Fast myocardial T1ρ mapping in mice using k-space weighted image contrast and a Bloch simulation-optimized radial sampling pattern

A new approach to characterize cardiac sodium storage by combining fluorescence photometry and magnetic resonance imaging in small animal research

Cardiac myocyte sodium (Na+) homoeostasis is pivotal in cardiac diseases and heart failure. Intracellular Na+ ([Na+]i) is an important regulator of excitation-contraction coupling and mitochondrial energetics. In addition, extracellular Na+ ([Na+]e) and its water-free storage trigger collagen cross-linking, myocardial stiffening and impaired cardiac function. Therefore, understanding the allocation of tissue Na+ to intra- and extracellular compartments is crucial in comprehending the pathophysiological processes in cardiac diseases. We extrapolated [Na+]e using a three-compartment model, with tissue Na+ concentration (TSC) measured by in vivo 23Na-MRI, extracellular volume (ECV) data calculated from T1 maps, and [Na+]i measured by in vitro fluorescence microscopy using Na+ binding benzofuran isophthalate (SBFI). To investigate dynamic changes in Na+ compartments, we induced pressure overload (TAC) or myocardial infarction (MI) via LAD ligation in mice. Compared to SHAM mice, TSC was similar after TAC but increased after MI. Both TAC and MI showed significantly higher [Na+]i compared to SHAM (around 130% compared to SHAM). Calculated [Na+]e increased after MI, but not after TAC. Increased TSC after TAC was primarily driven by increased [Na+]i, but the increase after MI by elevations in both [Na+]i and [Na+]e.

Robust cardiac T 1 ρ $$ {\mathrm{T}}_{1_{\boldsymbol{\rho}}} $$ mapping at 3T using adiabatic spin-lock preparations

PURPOSE

The aim of this study is to develop and optimize an adiabaticT 1 ρ $$ {\mathrm{T}}_{1\uprho} $$ (T 1 ρ , adiab $$ {\mathrm{T}}_{1\uprho, \mathrm{adiab}} $$ ) mapping method for robust quantification of spin-lock (SL) relaxation in the myocardium at 3T.

METHODS

Adiabatic SL (aSL) preparations were optimized for resilience againstB 0 $$ {\mathrm{B}}_0 $$ andB 1 + $$ {\mathrm{B}}_1^{+} $$ inhomogeneities using Bloch simulations. OptimizedB 0 $$ {\mathrm{B}}_0 $$ -aSL, Bal-aSL andB 1 $$ {\mathrm{B}}_1 $$ -aSL modules, each compensating for different inhomogeneities, were first validated in phantom and human calf. MyocardialT 1 ρ $$ {\mathrm{T}}_{1\uprho} $$ mapping was performed using a single breath-hold cardiac-triggered bSSFP-based sequence. Then, optimizedT 1 ρ , adiab $$ {\mathrm{T}}_{1\uprho, \mathrm{adiab}} $$ preparations were compared to each other and to conventional SL-preparedT 1 ρ $$ {\mathrm{T}}_{1\uprho} $$ maps (RefSL) in phantoms to assess repeatability, and in 13 healthy subjects to investigate image quality, precision, reproducibility and intersubject variability. Finally, aSL and RefSL sequences were tested on six patients with known or suspected cardiovascular disease and compared with LGE,T 1 $$ {\mathrm{T}}_1 $$ , and ECV mapping.

RESULTS

The highestT 1 ρ , adiab $$ {\mathrm{T}}_{1\uprho, \mathrm{adiab}} $$ preparation efficiency was obtained in simulations for modules comprising 2 HS pulses of 30 ms each. In vivoT 1 ρ , adiab $$ {\mathrm{T}}_{1\uprho, \mathrm{adiab}} $$ maps yielded significantly higher quality than RefSL maps. Average myocardialT 1 ρ , adiab $$ {\mathrm{T}}_{1\uprho, \mathrm{adiab}} $$ values were 183.28± $$ \pm $$ 25.53 ms, compared with 38.21± $$ \pm $$ 14.37 ms RefSL-preparedT 1 ρ $$ {\mathrm{T}}_{1\uprho} $$ .T 1 ρ , adiab $$ {\mathrm{T}}_{1\uprho, \mathrm{adiab}} $$ maps showed a significant improvement in precision (avg. 14.47± $$ \pm $$ 3.71% aSL, 37.61± $$ \pm $$ 19.42% RefSL, p < 0.01) and reproducibility (avg. 4.64± $$ \pm $$ 2.18% aSL, 47.39± $$ \pm $$ 12.06% RefSL, p < 0.0001), with decreased inter-subject variability (avg. 8.76± $$ \pm $$ 3.65% aSL, 51.90± $$ \pm $$ 15.27% RefSL, p < 0.0001). Among aSL preparations,B 0 $$ {\mathrm{B}}_0 $$ -aSL achieved the better inter-subject variability. In patients,B 1 $$ {\mathrm{B}}_1 $$ -aSL preparations showed the best artifact resilience among the adiabatic preparations.T 1 ρ , adiab $$ {\mathrm{T}}_{1\uprho, \mathrm{adiab}} $$ times show focal alteration colocalized with areas of hyper-enhancement in the LGE images.

