# Develop Multiphoton Magnetic Resonance Imaging

> **NIH NIH R21** · UNIVERSITY OF CALIFORNIA BERKELEY · 2021 · $179,154

## Abstract

Abstract
 Magnetic resonance imaging (MRI) is an indispensable non-invasive imaging technology for diagnostic
medicine and basic research. Today’s MRI assumes single-photon excitation. More specifically, for each nuclear
spin, a single photon accompanies the transition between energy states which creates MRI signal. This photon
must resonate near the Larmor frequency. The goal of this project is to explore and develop an entire new class
of MRI that utilizes multiphoton excitation. That is, instead of the usual single-photon resonance, the proposed
technique can excite multiphoton resonances to generate signal for MRI by using multiple magnetic field
frequencies, none of which are near the Larmor frequency. Only the total energy absorbed by a spin must
correspond to the Larmor frequency. Multiphoton MRI is a radical new way to perform MRI. It introduces new
flexibilities for excitation, encoding, contrast generation and reception. The project will explore and develop the
essential hardware, pulse sequences, excitation and receiving techniques for clinically feasible multiphoton MRI.
If successful, multiphoton MRI will pave the road for many new avenues to generate novel MRI images and
benefit human health.

## Key facts

- **NIH application ID:** 10217664
- **Project number:** 1R21EB030157-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** Chunlei Liu
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $179,154
- **Award type:** 1
- **Project period:** 2021-07-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10217664

## Citation

> US National Institutes of Health, RePORTER application 10217664, Develop Multiphoton Magnetic Resonance Imaging (1R21EB030157-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10217664. Licensed CC0.

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