Next generation of ultrasound imaging using ultrafast acquisition and machine learning

Aim of the PhD Project:

The project aims to develop advanced biomedical ultrasound image reconstruction technology, taking advantage of the large amount of data generated by ultrafast ultrasound acquisition, and machine learning algorithms for

fast/real-time data processing of the multiple GB/s data acquired by ultrafast ultrasound and 3D acquisitions,
novel image reconstruction technologies in both 2D and 3D, using machine learning and knowledge of the imaging physics to achieve unprecedented image quality
expanding the machine learning algorithms to have temporal components, taking advantage of the very high temporal resolution data obtained by ultrafast acquisition
explore the applications of the techniques in cardiovascular disease and cancer
Project Description / Background:

In the last decade, ultrafast and 3D ultrasound techniques are rapidly expanding fields in biomedical ultrasound thanks to the advances in electronics, computing and transducer technologies. While ultrafast acquisition technologies offer exciting opportunities for better image quality and information content, significant challenges still exist. Firstly, the amount of data is significant (multiple GBs per second) and the computational cost of the existing approaches prevents their real time implementation. Secondly even with the ultrafast capability the principles of acquisition and processing strategies still largely rely on classical approaches, which only produce a marginal improvement of image quality compared to standard ultrasound.

The aim of the project is to design and evaluate novel image reconstruction and data processing technologies by developing deep learning model based approaches throughout the image formation chain, in order to significantly speed up the imaging and improve image quality. Most existing machine learning studies in the field of ultrasound have been focused on post image-processing, and the application of deep learning models and algorithms for image reconstruction is largely an unexplored area.

As a roadmap, in this project the student will:

Explore the landscape of existing advanced image reconstruction algorithms for US which generate better image quality than the classic approach, but currently are too slow. These advanced algorithms include the Minimum Variance methods, Coherence factor, sparse regularization. Such approaches all suffer from very slow reconstruction and currently not suitable for clinical use. After evaluation of the various methods, the student will design neural network architectures that can speed up such methods by providing in real-time optimal acquisition and reconstruction parameters.
Explore the use of deep learning and our knowledge of physics (e.g. using acoustic wave simulation) to directly reconstruct images with superior image quality. Instead of relying on the simple geometrical acoustic approximation as currently done, it is possible to use the physics of the acoustic propagation and design reconstruction strategies based on more complex and realistic models. Deep neural networks will be used to regularize the inversion of measured data to ideal images via end to end training incorporating the simulation of physics. Compression of the problem to a suitable space will also be explored, to ensure a computational burden compatible with real time application.

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.