CONCLUSION

Adiabatic preparations enable robust in vivo quantification of myocardial SL relaxation times at 3T.

SPICY: a method for single scan rotating frame relaxometry

T _1ρ is an NMR relaxation mode that is sensitive to low frequency molecular motions, making it an especially valuable tool in biomolecular research. Here, we introduce a new method, SPICY, for measuring T_1ρ relaxation times. In contrast to conventional T_1ρ experiments, in which the sequence is repeated many times to determine the T_1ρ time, the SPICY sequence allows determination of T_1ρ within a single scan, shortening the experiment time remarkably. We demonstrate the method using ¹H T_1ρ relaxation dispersion experiments. Additionally, we combine the sequence with spatial encoding to produce 1D images in a single scan. We show that T_1ρ relaxation times obtained using the single scan approach are in good agreement with those obtained using the traditional experiments.

Quantification correction for free-breathing myocardial T1ρ mapping in mice using a recursively derived description of a T1ρ* relaxation pathway

BACKGROUND

Fast and accurate T1ρ 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 T1ρ relaxation pathway. In this study, we present an improved quantification method for T1ρ using a newly derived formalism of a T1ρ* 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 T1ρ mapping in mice. Here, the impact of the breath dependent spin recovery time Trec 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 T1ρ 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 T1ρ values in different animals was reduced by at least 39%.

CONCLUSION

The suggested quantification formalism enables fast and precise myocardial T1ρ 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.

Noncontrast T1ρ dispersion imaging is sensitive to diffuse fibrosis: A cardiovascular magnetic resonance study at 3T in hypertrophic cardiomyopathy

PURPOSE

To determine the sensitivity of a noncontrast T1 dispersion cardiovascular magnetic resonance technique for detecting diffuse fibrosis in hypertrophic cardiomyopathy (HCM).

METHODS

Thirty-two adult HCM patients and ten age- and gender-matched healthy volunteers were prospectively included in this study. Patients and controls underwent cine, T1ρ-mapping, and pre- and post-contrast T1-mapping imaging using a 3-T magnetic resonance system. Myocardial extracellular volume fraction (ECV) maps were obtained using pre- and post-contrast T1 maps to determine reference values for diffuse fibrosis. Myocardial T1ρ and T1ρ dispersion maps called myocardial fibrosis index (mFI) maps provided 570 myocardial segments for Pearson or Spearman correlation analysis. The left ventricle myocardia of the HCM patients were divided into 16 segments that were further classified as either normal-thickness myocardium (<15 mm) (HCM-N) or hypertrophic myocardium (≥15 mm) (HCM-H).

RESULTS

ECV and mFI values increased progressively on a per-segment basis from healthy controls to the HCM-N group and then to the HCM-H group (ECV: 27.4 ± 2.8% vs. 31.1 ± 4.2% vs. 37.6 ± 6.9%, respectively [P < 0.0001]; mFI: 6.1 ± 0.9 ms vs. 8 ± 1.9 ms vs. 11 ± 3.3 ms, respectively [P < 0.0001]). There was a strong positive correlation between the segmented ECV and the mFI (r = 0.878). The mFI was equally or significantly better than the ECV for differentiating fibrosis content in HCM-N and HCM-H according to their receiver operating characteristic curves.

CONCLUSION

A T1ρ dispersion imaging mFI can sensitively detect diffuse myocardial fibrosis in HCM, even in HCM-N.