FLAIR-only joint volumetric analysis of brain lesions and atrophy in clinically isolated syndrome (CIS) suggestive of multiple sclerosis

Brain atrophy assessment in multiple sclerosis: technical- and subject-related barriers for translation to real-world application in individual subjects

INTRODUCTION

Brain atrophy is a well-established MRI outcome for predicting clinical progression and monitoring treatment response in persons with multiple sclerosis (pwMS) at the group level. Despite the important progress made, the translation of brain atrophy assessment into clinical practice faces several challenges.

AREAS COVERED

In this review, the authors discuss technical- and subject-related barriers for implementing brain atrophy assessment as part of the clinical routine at the individual level. Substantial progress has been made to understand and mitigate technical barriers behind MRI acquisition. Numerous research and commercial segmentation techniques for volume estimation are available and technically validated, but their clinical value has not been fully established. A systematic assessment of subject-related barriers, which include genetic, environmental, biological, lifestyle, comorbidity, and aging confounders, is critical for the interpretation of brain atrophy measures at the individual subject level. Educating both medical providers and pwMS will help better clarify the benefits and limitations of assessing brain atrophy for disease monitoring and prognosis.

EXPERT OPINION

Integrating brain atrophy assessment into clinical practice for pwMS requires overcoming technical and subject-related challenges. Advances in MRI standardization, artificial intelligence, and clinician education will facilitate this process, improving disease management and potentially reducing long-term healthcare costs.

Clinical study on improving the diagnostic accuracy of adult elbow joint cartilage injury by multisequence magnetic resonance imaging

BACKGROUND

Due to frequent and high-risk sports activities, the elbow joint is susceptible to injury, especially to cartilage tissue, which can cause pain, limited movement and even loss of joint function.

AIM

To evaluate magnetic resonance imaging (MRI) multisequence imaging for improving the diagnostic accuracy of adult elbow cartilage injury.

METHODS

A total of 60 patients diagnosed with elbow cartilage injury in our hospital from January 2020 to December 2021 were enrolled in this retrospective study. We analyzed the accuracy of conventional MRI sequences (T1-weighted imaging, T2-weighted imaging, proton density weighted imaging, and T2 star weighted image) and Three-Dimensional Coronary Imaging by Spiral Scanning (3D-CISS) in the diagnosis of elbow cartilage injury. Arthroscopy was used as the gold standard to evaluate the diagnostic effect of single and combination sequences in different injury degrees and the consistency with arthroscopy.

RESULTS

The diagnostic accuracy of 3D-CISS sequence was 89.34% ± 4.98%, the sensitivity was 90%, and the specificity was 88.33%, which showed the best performance among all sequences (P < 0.05). The combined application of the whole sequence had the highest accuracy in all sequence combinations, the accuracy of mild injury was 91.30%, the accuracy of moderate injury was 96.15%, and the accuracy of severe injury was 93.33% (P < 0.05). Compared with arthroscopy, the combination of all MRI sequences had the highest consistency of 91.67%, and the kappa value reached 0.890 (P < 0.001).

CONCLUSION

Combination of 3D-CISS and each sequence had significant advantages in improving MRI diagnostic accuracy of elbow cartilage injuries in adults. Multisequence MRI is recommended to ensure the best diagnosis and treatment.

Exploring approaches to tackle cross-domain challenges in brain medical image segmentation: a systematic review

Introduction

Brain medical image segmentation is a critical task in medical image processing, playing a significant role in the prediction and diagnosis of diseases such as stroke, Alzheimer's disease, and brain tumors. However, substantial distribution discrepancies among datasets from different sources arise due to the large inter-site discrepancy among different scanners, imaging protocols, and populations. This leads to cross-domain problems in practical applications. In recent years, numerous studies have been conducted to address the cross-domain problem in brain image segmentation.

Methods

This review adheres to the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for data processing and analysis. We retrieved relevant papers from PubMed, Web of Science, and IEEE databases from January 2018 to December 2023, extracting information about the medical domain, imaging modalities, methods for addressing cross-domain issues, experimental designs, and datasets from the selected papers. Moreover, we compared the performance of methods in stroke lesion segmentation, white matter segmentation and brain tumor segmentation.

Results

A total of 71 studies were included and analyzed in this review. The methods for tackling the cross-domain problem include Transfer Learning, Normalization, Unsupervised Learning, Transformer models, and Convolutional Neural Networks (CNNs). On the ATLAS dataset, domain-adaptive methods showed an overall improvement of ~3 percent in stroke lesion segmentation tasks compared to non-adaptive methods. However, given the diversity of datasets and experimental methodologies in current studies based on the methods for white matter segmentation tasks in MICCAI 2017 and those for brain tumor segmentation tasks in BraTS, it is challenging to intuitively compare the strengths and weaknesses of these methods.

