Premium
Augmented patient‐specific functional medical imaging by implicit manifold learning
Author(s) -
Rapadamnaba Robert,
Nicoud Franck,
Mohammadi Bijan
Publication year - 2020
Publication title -
international journal for numerical methods in biomedical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.741
H-Index - 63
eISSN - 2040-7947
pISSN - 2040-7939
DOI - 10.1002/cnm.3325
Subject(s) - convolutional neural network , artificial intelligence , computer science , artificial neural network , functional magnetic resonance imaging , magnetic resonance imaging , machine learning , nonlinear dimensionality reduction , inversion (geology) , deep learning , medical imaging , radiology , medicine , paleontology , structural basin , biology , dimensionality reduction
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.