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AI-enabled Imaging, Affordable Imaging

Next generation of ultrasound imaging using ultrafast acquisition and machine learning

Project ID: 2020_051

Student: Rifkat Zaydullin

1st Supervisor: Mengxing Tang, Imperial College London
2nd Supervisors: Wenjia Bai, Imperial College London and Kirsten Christensen-Jeffries, King’s College London
Clinical Champion: Adrian Lim, Imperial College London
Additional Supervisors: Enrico Grisan, Anil Bharath and Antonio Stanziola

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

The successful applicant should have a degree in a relevant scientific field, such as engineering, computer science, physics, statistics.

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