Augmented patient-specific functional medical imaging by implicit manifold learning

Robert Rapadamnaba (1) , Franck Nicoud (1) , Bijan Mohammadi (1)

1 IMAG - Institut Montpelliérain Alexander Grothendieck

This paper uses machine learning to enrich magnetic resonance angiography and magnetic resonance imaging acquisitions. A convolutional neural network is built and trained over a synthetic database linking geometrical parameters and mechanical characteristics of the arteries to blood flow rates and pressures in an arterial network. Once properly trained, the resulting neural network can be used in order to predict blood pressure in cerebral arteries noninvasively in nearly real-time. One challenge here is that not all input variables present in the synthetic database are known from patient-specific medical data. To overcome this challenge, a learning technique, which we refer to as implicit manifold learning, is employed: in this view, the input and output data of the neural network are selected based on their availability from medical measurements rather than being defined from the mechanical description of the arterial system. The results show the potential of the method and that machine learning is an alternative to costly ensemble based inversion involving sophisticated fluid structure models

Mots clés: noninvasive pressure estimation.; hemodynamic problems; transfer learning; convolutional neural network; machine learning

Domaines: Modélisation et simulation

Robert Rapadamnaba, Franck Nicoud, Bijan Mohammadi. Augmented patient-specific functional medical imaging by implicit manifold learning. International Journal for Numerical Methods in Biomedical Engineering, 2020, 36 (5), pp.e3325. ⟨10.1002/cnm.3325⟩. ⟨hal-02500022⟩