Quantification correction for free-breathing myocardial T(1ρ) mapping in mice using a recursively derived description of a T(1ρ)* relaxation pathway

BACKGROUND: Fast and accurate T(1ρ) mapping in myocardium is still a major challenge, particularly in small animal models. The complex sequence design owing to electrocardiogram and respiratory gating leads to quantification errors in in vivo experiments, due to variations of the T(1ρ) relaxation pathway. In this study, we present an improved quantification method for T(1ρ) using a newly derived formalism of a T(1ρ)* relaxation pathway. METHODS: The new signal equation was derived by solving a recursion problem for spin-lock prepared fast gradient echo readouts. Based on Bloch simulations, we compared quantification errors using the common monoexponential model and our corrected model. The method was validated in phantom experiments and tested in vivo for myocardial T(1ρ) mapping in mice. Here, the impact of the breath dependent spin recovery time T(rec) on the quantification results was examined in detail. RESULTS: Simulations indicate that a correction is necessary, since systematically underestimated values are measured under in vivo conditions. In the phantom study, the mean quantification error could be reduced from − 7.4% to − 0.97%. In vivo, a correlation of uncorrected T(1ρ) with the respiratory cycle was observed. Using the newly derived correction method, this correlation was significantly reduced from r = 0.708 (p < 0.001) to r = 0.204 and the standard deviation of left ventricular T(1ρ) values in different animals was reduced by at least 39%. CONCLUSION: The suggested quantification formalism enables fast and precise myocardial T(1ρ) quantification for small animals during free breathing and can improve the comparability of study results. Our new technique offers a reasonable tool for assessing myocardial diseases, since pathologies that cause a change in heart or breathing rates do not lead to systematic misinterpretations. Besides, the derived signal equation can be used for sequence optimization or for subsequent correction of prior study results. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12968-022-00864-2.