A New Physics-Based Model Could Lead to Sharper and Safer MRI Scans

Researchers from Rice University and Oak Ridge National Laboratory have introduced a new physics-based model that could change how we understand and improve MRI scans. The model offers a clearer and far more accurate way to interpret how water molecules behave around metal-based contrast agents, which are substances commonly injected into patients to enhance MRI images. This breakthrough could eventually lead to sharper medical images, better diagnostics, and safer contrast agents.

At the heart of the research is a method called the NMR eigenmodes framework, which addresses a problem that has existed in MRI science for decades. Current models used to describe molecular relaxation — the process that determines how tissues appear on an MRI scan — rely heavily on simplified assumptions. These older models often treat complex molecular movements as if they were simple, one-note processes. While they’ve been useful, they tend to gloss over the richer, more detailed behavior of molecules, and that limits how accurately scientists can predict or improve MRI performance.

The new framework tackles this problem directly by solving the full physical equations behind nuclear magnetic resonance relaxation. The researchers turned to the Fokker–Planck equation, a powerful mathematical tool used to describe how probabilities of molecular positions and velocities evolve over time. By using this equation, they were able to map out an entire “spectrum” of molecular behaviors — essentially identifying all the natural modes by which water molecules respond to contrast agents.

This approach gives a far more detailed and realistic understanding of the relaxation process. To make sense of it, the team compares the idea to music: older models captured just one or two notes of a chord, while the new model captures the full harmony. This fuller picture is especially important when dealing with gadolinium-based contrast agents, the most widely used in clinical MRI scans.

One of the major strengths of this new model is that it doesn’t just match experimental measurements with high precision — it also explains why those measurements look the way they do. It can reproduce data at clinical MRI frequencies and show how the simplified models scientists have used for decades are actually special cases of this broader, more complete framework. This kind of theoretical unification is rare and valuable because it keeps past knowledge valid while expanding it significantly.

The research team tested their framework against detailed molecular dynamics simulations, which simulate how atoms and molecules interact, and also compared it with experimental results involving real contrast agents. In all of these tests, the model performed extremely well. By pinpointing both inner-shell (water molecules tightly bound to the contrast agent) and outer-shell (water molecules loosely interacting) contributions, the framework provided nuanced predictions that older models often missed or oversimplified.

Although the immediate application is improving MRI contrast agents and imaging quality, the implications of the new model reach far beyond medicine. NMR relaxation processes are used in many fields, including battery research, petroleum engineering, materials science, and the study of fluids in confined spaces such as porous rocks or biological cells. The ability to model molecular-scale behavior so precisely could give scientists in these fields a new tool for understanding how fluids behave under different conditions.

For instance, in energy storage research, understanding how electrolyte molecules move and interact can directly influence battery design. In geology, interpreting the relaxation of fluids in porous structures can help scientists study underground reservoirs. This new framework offers a physics-grounded pathway to expand insight across all these areas.

Another benefit: the research team has made their code open source, meaning other scientists can use, test, and expand on it. This is an important step because developing highly accurate models is only half the battle — the wider scientific community also needs access to the tools to validate and build upon these ideas.

Behind this research effort is a collaboration between experts in chemical engineering, mathematical modeling, and computational science. Key contributors include Thiago J. Pinheiro dos Santos (the first author), Walter Chapman, Dilipkumar Asthagiri, Philip Singer, and Betul Orcan-Ekmekci. Their combined expertise made it possible to bring together complex physics, advanced mathematics, and real-world experimental validation.

To give more context, MRI technology depends on the way hydrogen nuclei in water molecules respond to magnetic fields. After being disturbed by a magnetic pulse, these nuclei relax back to their original alignment, releasing signals that are captured to form images. The speed and complexity of this relaxation process determine image contrast — which is why contrast agents are used in the first place. The clearer the understanding of this relaxation, the better we can tune or redesign these agents.

Contrast agents, especially those based on gadolinium, have been widely used since the 1980s. While generally effective, there have been ongoing concerns about long-term safety and potential retention in tissues. A deeper understanding of how these agents interact with water at the molecular level can contribute to developing new agents that are both safer and more effective.

This is where the new eigenmodes framework might truly make its mark. By giving scientists a tool that doesn’t rely on broad approximations, they can now analyze how different molecular structures influence relaxation in a very specific and measurable way. This opens the possibility of designing contrast agents with tailored properties, predicting their performance before they are even created in a lab.

It also helps illuminate why certain agents behave unexpectedly. For example, if water molecules linger in one region of a contrast agent more than predicted or interact with a shell in an unusual manner, the new model can reveal these subtleties. Such insights can guide safer formulations, improved diagnostic capabilities, and potentially new imaging techniques.

Beyond medicine, the mathematical backbone of the framework — particularly its use of the Fokker–Planck equation — is a reminder of how deep, fundamental physics continues to shape modern technology. The equation itself has been instrumental in the study of diffusion, Brownian motion, and statistical mechanics. By integrating this classical tool with modern computational techniques, the researchers have reshaped an important part of NMR theory.

While the model is highly promising, it is still early in its journey. Real biological tissues are incredibly complex environments with structures, boundaries, and interactions that go far beyond simple fluid systems. Future work will likely involve adapting and expanding the model to handle these richer environments. Still, the foundation is strong, and its potential impact is broad.

As medical imaging continues to evolve, breakthroughs like this help push the boundaries of what is possible — not through new machines or hardware, but through a deeper understanding of the physics that underlie some of the most important tools in modern medicine.

Research Paper:
https://pubs.aip.org/aip/jcp/article/163/18/184105/3371841/Extended-molecular-eigenmodes-treatment-of-dipole?utm_source=chatgpt.com