Stress perfusion cardiac magnetic resonance (CMR) imaging has been shown to be highly accurate for the detection of coronary artery disease. However, a major limitation is that the accuracy of the visual assessment of the images is challenging and thus the accuracy of the diagnosis is highly dependent on the training and experience of the reader. Quantitative perfusion CMR, where myocardial blood flow values are inferred directly from the MR images, is an automated and user-independent alternative to the visual assessment.
This talk will focus on addressing the main technical challenges which have hampered the adoption of quantitative myocardial perfusion MRI in clinical practice. The talk will cover the problem of respiratory motion in the images and the use of dimension reduction techniques, such as robust principal component analysis, to mitigate this problem. I will then discuss our deep learning-based image processing pipeline that solves the necessary series of computer vision tasks required for the blood flow modelling and introduce the Bayesian inference framework in which the kinetic parameter values are inferred from the imaging data.