Quantitative magnetization transfer MRI unbiased by on‐resonance saturation and dipolar order contributions

PurposeMagnetization transfer saturation (MTsat) mapping is commonly used to examine the macromolecular content of brain tissue. This study compared variable flip angle (VFA) T1 mapping against compressed‐sensing MP2RAGE (csMP2RAGE) T1 mapping for accelerating MTsat imaging.MethodsVFA, MP2RAGE, and csMP2RAGE were compared against inversion‐recovery T1 in an aqueous phantom at 3 T. The same 1‐mm VFA, MP2RAGE, and csMP2RAGE protocols were acquired in 4 healthy subjects to compare T1 and MTsat. Bloch‐McConnell simulations were used to investigate differences between the phantom and in vivo T1 results. Ten healthy controls were imaged twice with the csMP2RAGE MTsat protocol to quantify repeatability.ResultsThe MP2RAGE and csMP2RAGE protocols were 13.7% and 32.4% faster than the VFA protocol, respectively. At these scan times, all approaches provided strong repeatability and accurate T1 times (< 5% difference) in the phantom, but T1 accuracy was more impacted by T2 for VFA than for MP2RAGE. In vivo, VFA estimated longer T1 times than MP2RAGE and csMP2RAGE. Simulations suggest that the differences in the T1 measured using VFA, MP2RAGE, and inversion recovery could be explained by the magnetization‐transfer effects. In the test–retest experiment, we found that the csMP2RAGE has a minimum detectable change of 2.3% for T1 mapping and 7.8% for MTsat imaging.ConclusionsWe demonstrated that MP2RAGE can be used in place of VFA T1 mapping in an MTsat protocol. Furthermore, a shorter scan time and high repeatability can be achieved using the csMP2RAGE sequence.

Section: Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fast magnetization transfer saturation imaging of the brain using MP2RAGE T₁ mapping

Rowley,

Nelson,

Campbell

et al. 2024

Rapid quantitative magnetization transfer imaging: Utilizing the hybrid state and the generalized Bloch model

Assländer,

Gultekin,

Mao

et al. 2023

PurposeTo explore efficient encoding schemes for quantitative magnetization transfer (qMT) imaging with few constraints on model parameters.Theory and MethodsWe combine two recently proposed models in a Bloch‐McConnell equation: the dynamics of the free spin pool are confined to the hybrid state, and the dynamics of the semi‐solid spin pool are described by the generalized Bloch model. We numerically optimize the flip angles and durations of a train of radio frequency pulses to enhance the encoding of three qMT parameters while accounting for all eight parameters of the two‐pool model. We sparsely sample each time frame along this spin dynamics with a three‐dimensional radial koosh‐ball trajectory, reconstruct the data with subspace modeling, and fit the qMT model with a neural network for computational efficiency.ResultsWe extracted qMT parameter maps of the whole brain with an effective resolution of 1.24 mm from a 12.6‐min scan. In lesions of multiple sclerosis subjects, we observe a decreased size of the semi‐solid spin pool and longer relaxation times, consistent with previous reports.ConclusionThe encoding power of the hybrid state, combined with regularized image reconstruction, and the accuracy of the generalized Bloch model provide an excellent basis for efficient quantitative magnetization transfer imaging with few constraints on model parameters.

Testing quantitative magnetization transfer models with membrane lipids

Shtangel,

Mezer

2024

PurposeQuantitative magnetization transfer (qMT) models aim to quantify the contributions of lipids and macromolecules to the MRI signal. Hence, a model system that relates qMT parameters and their molecular sources may improve the interpretation of the qMT parameters. Here we used membrane lipid phantoms as a meaningful tool to study qMT models. By controlling the fraction and type of membrane lipids, we could test the accuracy, reliability, and interpretability of different qMT models.MethodsWe formulated liposomes with various lipid types and water‐to‐lipids fractions and measured their signals with spoiled gradient‐echo MT. We fitted three known qMT models and estimated six parameters for every model. We tested the accuracy and reproducibility of the models and compared the dependency among the qMT parameters. We compared the samples' qMT parameters with their water‐to‐lipid fractions and with a simple MTnorm (= MTon/MToff) calculation.ResultsWe found that the three qMT models fit the membrane lipids signals well. We also found that the estimated qMT parameters are highly interdependent. Interestingly, the estimated qMT parameters are a function of the membrane lipid type and also highly related to the water‐to‐lipid fraction. Finally, we find that most of the lipid sample's information can be captured using the common and easy to estimate MTnorm analysis.ConclusionqMT parameters are sensitive to both the water‐to‐lipid fraction and to the lipid type. Estimating the water‐to‐lipid fraction can improve the characterization of membrane lipids' contributions to qMT parameters. Similar characterizations can be obtained using the MTnorm analysis.