This work investigates the use of configuration state imaging together with deep neural networks to develop quantitative MRI techniques for deployment in an interventional setting. A physics modeling technique for inhomogeneous fields and heterogeneous tissues is presented and used to evaluate the theoretical capability of neural networks to estimate parameter maps from configuration state signal data. All tested normalization strategies achieved similar performance in estimating and . Varying network architecture and data normalization had substantial impacts on estimated flip angle and , highlighting their importance in developing neural networks to solve these inverse problems. The developed signal modeling technique provides an environment that will enable the development and evaluation of physics-informed machine learning techniques for MR parameter mapping and facilitate the development of quantitative MRI techniques to inform clinical decisions during MR-guided treatments.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11653200PMC
http://dx.doi.org/10.1007/978-3-031-72120-5_47DOI Listing

Publication Analysis

Top Keywords

neural networks
16
configuration state
12
quantitative mri
8
mri techniques
8
modeling technique
8
physics informed
4
neural
4
informed neural
4
networks
4
networks estimation
4

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!