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AI-enabled Imaging

Robust quality-controlled quantitative stress perfusion cardiac MRI

Project ID: 2021_007

1st Supervisor: Amedeo Chiribiri, King’s College London
2nd Supervisor: Andrew King, King’s College London
Additional Supervisor: Cian Scannell, King’s College London
Industrial Supervisor: Marcel Breeuwer, Philips Healthcare

Aim of the PhD Project:

Fully automatic assessment of perfusion cardiac MRI can suffer from reliability issues due to low image quality, failed contrast injections, or a lack of response to stress. This project will develop a range of AI-enabled quality control processes to make perfusion cardiac MRI more robust and reliable in the clinic.

Project Description / Background:

The main cause of the underutilisation of stress perfusion cardiac MRI is that visual assessment of the images is highly dependent on the level of training of the operators [1]. As yet, quantitative analysis of stress perfusion cardiac MRI remains primarily a research tool but its clinical translation would be advantageous as it can be automated, enabling accurate and user-independent assessment of myocardial perfusion. Our group has also recently demonstrated the independent prognostic value of quantitative stress perfusion CMR [2]. However, this work still involved several steps of manual interaction, including the segmentation of the myocardium. The automated analysis of quantitative stress perfusion CMR has the potential to revolutionise the management of patients with suspect coronary artery disease.

Our group has pioneered the use of deep learning to achieve the automatic quantitative analysis of stress perfusion CMR [3]. The problem of deploying this solution at large scale is the visual inspection and quality control of the analysis is not feasible. If it is not obvious for which cases the analysis has failed, then clinicians may be presented with failed or inaccurate measurements and, therefore, draw the wrong conclusions. We aim to overcome this limitation by developing a fully automated image quality control (QC) tool using deep neural networks. Deep learning has been widely adopted in the field of medical imaging and has become the de facto standard for many processing tasks. Trends in cardiac MRI image analysis have followed a similar route where deep learning is now used for everything from reconstruction to detection and segmentation tasks to automating diagnostics and prognostics.

To this end, we envisage the development of three QC processes to be developed, validated, and integrated into the clinical workflow.

  1. Analysis of the arterial input function (AIF): Machine learning methods will be used to classify whether the injection of the contrast agent into the patient was done correctly.
  2. Analysis of stress response: Some patients do not respond as expected to the injection of the stressor drug. Algorithms will be trained and validated to identify these patients.
  3. Detection of failed cases. Our previously developed automated processing pipeline can fail, in that the heart is predicted in the wrong location or the predicted shape of the heart is not as expected. A further neural network will be developed to flag these cases.

This project would be suitable for a candidate with a background in engineering, computer science, or mathematics, with an interest in machine learning.

References:

  1. Villa ADM, Corsinovi L, Ntalas I, Milidonis X, Scannell C, Di Giovine G, Child N, Ferreira C, Nazir MS, Karady J, Eshja E, De Francesco V, Bettencourt N, Schuster A, Ismail TF, Razavi R, Chiribiri A. Importance of operator training and rest perfusion on the diagnostic accuracy of stress perfusion cardiovascular magnetic resonance. J Cardiovasc Magn Reson. 2018;20:74.
  2. Sammut EC, Villa ADM, Di Giovine G, Dancy L, Bosio F, Gibbs T, Jeyabraba S, Schwenke S, Williams SE, Marber M, Alfakih K, Ismail TF, Razavi R, Chiribiri A. Prognostic Value of Quantitative Stress Perfusion Cardiac Magnetic Resonance. JACC Cardiovasc Imaging. 2017;
  3. Scannell CM, Veta M, Villa ADM, Sammut EC, Lee J, Breeuwer M, Chiribiri A. Deep‐Learning‐Based Preprocessing for Quantitative Myocardial Perfusion MRI. J Magn Reson Imaging. 2020;51:1689–1696.
  4. Ruijsink B, Puyol-Antón E, Oksuz I, Sinclair M, Bai W, Schnabel JA, Razavi R, King AP. Fully Automated, Quality-Controlled Cardiac Analysis From CMR: Validation and Large-Scale Application to Characterize Cardiac Function. JACC Cardiovasc Imaging. 2020;13:684–695.
  5. Robinson, R., Valindria, V.V., Bai, W. et al. Automated quality control in image segmentation: application to the UK Biobank cardiovascular magnetic resonance imaging study. J Cardiovasc Magn Reson 21, 18 (2019). https://doi.org/10.1186/s12968-019-0523-x

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