# MR thermometry with high precision and temporal resolution by quadratic phase MR fingerprinting

## TL;DR

This paper introduces a new MRI technique called qRF-MRF that improves temperature mapping precision and speed compared to traditional methods.

## Contribution

The novel method uses quadratic RF excitation phase increments in MRF to achieve high-precision temperature imaging.

## Key findings

- qRF-MRF reduced temperature standard deviation by 85% in phantom simulations compared to 2DFT GRE.
- The technique enabled reconstruction of linewidth maps and closely matched 2DFT results in FUS heating experiments.
- CG reconstruction improved dictionary match and reduced temperature standard deviation by 30% compared to gridding.

## Abstract

To map temperature via the proton resonance frequency‐ (PRF‐) shift with a high frame rate and high precision using quadratic RF excitation phase‐magnetic resonance fingerprinting (qRF‐MRF).

A continuous balanced qRF‐MRF sequence was implemented using a constant low‐flip‐angle excitation with quadratic RF excitation phase increments, which impart sensitivity to resonance frequency changes from heating by repeatedly sweeping the sequence's resonance frequency between −1/(2TR) and +1/(2TR) Hz, while minimizing sensitivity to T1 and T2. Temperature maps were reconstructed from sliding windows using the conjugate gradient (CG) algorithm, dictionary matching, and conventional PRF temperature calculations using MRF‐synthesized gradient‐recalled echo (GRE) images. Monte Carlo simulations were performed to optimize the sequence. qRF‐MRF temperature precision was compared to acquisition time‐matched 2DFT GRE temperature maps at 3 Tesla in simulations, phantom imaging, and in vivo imaging. The ability to image dynamic temperature changes was validated in a phantom‐focused ultrasound (FUS) heating experiment.

Compared to 2DFT GRE, the optimized qRF‐MRF sequence achieved an 85% reduction in temperature standard deviation in phantom simulation (0.092 vs. 0.014), 71% reduction in phantom imaging (0.065 vs. 0.019), and 55% reduction in vivo (0.321 vs. 0.147). CG MRF reconstruction improved dictionary match inner products ∼ 2× and reduced temperature standard deviation 30% compared to gridding. In FUS heating, qRF‐MRF‐reconstructed heating pattern and temperature curve closely matched the 2DFT results. qRF‐MRF also enabled the reconstruction of linewidth maps.

Continuous low‐flip‐angle qRF‐MRF is capable of temperature imaging using the PRF shift with similar frame rates but higher precision than conventional GRE thermometry.