2401

View Abstract / View Presentation

A Supervised Artificial Neural Network Approach with Standardized Targets for IVIM Maps Computation

Alfonso Mastropietro¹, Daniele Procissi², Elisa Scalco¹, Giovanna Rizzo¹, and Nicola Bertolino²
¹Istituto di Tecnologie Biomediche, Consiglio Nazionale delle Ricerche, Segrate, Italy, ²Radiology, Northwestern University, Chicago, IL, United States

Fitting the IVIM bi-exponential model is challenging especially at low SNRs and time consuming. In this work we propose a supervised artificial neural network approach to obtain reliable parameters estimation as demonstrated in both simulated data and real acquisition.

Fig.1: The image shows a representative examples of D, f and D* maps for each SNRs, in simulations, generated with our neural network model and their respective ground-truth images.

Fig.4: The image shows in-vivo IVIM maps examples generated using: our neural network a), Bayesian method b), Barbieri's neural network c), and Bertleff's neural network d).