precise and less labor demanding tools for characterizing callosal atrophy would increase its potential as a proxy marker of MS evolution.

Purpose: The primary objective of this thesis was to evaluate and develop quantitative methods for measuring neurodegeneration in MS with a focus on the corpus callosum. This was achieved through the comparison of the accuracy and precision of manual delineation, conventional volumetric methods, and machine learning approaches.

Study I: In a prospective study, 9 MS patients underwent scan/re-scanning with and without repositioning to measure the precision and accuracy of manual versus volumetric cross-sectional and longitudinal FreeSurfer analyses. While the longitudinal stream of FreeSurfer revealed the highest precision, the overall limitations on accuracy warrants caution.

Study II: In a prospective study, 553 MS patients with 704 2D T2-weighted MRI acquisitions were used to train and validate a machine learning algorithm for segmenting a marker of neurodegeneration. The algorithm quickly produced highly accurate segmentations of the corpus callosum and brain (Dice Coefficient: 89% and 98%, respectively). The algorithm had numerically higher correlations to neurologic disability as compared to FreeSurfer.

Study III: Analogous to Study II, in a prospective study, 631 MS patients with 3D T1-weighted and T2-weighted FLAIR acquisitions were used to train and validate a machine learning algorithm for segmenting the mid-sagittal normalized corpus callosum area. The algorithm performed better with T1-weighted scans and less atrophied patients. Scanner parameters had no significant effect on the T1-weighted output. The algorithm produced segmentations in less than a minute per scan, with similar correlations to neurologic disability, as compared to FreeSurfer.

Study IV: In a prospective study, 92 MS patients acquired both 3 and 7 Tesla brain MRI scans to reveal the lobe-specific lesion volumes’ association to corpus callosum atrophy, where lesion burden was found to be greatest in the frontal and parietal lobes. In addition, the posterior portions of the corpus callosum provided the strongest fit linear regression models, with a combination of white matter lesions and intracortical lesions predicting atrophy.

Conclusions: Creating and evaluating novel tools for measuring neurodegeneration over time is important both for monitoring disease progression and to evaluate therapeutic responses with current drugs. As novel therapeutic strategies appear, it may also help in assessing neuroregenerative approaches.

BACKGROUND AND PURPOSE: Corpus callosum atrophy is a sensitive biomarker of multiple sclerosis (MS) neurodegeneration but typically requires manual 2D or volumetric 3D-based segmentations. We developed a supervised machine learning algorithm, DeepnCCA, for corpus callosum segmentation and relate callosal morphology to clinical disability using conventional MRI scans collected in clinical routine.

METHODS: In a prospective study of 553 MS patients with 704 acquisitions, 200 unique 2D T₂ -weighted MRI scans were delineated to develop, train, and validate DeepnCCA. Comparative FreeSurfer segmentations were obtained in 504 3D T₁ -weighted scans. Both FreeSurfer and DeepnCCA outputs were correlated with clinical disability. Using principal component analysis of the DeepnCCA output, the morphological changes were explored in relation to clinical disease burden.

RESULTS: .76%, for intracranial and corpus callosum area, respectively through 10-fold cross-validation). DeepnCCA had numerically stronger correlations with cognitive and physical disability as compared to FreeSurfer: Expanded disability status scale (EDSS) ±6 months (r = -.22 P = .002; r = -.17, P = .013), future EDSS (r = -.26, P<.001; r = -.17, P = .012), and future symbol digit modalities test (r = .26, P = .001; r = .24, P = .003). The corpus callosum became thinner with increasing cognitive and physical disability. Increasing physical disability, additionally, significantly correlated with a more angled corpus callosum.

CONCLUSIONS: DeepnCCA (https://github.com/plattenmichael/DeepnCCA/) is an openly available tool that can provide fast and accurate corpus callosum measurements applicable to large MS cohorts, potentially suitable for monitoring disease progression and therapy response.

BACKGROUND AND PURPOSE

Corpus callosum atrophy is a neurodegenerative biomarker in multiple sclerosis (MS). Manual delineations are gold standard but subjective and labor intensive. Novel automated methods are promising but require validation. We aimed to compare the robustness of manual versus automatic corpus callosum segmentations based on FreeSurfer.

METHODS

Nine MS patients (6 females, age 38 ± 13 years, disease duration 7.3 ± 5.2 years) were scanned twice with repositioning using 3‐dimensional T₁‐weighted magnetic resonance imaging on three scanners (two 1.5 T and one 3.0 T), that is, six scans/patient, on the same day. Normalized corpus callosum areas were measured independently by a junior doctor and neuroradiologist. The cross‐sectional and longitudinal streams of FreeSurfer were used to segment the corpus callosum volume.

RESULTS

Manual measurements had high intrarater (junior doctor .96 and neuroradiologist .96) and interrater agreement (.94), by intraclass correlation coefficient (P < .001). The coefficient of variation was lowest for longitudinal FreeSurfer (.96% within scanners; 2.0% between scanners) compared to cross‐sectional FreeSurfer (3.7%, P = .001; 3.8%, P = .058) and the neuroradiologist (2.3%, P = .005; 2.4%, P = .33). Longitudinal FreeSurfer was also more accurate than cross‐sectional (Dice scores 83.9 ± 7.5% vs. 78.9 ± 8.4%, P < .01 relative to manual segmentations). The corpus callosum measures correlated with physical disability (longitudinal FreeSurfer r = –.36, P < .01; neuroradiologist r = –.32, P < .01) and cognitive disability (longitudinal FreeSurfer r = .68, P < .001; neuroradiologist r = .64, P < .001).

CONCLUSIONS

FreeSurfer's longitudinal stream provides corpus callosum measures with better repeatability than current manual methods and with similar clinical correlations. However, due to some limitations in accuracy, caution is warranted when using FreeSurfer with clinical data.