Mining for iron in the brain: a deep learning approach

Brain iron accumulate with ageing, with advancing age being the major risk factor for neurodegenerative diseases such as Alzheimer's Disease (AD; Walker et al., 2016; Ashraf et al., 2018). While iron chelation therapy has been shown to be effective in attenuating symptoms in experimental models and some studies in man for AD, Parkinson's Disease (PD) and multiple sclerosis (MS), it is only recently that iron chelation has been considered a therapy for these and other neurodegenerative diseases. Iron chelation trials are ongoing in PD and one in AD has recently started. From informal discussions, iron chelation therapy appears promising in the PD trials.

The challenge in using therapeutic iron chelation for maintaining healthy brain ageing or neurodegenerative diseases is the difficulty in in vivo measuring and monitoring iron in human brain. MRI methods including T1, T2 and T2* relaxometry and quantitative susceptibility mapping (QSM), are sensitive to iron content to differing extents and specificities. We and others have used T2 and T2* relaxometry to non-invasively assess iron in vivo, and we have shown their differential associations with synchrotron radiation X-ray fluorescence (SRXRF)-measured iron content (Walker et al., 2016). However, the influence of the cellular and molecular environment, e.g., myelin, on the relationship between various QMRI measurements and iron remain to be studied. Studying post-mortem human brain tissues from control and AD patients, PWS et al., (unpublished data), have observed the relationship between QMRI and iron is better in grey than white matter, and the association is also better in grey matter in AD than in controls, suggesting the modulation of the relationship between QMRI and iron by myelin. Additionally, iron and other transition metals, including copper and zinc are known to associate with amyloid plaques (AP) and neurofibrillary tangles (NFT). How the associations between QMRI and iron are affected by AP and NFT has not been investigated.

The purpose of this project is to develop an interpretable deep learning approach to estimate iron content in human brain from multi-modal QMRI data (see Illustration at the end). We will apply QMRI techniques sensitive to iron, tissue microstructure and composition to post-mortem brain tissue, prior to histology and SRXRF iron mapping pf the same tissue (see adjacent Figure). While histology and SRXRF iron mapping can provide 'ground truth', they can only be used post-mortem. By relating these measurements to QMRI, we will obtain a non-invasive tool with the potential for measuring iron content in-vivo.

This project will benefit from a bespoke state-of-the art MRI hardware that includes recently customized high signal-to-noise (SNR) MRI radiofrequency (RF) coils compatible with existing pre-clinical 9.4T MRI scanners and innovative robust tissue handling system for MRI-histology that enables us to perform MRI at/near microscopic scales and minimise the complexity of overlaying QMRI and histology by performing both on the same slice of tissue.

We will develop an integrated deep learning framework that will align multimodal QMRI, histology and iron maps, bridge the differences in the resolutions and ultimately construct a model of neuron, astrocyte, microglia and oligodendrocyte composition, iron content and tissue microstructure from QMRI. We will generate an integrated MRI-view of brain iron distribution in the presence and absence of AD pathology, including neuroinflammation, AP and NFT.

Strains on the healthcare system in the UK create an acute need for finding more effective, efficient, safe, and accurate non-invasive imaging solutions for clinical decision-making, both in terms of diagnosis and prognosis, and to reduce unnecessary treatment procedures and associated costs. Medical imaging is currently undergoing a step-change facilitated through the advent of artificial intelligence (AI) techniques, in particular deep learning and statistical machine learning, the development of targeted molecular imaging probes and novel "push-button" imaging techniques. There is also the availability of low-cost imaging solutions, creating unique opportunities to improve sensitivity and specificity of treatment options leading to better patient outcome, improved clinical workflow and healthcare economics. However, a skills gap exists between these disciplines which this CDT is aiming to fill.

Consistent with our vision for the CDT in Smart Medical Imaging to train the next generation of medical imaging scientists, we will engage with the key beneficiaries of the CDT: (1) PhD students & their supervisors; (2) patient groups & their carers; (3) clinicians & healthcare providers; (4) healthcare industries; and (5) the general public. We have identified the following areas of impact resulting from the operation of the CDT.

- Academic Impact: The proposed multidisciplinary training and skills development are designed to lead to an appreciation of clinical translation of technology and generating pathways to impact in the healthcare system. Impact will be measured in terms of our students' generation of knowledge, such as their research outputs, conference presentations, awards, software, patents, as well as successful career destinations to a wide range of sectors; as well as newly stimulated academic collaborations, and the positive effect these will have on their supervisors, their career progression and added value to their research group, and the universities as a whole in attracting new academic talent at all career levels.

- Economic Impact: Our students will have high employability in a wide range of sectors thanks to their broad interdisciplinary training, transferable skills sets and exposure to industry, international labs, and the hospital environment. Healthcare providers (e.g. the NHS) will gain access to new technologies that are more precise and cost-efficient, reducing patient treatment and monitoring costs. Relevant healthcare industries (from major companies to SMEs) will benefit and ultimately profit from collaborative research with high emphasis on clinical translation and validation, and from a unique cohort of newly skilled and multidisciplinary researchers who value and understand the role of industry in developing and applying novel imaging technologies to the entire patient pathway.

- Societal Impact: Patients and their professional carers will be the ultimate beneficiaries of the new imaging technologies created by our students, and by the emerging cohort of graduated medical imaging scientists and engineers who will have a strong emphasis on patient healthcare. This will have significant societal impact in terms of health and quality of life. Clinicians will benefit from new technologies aimed at enabling more robust, accurate, and precise diagnoses, treatment and follow-up monitoring. The general public will benefit from learning about new, cutting-edge medical imaging technology, and new talent will be drawn into STEM(M) professions as a consequence, further filling the current skills gap between healthcare provision and engineering.

We have developed detailed pathways to impact activities, coordinated by a dedicated Impact & Engagement Manager, that include impact training provision, translational activities with clinicians and patient groups, industry cooperation and entrepreneurship training, international collaboration and networks, and engagement with the General Public.