Manisha Waterston

Research Topic: AI methods to optimise ultrasound-guided high intensity focused ultrasound (USgHIFU) treatment plans.

Supervisors: James McLaughlan, James Chandler, Tze Min Wah

About Manisha: I graduated from the University of Edinburgh with a Bachelor’s degree in Theoretical Physics. My final-year project investigated if Regge theory, in particular Multi-Regge theory, could be applied to accurately predict the scaling of two Higgs Bosons involved in a collision. I then took a year out to travel Asia and Australia before beginning the CDT.

Project Description:

High Intensity Focused Ultrasound (HIFU) is a non-invasive and non-ionising treatment that uses high-amplitude ultrasound waves to thermally ablate the treatment area. HIFU is a non-invasive treatment option which is highly desirable due to its lower risk of complications compared to traditional surgical methods. HIFU has been applied across a broad range of medical conditions, including cancers of the thyroid, breast, pancreas, and brain.

A key aspect of making HIFU more efficient is in treatment planning. Current treatment planning methods rely on a Clinician to highlight a Planning Tumour Volume and a Tumour volume similar to a surgical method. Further, the acoustic parameters of the Ultrasound are clinician-dependent. As there is no standardised method, this can lead to over-treating or under-treating the area at risk.

This project seeks to apply AI techniques to increase the efficiency of Ultrasound guided HIFU treatment through optimised real-time treatment planning. These treatment plans will be used with a co-robotic-assisted  USgHIFU system. Further, the aim is to apply these technologies to a thyroid dataset, as utilising HIFU to treat benign thyroid nodules over traditional ablation techniques is highly beneficial. We aim to develop a USgHIFU treatment system for clinical use.