DIFFERENCE: DIFFusion magnetic resonance imaging with Enhanced Resolution ENCoding - Precision Imaging in Cancer

Cancer causes a third of all UK deaths and its management remains a challenge. For example, 1 in 7 men will be diagnosed with prostate cancer in their lifetime; 8 in 10 men will survive for >10-years, yet, it is difficult to tell at the beginning who is likely to respond well or poorly to treatment and indeed, who may not need treatment at all. Our current way of diagnosing cancer is to sample the prostate using cutting needles (a 'biopsy').

This can be painful, is invasive, and importantly, biopsies may miss prostate cancer because they are not aimed at the right area or yield a false diagnosis because they miss the most aggressive part of the cancer in up to a third of patients.

Imaging already plays a key role in cancer diagnostics. Diffusion imaging using a magnetic resonance scanner is particularly good for imaging cancer. It uses magnetic fields to scan the motion of water molecules at a microscopic level.

As this water motion is reduced in cancers, they stand out against the background and are easily detected. However, the problem with diffusion imaging is that the images can appear blurred and misshapen ('low quality') as it takes time to scan during which the body organs move: These images are also only two dimensional, 2D not three-dimensional, 3D, and lack fine detail ('low resolution').

If we had 3D images with fine detail and more accurate numerical measurements instead, we could use diffusion imaging to detect even small cancers, to plan new treatments that require an exact picture of the cancer, and with new computing techniques ('artificial intelligence') unravel currently hidden information about the cancer that may forecast its future behaviour. However, the robustness and sensitivity of diffusion imaging data needs to be improved significantly for these sophisticated analyses to work.

My aim is to turn diffusion imaging into a precise diagnostic tool, focussing first on prostate cancer. Specifically, I will tackle the technical challenges that currently prevent us from having high-quality, high-resolution 3D diffusion images.

Challenge 1: Diffusion imaging takes too long: movement from breathing, bowel contraction and heart motion blurs the microscopic water movement we are trying to see when we scan. Challenge 2: The quality of data from images is not detailed enough for precise treatments to specific cancer areas.

Challenge 3: Current analysis only achieves a very simple measurement of 'diffusion length '. Deeper insight into cancer microstructure, and how the cancer may behave, could be gained by developing more sophisticated analysis combining diffusion imaging with artificial intelligence (deep learning).

I will develop a new method to acquire diffusion images that will use detailed magnetic resonance signal 'simulations' and a novel 3D 'phase navigator signal' to track, model and get rid of the movement that interferes with the diffusion signal we wish to capture. I will also develop more sophisticated analysis methods to better quantify and show this information.

I will combine the new imaging method with 'artificial intelligence' computing to see if together they can help forecast the aggressiveness of an individual patient's cancer.

My research will take place at King's College London, where there is a unique interdisciplinary environment with artificial intelligence researchers, clinicians, and industry partners. At the end of this fellowship, I believe the new high-resolution diffusion technique together with new analysis methods will improve the early detection of clinically significant prostate cancer, avoid unnecessary biopsy and guide biopsy when needed, and help determine whether treatment is required or not.

Combined with artificial intelligence methods, my work will open up new opportunities to transform patient outcomes by enabling personalised precision treatment such as MR-guided radiotherapy treatment.