Physics model for neural network-based property estimation from multi-pathway multi-echo imaging

Motivation: Generation of multiple MR quantitative contrasts from an efficient multi-pathway multi-echo sequence would be highly useful for non-invasive MRgFUS breast cancer therapy assessment. Goal(s): Develop physics models that enable neural networks to accurately estimate tissue properties from multi-pathway multi-echo imaging. Approach: A Bloch solver was implemented that directly models spectroscopic and position information. Simulated signal magnitudes for a multi-pathway multi-echo sequence were used to train neural networks to estimate flip angle, T1, T2, and T2*. Results: RMS error of parameter estimates for noisy/noiseless evaluation data were 0.4/0.3° for flip angle, 40/9 ms for T1, 10/2 ms for T2, and 7/1.7 ms for T2*. Impact: Multi-pathway multi-echo imaging with machine learning-based MR parameter estimation shows promise in rapidly collecting quantitative data for evaluation of breast cancer treatment. The implemented Bloch solver enables versatile simulation of biological tissues through direct modeling of spectroscopic and position information.