EPSRC Reference: 
EP/V029428/1 
Title: 
Combining Knowledge And Data Driven Approaches to Inverse Imaging Problems 
Principal Investigator: 
Schoenlieb, Professor C 
Other Investigators: 

Researcher CoInvestigators: 

Project Partners: 

Department: 
Applied Maths and Theoretical Physics 
Organisation: 
University of Cambridge 
Scheme: 
EPSRC Fellowship 
Starts: 
01 June 2021 
Ends: 
31 May 2026 
Value (£): 
1,240,288

EPSRC Research Topic Classifications: 

EPSRC Industrial Sector Classifications: 
R&D 
Healthcare 
Information Technologies 


Related Grants: 

Panel History: 

Summary on Grant Application Form 
Imaging plays an important role in many applications in the natural sciences, medicine and the life sciences, as well as in engineering and industrial applications. An example is an MRI image of a brain used by a physician to detect a brain tumour such as glioblastoma. At the core of many imaging applications is an inverse problem, i.e. the mathematical problem of reconstructing the image from data produced by the imaging machine, for example the MRI machine. Such inverse imaging problems have been approached for many years in a "knowledgedriven" way, using information about the device and the imaging procedure. However, the knowledgedriven models cannot always be solved, are computationally very expensive, or deliver suboptimal images.
In recent years, new "datadriven" methods, which use past examples of successfully reconstructed images together with the data that produced them, have been shown to produce some impressive results in image reconstruction. The problem with such datadriven methods, however, is that currently they do not have "mathematical guarantees", in other words one cannot state the degree to which the results are reliable. They also have the property that even small deviations in the data could result in large differences in the results. This clearly could have devastating implications for many applications.
In this proposal, we will develop a new hybrid approach that combines the best of knowledgedriven and datadriven methods for inverse imaging problems, crucially providing the mathematical guarantees essential to being able to use the methods in realworld applications. Once the challenging task of developing these mathematical methods is achieved, we will apply this learning to produce an imaging pipeline that draws into a single step the stages of the imaging process, thus optimising the process further. We will apply the new methods to realworld applications. For example, using the data driven mathematical methods developed in the project and working closely with the Radiology Department, we will create an endtoend workflow where multimodal image acquisition, reconstruction, segmentation and image analyses are performed jointly and optimised for the end task of real time treatment response assessment in patients with metastatic cancer.

Key Findings 
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Potential use in nonacademic contexts 
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Impacts 
Description 
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Summary 

Date Materialised 


Sectors submitted by the Researcher 
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Project URL: 

Further Information: 

Organisation Website: 
http://www.cam.ac.uk 