Multicenter Evaluation of Geometric Accuracy of MRI Protocols Used in Experimental Stroke

Now that the door is open: an update on ischemic stroke pharmacotherapeutics for the neurointerventionalist

The last 10 years have seen a major shift in management of large vessel ischemic stroke with changes towards ever-expanding use of reperfusion therapies (intravenous thrombolysis and mechanical thrombectomy). These strategies 'open the door' to acute therapeutics for ischemic tissue, and we should investigate novel therapeutic approaches to enhance survival of recently reperfused brain. Key insights into new approaches have been provided through translational research models and preclinical paradigms, and through detailed research on ischemic mechanisms. Additional recent clinical trials offer exciting salvos into this new strategy of pairing reperfusion with neuroprotective therapy. This pairing strategy can be employed using drugs that have shown neuroprotective efficacy; neurointerventionalists can administer these during or immediately after reperfusion therapy. This represents a crucial moment when we emphasize reperfusion, and have the technological capability along with the clinical trial experience to lead the way in multiprong approaches to stroke treatment.

Machine Learning for Brain MRI Data Harmonisation: A Systematic Review

BACKGROUND

Magnetic Resonance Imaging (MRI) data collected from multiple centres can be heterogeneous due to factors such as the scanner used and the site location. To reduce this heterogeneity, the data needs to be harmonised. In recent years, machine learning (ML) has been used to solve different types of problems related to MRI data, showing great promise.

OBJECTIVE

This study explores how well various ML algorithms perform in harmonising MRI data, both implicitly and explicitly, by summarising the findings in relevant peer-reviewed articles. Furthermore, it provides guidelines for the use of current methods and identifies potential future research directions.

METHOD

This review covers articles published through PubMed, Web of Science, and IEEE databases through June 2022. Data from studies were analysed based on the criteria of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Quality assessment questions were derived to assess the quality of the included publications.

RESULTS

a total of 41 articles published between 2015 and 2022 were identified and analysed. In the review, MRI data has been found to be harmonised either in an implicit (n = 21) or an explicit (n = 20) way. Three MRI modalities were identified: structural MRI (n = 28), diffusion MRI (n = 7) and functional MRI (n = 6).

CONCLUSION

Various ML techniques have been employed to harmonise different types of MRI data. There is currently a lack of consistent evaluation methods and metrics used across studies, and it is recommended that the issue be addressed in future studies. Harmonisation of MRI data using ML shows promises in improving performance for ML downstream tasks, while caution should be exercised when using ML-harmonised data for direct interpretation.

LEGO-compatible modular mapping phantom for magnetic resonance imaging

Physical phantoms have been widely used for performance evaluation of magnetic resonance imaging (MRI). Although there are many kinds of physical phantoms, most MRI phantoms use fixed configurations with specific sizes that may fit one or a few different types of radio frequency (RF) coils. Therefore, it has limitations for various image quality assessments of scanning areas. In this article, we report a novel design for a truly customizable MRI phantom called the LEGO-compatible Modular Mapping (MOMA) phantom, which not only serves as a general quality assurance phantom for a wide range of RF coils, but also a flexible calibration phantom for quantitative imaging. The MOMA phantom has a modular architecture which includes individual assessment functionality of the modules and LEGO-type assembly compatibility. We demonstrated the feasibility of the MOMA phantom for quantitative evaluation of image quality using customized module assembly compatible with head, breast, spine, knee, and body coil features. This unique approach allows comprehensive image quality evaluation with wide versatility. In addition, we provide detailed MOMA phantom development and imaging characteristics of the modules.

Atlas registration for edema-corrected MRI lesion volume in mouse stroke models

Lesion volume measurements with magnetic resonance imaging are widely used to assess outcome in rodent models of stroke. In this study, we improved a mathematical framework to correct lesion size for edema which is based on manual delineation of the lesion and hemispheres. Furthermore, a novel MATLAB toolbox to register mouse brain MR images to the Allen brain atlas is presented. Its capability to calculate edema-corrected lesion size was compared to the manual approach. Automated image registration performed equally well in in a mouse middle cerebral artery occlusion model (Pearson r = 0.976, p = 2.265e-11). Information encapsulated in the registration was used to generate maps of edema induced tissue volume changes. These showed discrepancies to simplified tissue models underlying the manual approach. The presented techniques provide biologically more meaningful, voxel-wise biomarkers of vasogenic edema after stroke.

In vivo imaging biomarkers of neuroinflammation in the development and assessment of stroke therapies - towards clinical translation

Modulation of the inflammatory microenvironment after stroke opens a new avenue for the development of novel neurorestorative therapies in stroke. Understanding the spatio-temporal profile of (neuro-)inflammatory imaging biomarkers in detail thereby represents a crucial factor in the development and application of immunomodulatory therapies. The early integration of quantitative molecular imaging biomarkers in stroke drug development may provide key information about (i) early diagnosis and follow-up, (ii) spatio-temporal drug-target engagement (pharmacodynamic biomarker), (iii) differentiation of responders and non-responders in the patient cohort (inclusion/exclusion criteria; predictive biomarkers), and (iv) the mechanism of action. The use of targeted imaging biomarkers for may thus allow clinicians to decipher the profile of patient-specific inflammatory activity and the development of patient-tailored strategies for immunomodulatory and neuro-restorative therapies in stroke. Here, we highlight the recent developments in preclinical and clinical molecular imaging biomarkers of neuroinflammation (endothelial markers, microglia, MMPs, cell labeling, future developments) in stroke and outline how imaging biomarkers can be used in overcoming current translational roadblocks and attrition in order to advance new immunomodulatory compounds within the clinical pipeline.