Treatment efficacy prediction of focused ultrasound therapies using multi-parametric magnetic resonance imaging.

Magnetic resonance guided focused ultrasound (MRgFUS) is one of the most attractive emerging minimally invasive procedures for breast cancer, which induces localized hyperthermia, resulting in tumor cell death. Accurately assessing the post-ablation viability of all treated tumor tissue and surrounding margins immediately after MRgFUS thermal therapy residual tumor tissue is essential for evaluating treatment efficacy. While both thermal and vascular MRI-derived biomarkers are currently used to assess treatment efficacy, currently, no adequately accurate methods exist for the in vivo determination of tissue viability during treatment.

Physics Informed Neural Networks for Estimation of Tissue Properties from Multi-echo Configuration State MRI.

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 .

Validation of a drift-corrected 3D MR temperature imaging sequence for breast MR-guided focused ultrasound treatments.

Real-time temperature monitoring is critical to the success of thermally ablative therapies. This work validates a 3D thermometry sequence with k-space field drift correction designed for use in magnetic resonance-guided focused ultrasound treatments for breast cancer. Fiberoptic probes were embedded in tissue-mimicking phantoms, and temperature change measurements from the probes were compared with the magnetic resonance temperature imaging measurements following heating with focused ultrasound.