Unconstrained quantitative magnetization transfer imaging: disentangling T1 of the free and semi-solid spin pools
arXiv:2301.08394 · doi:10.1162/imag_a_00177
Abstract
Since the inception of magnetization transfer (MT) imaging, it has been widely assumed that Henkelman's two spin pools have similar longitudinal relaxation times, which motivated many researchers to constrain them to each other. However, several recent publications reported a of the semi-solid spin pool that is much shorter than of the free pool. While these studies tailored experiments for robust proofs-of-concept, we here aim to quantify the disentangled relaxation processes on a voxel-by-voxel basis in a clinical imaging setting, i.e., with an effective resolution of 1.24mm isotropic and full brain coverage in 12min. To this end, we optimized a hybrid-state pulse sequence for mapping the parameters of an unconstrained MT model. We scanned four people with relapsing-remitting multiple sclerosis (MS) and four healthy controls with this pulse sequence and estimated s and s in healthy white matter. Our results confirm the reports that and we argue that this finding identifies MT as an inherent driver of longitudinal relaxation in brain tissue. Moreover, we estimated a fractional size of the semi-solid spin pool of , which is larger than previously assumed. An analysis of in normal-appearing white matter revealed statistically significant differences between individuals with MS and controls.
References in corpus (14)
- Automated Design of Deep Learning Methods for Biomedical Image Segmentation
- On the Variance of the Adaptive Learning Rate and Beyond
- SynthStrip: Skull-Stripping for Any Brain Image
- MR fingerprinting Deep RecOnstruction NEtwork (DRONE)
- Low Rank Alternating Direction Method of Multipliers Reconstruction for MR Fingerprinting
- Optimal Experiment Design for Magnetic Resonance Fingerprinting: Cramér-Rao Bound Meets Spin Dynamics
- OEDIPUS: An Experiment Design Framework for Sparsity-Constrained MRI
- Hybrid-State Free Precession in Nuclear Magnetic Resonance
- Dictionary-Free MRI PERK: Parameter Estimation via Regression with Kernels
- Optimized Quantification of Spin Relaxation Times in the Hybrid State
- Cramér-Rao bound-informed training of neural networks for quantitative MRI
- Generalized Bloch model: a theory for pulsed magnetization transfer
- Rapid quantitative magnetization transfer imaging: utilizing the hybrid state and the generalized Bloch model
- Bias-Reduced Neural Networks for Parameter Estimation in Quantitative MRI