Conclusion

Although various techniques have been applied to address the cross-domain problem in brain image segmentation, there is currently a lack of unified dataset collections and experimental standards. For instance, many studies are still based on n-fold cross-validation, while methods directly based on cross-validation across sites or datasets are relatively scarce. Furthermore, due to the diverse types of medical images in the field of brain segmentation, it is not straightforward to make simple and intuitive comparisons of performance. These challenges need to be addressed in future research.

Unveiling the Intricacies: A Comprehensive Review of Magnetic Resonance Imaging (MRI) Assessment of T2-Weighted Hyperintensities in the Neuroimaging Landscape

T2-weighted hyperintensities in neuroimaging represent areas of heightened signal intensity on magnetic resonance imaging (MRI) scans, holding crucial importance in neuroimaging. This comprehensive review explores the T2-weighted hyperintensities, providing insights into their definition, characteristics, clinical relevance, and underlying causes. It highlights the significance of these hyperintensities as sensitive markers for neurological disorders, including multiple sclerosis, vascular dementia, and brain tumors. The review also delves into advanced neuroimaging techniques, such as susceptibility-weighted and diffusion tensor imaging, and the application of artificial intelligence and machine learning in hyperintensities analysis. Furthermore, it outlines the challenges and pitfalls associated with their assessment and emphasizes the importance of standardized protocols and a multidisciplinary approach. The review discusses future directions for research and clinical practice, including the development of biomarkers, personalized medicine, and enhanced imaging techniques. Ultimately, the review underscores the profound impact of T2-weighted hyperintensities in shaping the landscape of neurological diagnosis, prognosis, and treatment, contributing to a deeper understanding of complex neurological conditions and guiding more informed and effective patient care.

Feasibility of detecting atrophy relevant for disability and cognition in multiple sclerosis using 3D-FLAIR

BACKGROUND AND OBJECTIVES

Disability and cognitive impairment are known to be related to brain atrophy in multiple sclerosis (MS), but 3D-T1 imaging required for brain volumetrics is often unavailable in clinical protocols, unlike 3D-FLAIR. Here our aim was to investigate whether brain volumes derived from 3D-FLAIR images result in similar associations with disability and cognition in MS as do those derived from 3D-T1 images.

METHODS

3T-MRI scans of 329 MS patients and 76 healthy controls were included in this cross-sectional study. Brain volumes were derived using FreeSurfer on 3D-T1 and compared with brain volumes derived with SynthSeg and SAMSEG on 3D-FLAIR. Relative agreement was evaluated by calculating the intraclass correlation coefficient (ICC) of the 3D-T1 and 3D-FLAIR volumes. Consistency of relations with disability and average cognition was assessed using linear regression, while correcting for age and sex. The findings were corroborated in an independent validation cohort of 125 MS patients.

RESULTS

The ICC between volume measured with FreeSurfer and those measured on 3D-FLAIR for brain, ventricle, cortex, total deep gray matter and thalamus was above 0.74 for SAMSEG and above 0.91 for SynthSeg. Worse disability and lower average cognition were similarly associated with brain (adj. R2 = 0.24-0.27, p < 0.01; adj. R2 = 0.26-0.29, p < 0.001) ventricle (adj. R2 = 0.27-0.28, p < 0.001; adj. R2 = 0.19-0.20, p < 0.001) and deep gray matter volumes (adj. R2 = 0.24-0.28, p < 0.001; adj. R2 = 0.27-0.28, p < 0.001) determined with all methods, except for cortical volumes derived from 3D-FLAIR.

DISCUSSION

In this cross-sectional study, brain volumes derived from 3D-FLAIR and 3D-T1 show similar relationships to disability and cognitive dysfunction in MS, highlighting the potential of these techniques in clinical datasets.

Multisite MRI reproducibility of lateral ventricular volume using the NAIMS cooperative pilot dataset

BACKGROUND AND PURPOSE

The North American Imaging in Multiple Sclerosis (NAIMS) multisite project identified interscanner reproducibility issues with T1-based whole brain volume (WBV). Lateral ventricular volume (LVV) acquired on T2-fluid-attenuated inverse recovery (FLAIR) scans has been proposed as a robust proxy measure. Therefore, we sought to determine the relative magnitude of scanner-induced T2-FLAIR-based LVV and T1-based WBV measurement errors in relation to clinically meaningful changes.

METHODS

This was a post hoc analysis of the NAIMS pilot dataset in which a relapsing-remitting MS patient with no intrastudy clinical or radiological activity was imaged twice on seven different Siemens scanners across the United States. LVV was determined using the automated NeuroSTREAM technique on T2-FLAIR and WBV was determined with SIENAX on high-resolution T1-MPRAGE. Average LVV and WBV were measured, and absolute intrascanner and interscanner coefficients of variation (CoVs) were calculated. The variabilities were compared to previously established annual pathological and clinically meaningful cutoffs of 0.40% for WBV and of 3.51% for LVV.

