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

Abstract

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.

Sensitivity of unconstrained quantitative magnetization transfer MRI to Amyloid burden in preclinical Alzheimer's disease

“…The largest effect was observed in the semi-solid pool’s longitudinal relaxation rate , a parameter previously considered inaccessible and hence typically constrained—but which we quantify on a voxel-by-voxel basis using the hybrid-state’s enhanced signal encoding capabilities. 13,15,41 This suggests that may be a potential biomarker for amyloid pathology preceding morphometric measures of atrophy. 39,40 A potential biophysical explanation could be the slower longitudinal relaxation of spins bound in Aβ plaques relative to other constituents of the macromolecular pool, e.g., myelin.…”

“…This parameter was previously considered inaccessible and typically constrained. However, using the hybrid-state's enhanced signal encoding capabilities (Assländer, Gultekin, et al, 2024;Assländer et al, 2019), we are able to quantify R s 1 on a voxel-by-voxel basis. Our data suggests that R s 1 may be a potential biomarker for amyloid pathology preceding morphometric measures of atrophy (Dickerson et al, 2011;Sabuncu et al, 2011).…”

“…Zhang et al (2022): 11 fully connected layers with batch normalization and skip connections with a maximum layer width of 1024. The network incorporated a data-driven correction for B 0 and B + 1 inhomogeneities as described in (Assländer, Gultekin, et al, 2024), and was trained explicitly to minimize the bias that is typically introduced when assuming a distribution for the simulated training data (Mao et al, 2024). Example parameter maps for an Aβ+ subject are shown in Figure 2.…”