An integrated MRI tool to map brain microvascular and metabolic function: improving imaging diagnostics for human brain disease

Brain diseases such as tumours, head injury, epilepsy, multiple sclerosis and dementias have considerable personal, social and economic costs for the sufferers and their carers. While magnetic resonance imaging (MRI) has revolutionised the management of many brain conditions in the last 40 years, there is a need for better tools for quantifying the brain's supply of energy in terms of blood flow and vascular function and it use of energy in terms of metabolic function. For example, in the case of the most common forms of brain tumour, glioma, we lack detailed information about the heterogeneity of tissue function that could help guide better treatments such as more targeted and individualised combined radiotherapy and drug programmes. Understanding more about the tumour microenvironment will also promote the development of more effective treatments. For high-grade gliomas, particularly glioblastoma, the prognosis remains poor, highlighting an urgent clinical need.

Recently, we at Cardiff University Brain Research Imaging Centre (CUBRIC), and others, have developed MRI-based tools (termed dual calibrated fMRI) to map across the human brain, with a spatial resolution of a few millimetres, the amount of oxygen that the brain is consuming (known as CMRO2) along with measures of the efficiency of blood supply. CMRO2 reflects neural activity and can be altered with disease such as tumour where there is cell proliferation and energy metabolism is changed. Knowing also the functional properties of brain blood vessels and the oxygen status of brain tissue is important for understanding whether blood supply is sufficient or the vasculature is abnormal as is often seen in tumours where vessels proliferate. Our newly developed methods have shown promise in revealing abnormalities of brain tissue energy consumption in multiple sclerosis and epilepsy. In epilepsy they may offer an alternative to the use of radiation-based PET scans in the evaluation of patients for brain surgery by identifying areas in the brain with abnormally low metabolism.

However, to produce a wider clinical impact it is necessary to advance the MRI and data analysis further, such that they could then be taken forward for commercial development and routine clinical use, initially within clinical trials. Two-thirds of the proposed project will address engineering and physical science challenges to (i) speed up data acquisition to about 10 mins, a clinically feasible time, by optimising the MRI data acquisition and analysis, (ii) widen the range of tissue pathology that we can reliably measure through collection of additional MRI information and detailed biophysical modelling of tissue properties and (iii) implement efficient artificial intelligence (neural network) based data analysis that can rapidly feed the images to the clinician at the MRI scanner. The remaining one-third of the project will demonstrate the feasibility of the method and its value in application to brain tumour (glioma). We aim to show that we can map the heterogeneity of tumour tissue that can reveal the type of tumour, where it is actively growing, where it is and is not responding to treatment and where radiotherapy may be damaging healthy tissue, all helping to guide treatment decisions for maximum efficacy.

Central to the success of our proposal are our partnerships with industry and the NHS. Siemens will contribute the expertise of its onsite scientist at CUBRIC for the development of the MRI technology. The Velindre Cancer Centre, South Wales' principal centre for oncology, will partner on the clinical pilot studies and help to evaluate imaging for future patient benefit. Our partners will help us to bring the methods to the point within this project, if successful, of commercial development for healthcare benefit and larger scale clinical trials to demonstrate how the methods may be used in clinical practice for diagnosis, treatment planning and monitoring.

The research proposed would ultimately benefit patients with common neurological diseases as well as patients with brain tumours. Cancer Research UK has identified brain tumours as one of its four cancers of unmet need because of their poor outcome. In the short term, researchers would benefit from a non-invasive neuroimaging tool that allows them to map brain energy supply and oxygen consumption with good reliably across a wide range of tissue pathology. Tissue hypoxia is a well-recognised cause of treatment resistance, particularly to radiotherapy. Identifying areas of hypoxia within tumours at baseline, or evolving areas of hypoxia during treatment, could allow radiotherapy treatment plans to be personalised or adapted mid-treatment based on tumour response. It would therefore promote research into radiotherapy personalisation. This work addresses an important priority area for Neuro-Oncology research identified by the James Lind Alliance in 2015 including improving techniques for subtyping brain tumours for treatment selection. Specifically, this project is supporting efforts to improve the outcome of patients with brain cancers through better tumour control and survival, reduced side effects and quicker recovery from treatment with improved quality of life.

As well as the contribution to improved treatment for brain cancer, for which urgent improvements are needed, metabolic brain imaging could become widespread for diagnosis, treatment planning and monitoring a wide range of brain diseases, including epilepsy, MS, head injury, cerebrovascular disease and neurodegenerative conditions such as dementia (e.g. Alzheimer's), as well as psychiatric conditions for which there is a current lack of diagnostic imaging tools. The ability to map human brain oxygen metabolism and detailed cerebrovascular (including microvascular) function could offer benefits to many of these patients. In some cases, the MRI-based methods may replace radiotracer based clinical scans aimed at measuring brain metabolism, by providing clinically comparable information (we have already collected some evidence for the feasibility of this in epilepsy). Use of MRI in some of these conditions could expand the patient groups that benefit from metabolic imaging and open the way to repeated scanning over time and thus the monitoring of disease progression and treatment effectiveness, allowing more individualised treatment pathways.

Some of these brain conditions affect young patients in their economically most active years, causing disability and a reducing their ability to work. Dementia, however, as well as being debilitating for the patient, imposes a high economic and healthcare burden on society and carers. An improved availability of the clinical tools to diagnose early, stratify patients and select the best treatment early on could reduce the impact of some of these conditions on the individual and on society, as well as reducing the overall health costs through the reduction of accumulated disability.

Commercialisation of a new clinical imaging product, incorporating both imaging and analysis technology developments, would provide a positive economic impact for imaging technology providers such as Siemens, other MRI vendors and potential developers of image analysis and interpretation tools based on artificial intelligence techniques. The new imaging tools would be valuable in drug development (we have already shown proof-of-concept in a pilot pharmacological study of caffeine's effects on the brain). The pharmaceutical industry needs tools to demonstrate brain penetration of new compounds and early signals of efficacy to de-risk the very expensive drug development pipeline. The UK would benefit greatly given its strength in drug discovery and development. Prof Wise has 18 years' experience in developing fMRI methods in concert with large pharmaceutical companies in the UK and internationally.