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Updated: Sep 19, 2025

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
Published on: December 18, 2016
Steady-state free precession for T2* relaxometry: All echoes in every readout with k-space aliasing
Peter J Lally1,2,3, Yifei Jin1, Zimu Huo1
1Department of Bioengineering, Imperial College London, London, UK.
This study introduces a new magnetic resonance imaging (MRI) method using N-periodic steady-state free precession (SSFP) sequences for faster relaxometry. The technique simultaneously captures multiple echoes, offering an efficient alternative to traditional multi-echo gradient echo sequences.
Area of Science:
- Magnetic Resonance Imaging (MRI)
- Biomedical Engineering
- Medical Physics
Background:
- Multi-echo gradient echo imaging is vital for applications like relaxometry and susceptibility mapping.
- Conventional methods often use long-TR FLASH sequences, acquiring one echo per readout.
- This sequential acquisition can limit temporal resolution in dynamic studies.
Purpose of the Study:
- To propose an alternative strategy for simultaneous multi-echo acquisition using N-periodic SSFP sequences.
- To enable efficient, short-TR relaxometry by measuring all signal components in a single readout.
- To compare the proposed method with traditional multi-echo FLASH for R2* relaxometry.
Main Methods:
- Utilized N-periodic SSFP sequences to simultaneously record multiple echoes.
- Separated echoes based on differing phase evolution across multiple TRs.
- Compared R2* relaxometry results from the proposed method and multi-echo FLASH in phantoms and volunteers.
Main Results:
- The proposed SSFP method demonstrated close agreement with multi-echo FLASH for R2* estimation.
- Achieved more rapid temporal sampling compared to conventional sequential echo acquisition.
- Validated the approach in both phantom and human volunteer studies.
Conclusions:
- The simultaneous multi-echo SSFP approach is a promising alternative for relaxometry.
- It offers efficient data acquisition by measuring multiple echo pathways concurrently.
- The method relies on a simple analytical model for signal separation.
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