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Longitudinal relaxation rate ( R 1 ) in lung: a systematic review and meta-analysis
Lucy Edwards1, Geoff J M Parker2,3, John C Waterton1,4
1Bioxydyn Limited, St James Tower, 7 Charlotte Street, Manchester, M1 4DZ, UK.
Magma (New York, N.Y.)
|May 16, 2025
Summary
Lung magnetic resonance longitudinal relaxation rate (R1) values vary significantly across studies. This research provides reference values and highlights methodological differences as the likely cause of R1 variability in healthy subjects.
Area of Science:
- Medical imaging
- Magnetic Resonance Imaging (MRI)
- Lung physiology
Background:
- The lung magnetic resonance longitudinal relaxation rate (R1) is a key biomarker that varies with disease.
- Reported R1 values in healthy subjects differ significantly across studies, hindering comparisons.
Purpose of the Study:
- To evaluate reported lung R1 values and their variances.
- To assess the influence of magnetic field strength, acquisition methods, and disease on R1 measurements.
- To establish reference values for lung R1 across various field strengths.
Main Methods:
- Systematic literature search for studies quantifying lung R1 or its reciprocal.
- Analysis of the relationship between field strength and R1 in healthy subjects.
- Fitting data to a heuristic equation and assessing variance components (between-study, within-study-between-subject, within-study-within-subject).
- Investigation of the correlation between R1 and echo time (TE).
Main Results:
- Fifty-nine papers were analyzed, including data from 1559 subjects across a field strength range of 0.2 T to 9.4 T.
- Reference values for lung R1 were derived for field strengths from 0.55 T to 7 T.
- A negative relationship between R1 and TE was confirmed for inversion recovery with gradient echo readout at 1.5 T.
Conclusions:
- Reference values for lung R1 are provided across common field strengths.
- Methodological differences between studies are identified as the primary cause for the observed variability in healthy subject lung R1.
- Standardization of methods in multicenter projects is recommended to minimize between-center variance and improve reproducibility.
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