Project Summary My overarching goal is to become a biomedical engineer capable of advancing minimally invasive procedures through medical imaging and machine learning. To achieve this, I aim to develop a broad understanding of diagnostic and interventional radiology and robust technical skills in acquiring, reconstructing, and applying machine learning techniques to biomedical imaging data. This research focuses on addressing unsolved engineering problems in magnetic resonance imaging (MRI)-based evaluation of focused ultrasound for breast cancer treatment, providing a strong foundation for a successful career in interventional imaging. Traditionally, MRI has provided qualitative insights into biological tissues. Recent advances in image acquisition, reconstruction, and deep learning have created new opportunities for making quantitative measurements of physical and chemical properties using MRI. Deep learning techniques face technical challenges in quantitative MRI, such as the absence of large training datasets and their current inability to cope with variations across scanners and protocols, especially in the interventional context. This work investigates integrating physics knowledge into the architecture and training of deep learning models to mitigate these problems and enable reliable and clinically deployable quantitative MRI techniques for evaluating MR-guided focused ultrasound breast cancer treatments. Aim 1 uses physics-informed machine learning to develop an efficient technique for measuring MR relaxation times in the breast using configuration state imaging. A physics-informed neural network architecture and training paradigm will be methodically investigated using simulated data. The developed model will then be trained on sparse real data acquired using a multi-echo configuration state imaging sequence and rigorously evaluated on data from phantoms, healthy volunteers, and breast cancer patients across multiple MR scanners and time points. Aim 2 aims to develop a time-efficient technique for obtaining diffusion measurements in breast imaging with full 3D coverage, evaluating the relative performance of conventional model-based techniques and physics-informed neural networks in estimating diffusion parameters from the collected data. After developing the sequence and technique, diffusion parameter maps will be compared with gold-standard measurements in standardized diffusion phantoms, healthy volunteers, and breast cancer patients on multiple scanners. This research will advance the current understanding of how to create generalizable machine learning models for MRI and to design them for usability in a clinical context. Additionally, the developed MRI techniques will enable clinical and interventional use of quantitative MRI, supporting the development of biomarkers that provide real-time evaluation of MR-guided focused ultrasound breast cancer treatments.