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Small vessel disease (SVD) is a major cause of stroke and contributor to dementia cases. As work continues to develop new treatments to address the impact of SVD, new imaging techniques are needed to identify and track the progression of brain changes that occur with SVD. Magnetic Resonance Imaging (MRI) is the gold standard to diagnose poor brain health due to small vessel disease. However, current MRI systems are expensive and complex to operate, and so access is limited.
Low-field MRI technology, operating at magnetic field strengths many times lower than conventional MRI, can make brain imaging much more cost-effective and accessible. However, further work is needed to develop low-field MRI towards clinically feasible assessments of brain health. The University of Aberdeen hosts a unique network of researchers and imaging technologies that is now making it possible to test and develop different low-field MRI approaches towards solving key healthcare challenges.
The aim of this study is to evaluate the potential of two distinct approaches, field-cycling imaging (FCI) and ultra-low field MRI (ULF-MRI), to detect brain changes linked with small vessel disease. Automated methods will be developed to analyse images and extract measurements that detect and track progression of disease severity.
Full description
Cerebral small vessel disease (SVD) is a major underlying contributor to ischaemic and haemorrhagic stroke, cognitive decline, and dementia. Neuroimaging features of SVD have been shown to be associated with increased risk of stroke occurrence, reoccurrence, and cognitive decline post-stroke. New approaches are needed that target SVD diagnosis, monitoring, and treatment, to improve outcomes across the stroke and cognitive decline care pathways. However, providing equitable access to advanced medical technologies across the Scottish population is becoming a significant burden on the NHS and wider clinical community. Low-field MRI, which is both more cost-effective and less complex to operate, has the potential to address this growing societal burden at a whole-population level. Whilst on-going research efforts remain focussed on developing new interventions to slow progression of SVD and improve treatment outcomes, work is also needed to improve approaches to assess SVD pathophysiology, severity, progression, and treatment outcome. Repeated assessment with magnetic resonance imaging (MRI), is recognised as the best solution to detect the multiple features that combine to produce SVD. This has the potential to identify markers of stroke risk and cognitive decline and, in future, guide tailored treatments to better manage stroke risk and improve post-stroke long-term outcomes. No other imaging technique, blood test or other assessment can provide the necessary information. However, the high cost and infrastructure demands of conventional MRI means that while it is feasible for clinical trials and research, it cannot realistically be used for widespread repeated brain imaging assessment, or screening, for SVD. For this reason, robust and reliable imaging methods that can be easily implemented across the whole Scottish population, rural/urban, wealthy/deprived must be developed. Operating at magnetic field strengths up to 10,000 times lower than conventional MRI, low-field MRI technologies provide a unique opportunity to make repeated brain imaging cost-effective and accessible in community and outpatient settings. The utility of commercially available portable low-field MRI technology, operating at a fixed field of 64 mT (milliTesla), has already been proven for imaging acute stroke at the bedside of patients and recent preliminary findings have indicated its feasibility to detect moderate and severe SVD. However, the novelty of portable low-field MRI means that its full potential has yet to be realised, and work is now required to develop low-field quantitative imaging approaches to enhance the capabilities of low-field MRI to extract underlying features related to brain health and SVD severity. We aim to provide a stepping-stone to optimised fixed low-field MRI technology by leveraging the unique capabilities of field-cycling imaging (FCI), a unique whole-body MRI technology developed at the University of Aberdeen. IRAS Form Reference: 25/PR/0216 IRAS Version 6.4.1 Date: 13/02/2025 350553/1711955/37/104 7Investigations using field-cycling technologies, which vary the strength of the magnetic field during acquisition, have shown unique sensitivity to tissue features and contrast mechanisms that are invisible at higher, fixed, magnetic fields such as 1.5 and 3T. Furthermore, using FCI, differences between healthy tissue and stroke pathology have been observed. Our own preliminary results, using the mark 2 FCI scanner to image patients with SVD at field strengths between 0.2 and 200 mT have shown a significant positive linear correlation between the volume of SVD extracted from FCI images and from 3T MRI images. However, these preliminary results, limited to a single slice acquisition at 3.1 x 3.1 x 10 mm resolution, do not provide the full evidence needed to design new, cost-effective and accessible, lowfield MRI scanners that would enable widespread, repeated brain imaging assessment for SVD. The objective of this proposal is therefore to address this evidence gap and to inform the design of new low-field MRI technologies tailored to extracting key markers of SVD. First, by utilising the unique capabilities of the new mark 3 FCI scanner, now onsite at Aberdeen Royal Infirmary, imaging data will be acquired at multiple different field strengths, and a higher pixel resolution. This data will be used to determine the most effective low-field approach to identify markers of SVD (e.g., white matter changes, reduced perfusion, microbleeds, recent lacunar infarcts) and SVD progression. Second, by acquiring imaging data from both field-cycling imaging (FCI) and a separate fixed low-field MRI scanner, the extent of agreement between assessments of SVD will be examined.
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85 participants in 4 patient groups
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Gordon D Waiter; Mary Joan MacLeod
Data sourced from clinicaltrials.gov
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