NEW-CONTRAST-MRI · A foundational AI-MRI framework for self-supervised discovery of transformative low-field MRI techniques

Magnetic resonance imaging (MRI) is crucial to healthcare for its radiation-free, high-quality scans, but its high cost leaves 70% of the global population without access. Emerging low-field MRI technology offers affordable, portable systems with transformative potential, but faces critical challenges: long scan times, low signal-to-noise ratio (SNR), and poor tissue contrast, which makes some tissues indistinguishable. Although contrast agents help, they add risks. Early studies showed that low-field MRI can detect cancer without contrast agents using unique pulse sequences, but those are manually designed and slow. Recently, AI has been adopted for clinical high-field MRI, but AI pulse-sequence optimization relies on supervised learning, limiting discovery, AI theory is scarce, and simulations do not fully capture MRI’s complex spin dynamics. Moreover, AI use in low-field MRI remains mostly focused on image post-processing, while pulse sequence design, sampling, and reconstruction remain traditional and suboptimal.I propose to develop a foundational AI framework that will transform low-field MRI into a rapid, high-quality modality. To break the barriers posed by supervised AI and simulations, my key innovation is an integrated AI-MRI framework, where on-the-fly MRI measurements guide a self-supervised AI search through the parameter space. Leveraging AI foundations I have recently developed, the framework will jointly optimize pulse sequences, sampling, and reconstruction to revolutionize imaging. Specific aims: (1) speed up MRI by an order of magnitude; (2) establish AI theory; (3) build the framework and develop cutting-edge sequences for optimal tissue contrast; and (4) demonstrate these in human scans with my lab’s low-field MRI. Preliminary results support the feasibility of our design aims. This project will transform low-field MRI into a fast, affordable, contrast-agent-free tool with broad clinical applications, particularly in low-income regions.