Multi-echo gradient echo pulse sequences: which is best for PRFS MR thermometry guided hyperthermia?

Abstract

Purpose

MR thermometry (MRT) enables noninvasive temperature monitoring during hyperthermia treatments. MRT is already clinically applied for hyperthermia treatments in the abdomen and extremities, and devices for the head are under development. In order to optimally exploit MRT in all anatomical regions, the best sequence setup and post-processing must be selected, and the accuracy needs to be demonstrated.

Methods

MRT performance of the traditionally used double-echo gradient-echo sequence (DE-GRE, 2 echoes, 2D) was compared to multi-echo sequences: a 2D fast gradient-echo (ME-FGRE, 11 echoes) and a 3D fast gradient-echo sequence (3D-ME-FGRE, 11 echoes). The different methods were assessed on a 1.5 T MR scanner (GE Healthcare) using a phantom cooling down from 59 °C to 34 °C and unheated brains of 10 volunteers. In-plane motion of volunteers was compensated by rigid body image registration. For the ME sequences, the off-resonance frequency was calculated using a multi-peak fitting tool. To correct for B0 drift, the internal body fat was selected automatically using water/fat density maps.

Results

The accuracy of the best performing 3D-ME-FGRE sequence was 0.20 °C in phantom (in the clinical temperature range) and 0.75 °C in volunteers, compared to DE-GRE values of 0.37 °C and 1.96 °C, respectively.

Conclusion

For hyperthermia applications, where accuracy is more important than resolution or scan-time, the 3D-ME-FGRE sequence is deemed the most promising candidate. Beyond its convincing MRT performance, the ME nature enables automatic selection of internal body fat for B0 drift correction, an important feature for clinical application.

Keywords:

• Thermometry
• thermal therapy
• temperature mapping
• MRT
• proton resonance frequency shift (PRFS)
• hyperthermia

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Acknowledgements

We would like to acknowledge Loes Priester for her help with making phantom, Samy Abo Seada for his code for automatic sorting DICOM images, Sergio Curto Ramos for his help setting up the phantom experiments and Iva Vilas Boas Ribeiro for her help setting up and conducting the volunteer experiments.

Ethical approval

The protocol used was approved by our institutional review board, protocol 'MRI technology healthy volunteers’ (MEC-2014-096).

Consent form

After having received an explanation of the study, all volunteers signed an informed consent.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 2D parameter file ‘parameters_rigid.txt’ and 3D parameter file ‘parameters_rigid_3D.txt’ at https://github.com/tfeddersen/ElastixModelZoo.

Additional information

Funding

This research is funded by the KWF project number 11368.