RESULTS

Mean LVV across all seven scan/rescan pairs was 45.87 ± 1.15 ml. Average LVV intrascanner CoV was 1.42% and interscanner CoV was 1.78%, both smaller than the reported annualized clinically meaningful cutoff of 3.51%. In contrast, intra- and interscanner CoVs for WBV (0.99% and 1.15%) were both higher than the established cutoff of 0.40%. Individually, 1/7 intrasite and 2/7 intersite pair-wise LVV comparisons were above the 3.51% cutoff, whereas 4/7 intrasite and 7/7 intersite WBV comparisons were above the 0.40% cutoff.

CONCLUSION

Fully automated LVV segmentation has higher absolute variability than WBV, but much lower relative variability compared to clinically relevant changes, and may therefore be a meaningful proxy outcome measure of neurodegeneration.

Quantification of amyloid PET for future clinical use: a state-of-the-art review

Amyloid-β (Aβ) pathology is one of the earliest detectable brain changes in Alzheimer's disease (AD) pathogenesis. The overall load and spatial distribution of brain Aβ can be determined in vivo using positron emission tomography (PET), for which three fluorine-18 labelled radiotracers have been approved for clinical use. In clinical practice, trained readers will categorise scans as either Aβ positive or negative, based on visual inspection. Diagnostic decisions are often based on these reads and patient selection for clinical trials is increasingly guided by amyloid status. However, tracer deposition in the grey matter as a function of amyloid load is an inherently continuous process, which is not sufficiently appreciated through binary cut-offs alone. State-of-the-art methods for amyloid PET quantification can generate tracer-independent measures of Aβ burden. Recent research has shown the ability of these quantitative measures to highlight pathological changes at the earliest stages of the AD continuum and generate more sensitive thresholds, as well as improving diagnostic confidence around established binary cut-offs. With the recent FDA approval of aducanumab and more candidate drugs on the horizon, early identification of amyloid burden using quantitative measures is critical for enrolling appropriate subjects to help establish the optimal window for therapeutic intervention and secondary prevention. In addition, quantitative amyloid measurements are used for treatment response monitoring in clinical trials. In clinical settings, large multi-centre studies have shown that amyloid PET results change both diagnosis and patient management and that quantification can accurately predict rates of cognitive decline. Whether these changes in management reflect an improvement in clinical outcomes is yet to be determined and further validation work is required to establish the utility of quantification for supporting treatment endpoint decisions. In this state-of-the-art review, several tools and measures available for amyloid PET quantification are summarised and discussed. Use of these methods is growing both clinically and in the research domain. Concurrently, there is a duty of care to the wider dementia community to increase visibility and understanding of these methods.

Recent advances in the longitudinal segmentation of multiple sclerosis lesions on magnetic resonance imaging: a review

Multiple sclerosis (MS) is a chronic autoimmune disease characterized by demyelinating lesions that are often visible on magnetic resonance imaging (MRI). Segmentation of these lesions can provide imaging biomarkers of disease burden that can help monitor disease progression and the imaging response to treatment. Manual delineation of MRI lesions is tedious and prone to subjective bias, while automated lesion segmentation methods offer objectivity and speed, the latter being particularly important when analysing large datasets. Lesion segmentation can be broadly categorised into two groups: cross-sectional methods, which use imaging data acquired at a single time-point to characterise MRI lesions; and longitudinal methods, which use imaging data from the same subject acquired at two or more different time-points to characterise lesions over time. The main objective of longitudinal segmentation approaches is to more accurately detect the presence of new MS lesions and the growth or remission of existing lesions, which may be effective biomarkers of disease progression and treatment response. This paper reviews articles on longitudinal MS lesion segmentation methods published over the past 10 years. These are divided into traditional machine learning methods and deep learning techniques. PubMed articles using longitudinal information and comparing fully automatic two time point segmentations in any step of the process were selected. Nineteen articles were reviewed. There is an increasing number of deep learning techniques for longitudinal MS lesion segmentation that are promising to help better understand disease progression.

MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies

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Sharing data from cooperative studies is essential to develop new biomarkers in MS.

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Differences in MRI acquisition, analysis, storage represent a substantial constraint.

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We review the state of the art and developments in the harmonization of MRI.

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We provide recommendations to harmonize large MRI datasets in the MS field.

There is an increasing need of sharing harmonized data from large, cooperative studies as this is essential to develop new diagnostic and prognostic biomarkers. In the field of multiple sclerosis (MS), the issue has become of paramount importance due to the need to translate into the clinical setting some of the most recent MRI achievements. However, differences in MRI acquisition parameters, image analysis and data storage across sites, with their potential bias, represent a substantial constraint. This review focuses on the state of the art, recent technical advances, and desirable future developments of the harmonization of acquisition, analysis and storage of large-scale multicentre MRI data of MS cohorts. Huge efforts are currently being made to achieve all the requirements needed to provide harmonized MRI datasets in the MS field, as proper management of large imaging datasets is one of our greatest opportunities and challenges in the coming years. Recommendations based on these achievements will be provided here. Despite the advances that have been made, the complexity of these tasks requires further research by specialized academical centres, with dedicated technical and human resources. Such collective efforts involving different professional figures are of crucial importance to offer to MS patients a personalised management while minimizing consumption of resources.