Student: Cian Scannell
Stress perfusion CMR is currently one of the clinical methods of choice for diagnosing myocardial ischaemia. However, its use is presently restricted to experienced tertiary centres: not because most hospitals lack the equipment to carry out these scans, but because interpretation of the data is complex, time-consuming, and requires extensive training of those making the diagnosis. Therefore there is a need for a simpler and more objective quantitative method of CMR perfusion analysis.
Over the last five years, Philips Healthcare and KCL have investigated the possibility of automatic quantitative CMR perfusion analysis, which could provide a more reproducible and accurate evaluation of patients’ condition and would provide useful quantitative data for future research. We have already proven the concept’s feasibility and have invented a range of new semi-quantitative algorithms that can identify abnormal myocardial perfusion. These tools have proven robust, accurate and simple to use.
We now want to translate these results into an automatic perfusion-analysis tool, to validate this tool against established diagnostic standards, and to evaluate its efficacy on a larger number of patients with suspected CAD. This is what the proposed project is designed to perform.
The PhD project will consist of three work packages:
1) Completing the software tool, building on existing coding work from Philips and KCL.
2) Testing the software on existing datasets to refine the clinical workflow and allow comparison with alternative diagnostic methods.
3) Testing the efficacy of user-independent quantitative analysis against fractional flow reserve (FFR), the present diagnostic standard.
Because the software is designed to work with existing hospital equipment, a successful project could have significant and immediate consequences for clinical practice, making it possible for smaller hospitals accurately to diagnose for this condition where they could not before.