Background

Recently developed ultra-low-field MRI scanners are cheaper than conventional high-field MRI, are mobile, and only require a standard electrical socket to run. King’s has access to several portable ultra-low-field MRI systems, which we are beginning to use for the imaging of patients referred for routine clinical high-field MRI. However, it isn’t clear whether existing approaches for neuroimaging analysis apply to portable MRI, and development of customised approaches is likely required.

Novelty & Importance

Portable ultra-low-field MRI has the potential to considerably widen access to clinical neuroimaging. In high-income settings such as the UK, portable MRI scanners could be used for triage of patients in community diagnostic centres or even GP services. In low-and-middle-income settings, portable MRI could facilitate diagnosis of patients with limited or no access to standard high-field MRI.

Aims & Objectives

This project will explore the practical relevance of clinical ultra-low-field MRI, both by evaluating existing methods for quantitative neuroimaging analysis, and by developing novel approaches. Current approaches for neuroimaging analysis include methods for automated segmentation of both healthy and abnormal tissues, and estimation of their volumes; registration to align pairs of scans; as well as other dedicated processing steps. However, it isn’t clear whether these tools can be applied to clinical scans from portable ultra-low-field scanners. The student will have access to data from both healthy participants and patients with numerous diagnoses, scanned at both ultra-low- and high-field, to systematically evaluate existing processing tools on clinical portable MRI scans, and to develop new approaches.

References

Váša F, Bennallick C, Bourke NJ, Padormo F, Baljer L, Briski U, Cawley P, Arichi T, Wood TC, Lythgoe DJ, Dell’Acqua F, Booth TC, Venkataraman AV, Ljungberg E, Deoni SCL, Moran RJ, Leech R*, Williams SCR* (2024). Ultra-low-field brain MRI morphometry: test-retest reliability and correspondence to high-field MRI. bioRxiv 2024.08.14.607942

Wood DA, Kafiabadi S, Busaidi AA, […], Booth TC. (2022). Deep learning models for triaging hospital head MRI examinations. Medical Image Analysis 78:102391. 

Arnold TC, Freeman CW , Litt B, and Stein JM. (2023). Low-field MRI: Clinical promise and challenges. Journal of Magnetic Resonance Imaging 57 (1): 25–44.