From the cluster to the clinic: Real-time treatment planning for transcranial ultrasound therapy using deep learning (Ext.)

Lead Research Organisation: University College London
Department Name: Medical Physics and Biomedical Eng

Abstract

This is an extension of the Early Career Fellowship: Model-Based Treatment Planning for Focused Ultrasound Surgery.

Brain disorders present a huge challenge for health services across the world, with studies showing these conditions affect as many as one third of the adult population. In the UK, approximately 1 in 6 people are affected by a neurological disorder and 1 in 6 by a common psychiatric disorder. The total annual cost of these conditions is estimated to exceed £100 billion. These disorders can be devastating for patients and greatly reduce their quality of life. Today, patients are often treated by the prescription of drugs that alter the way the brain functions. For many patients, this causes a reduction in their symptoms. However, if these drugs are used for long periods of time, their effectiveness often decreases and there can be many side-effects. It can also be difficult for drugs to exit the blood-stream and enter the brain as desired because of a protective lining called the blood-brain barrier. Depending on their diagnosis, some patients may also be offered surgical procedures to remove part of the brain or implant small wires that use electricity to stimulate brain cells.

One exciting alternative to drugs and surgery is the use of ultrasound. Ultrasound imaging is well known for taking pictures of developing babies during pregnancy. However, ultrasound is now also starting to be used to treat brain disorders. This is possible because ultrasound waves cause mechanical vibrations that affect the brain in different ways. For example, they can cause the tissue to heat up or generate forces that agitate the brain cells and tissue scaffolding. Several different types of treatment are possible depending on the pattern of ultrasound pulses used. This includes precisely destroying small regions of tissue, generating or suppressing electrical signals in the brain, or temporarily opening the blood-brain barrier to allow drugs to be delivered more effectively. These treatments are all completely non-invasive and have the potential to significantly improve outcomes for patients.

A major challenge for ultrasound therapy is ensuring the ultrasound energy is delivered to the precise location identified by the doctor. This is difficult because the skull bone is very rigid and causes the ultrasound waves to be reflected and distorted. It is possible to predict and correct for these distortions using powerful computer models of how ultrasound waves travel through the body. However, these models can take many hours or days to run on large supercomputers, so cannot currently be used for patient treatments.

The aim of this fellowship extension is to develop a new type of model that can make very fast predictions of how sound waves travel in the brain. This will be based on a special type of artificial intelligence called deep learning. The deep learning models will be trained to predict the distortion caused by the skull bone. The models will learn this using a large number of training examples generated using the powerful computer models mentioned above. As part of the project, the models will be rigorously tested using patient data from previous clinical treatments. Carefully planned laboratory experiments using phantom materials designed to mimic the skull and brain will also be conducted. The new models will allow doctors to automatically correct for distortions caused by the skull and quickly predict the treatment outcomes. This would be a major breakthrough in the treatment of brain disorders and enable the wide-spread application of ground-breaking ultrasound therapies.

Planned Impact

The direct beneficiaries of this project are patients suffering neurological and psychiatric brain disorders. This covers a wide spectrum of conditions, including Parkinson's disease, Alzheimer's disease, essential tremor, and depression. These disorders are extremely debilitating and have a significant impact on quality of life for patients and carers. Taken together, these conditions comprise the largest single cause of morbidity in the EU in terms of disability adjusted life years. This has clear implications for healthcare budgets and the economy more broadly.

In the last decade, new treatments for brain disorders based on therapeutic uses of ultrasound have generated huge excitement in the research and medical communities. Ultrasound offers the unique ability to non-invasively ablate brain tissue, deliver drugs, stimulate or modulate brain activity, and open the blood-brain barrier. However, one major barrier to the wider clinical adoption of this technology is the lack of accurate online treatment planning tools. The skull can significantly distort the ultrasound waves as they propagate into the brain, so planning tools are essential to correct for these distortions and predict treatment outcomes ahead of time. However, even with large supercomputers, existing treatment planning models can take hours or days to run, making them unsuitable for online use in many clinical applications.

The novel tools for treatment planning based on deep learning outlined in this proposal could provide a major breakthrough in computing performance and act as a catalyst for the widespread clinical application of therapeutic ultrasound technologies in the brain. Impact will arise from: (i) the enhanced accuracy compared to existing models used in commercial devices, (ii) the unprecedented levels of computational performance which will allow model-based treatment planning predictions to be made in real-time, (iii) the extensive validation of the models, and (iv) adherence to the regulatory framework required for the clinical application of scientific software. In the context of delivering value-based healthcare, these tools could also play a significant role in decreasing procedural costs and optimising clinical outcomes. This impact will be enhanced by open-source software releases and the establishment of a new subject repository for ultrasound metrology data.

The enhanced capabilities for ultrasound therapy offered by real-time treatment planning software will provide a significant competitive edge over other planning approaches currently used in academia and industry. This will make the software commercially attractive to manufacturers of therapeutic ultrasound equipment, two of whom are already directly engaged with this project. It is expected the generated IP will lead to licensing agreements or the development of new start-ups, with the UK becoming a base for future international investment. The developed tools will also act as a platform technology for wide-reaching investigations into the interaction of ultrasound with the human body.

Publications

10 25 50