Understanding aqueous and non-aqueous proton T1 relaxation in brain

Manning, Alan P.; MacKay, Alex L.; Michal, Carl A.

doi:10.1016/j.jmr.2020.106909

Cited by 35 publications

(65 citation statements)

References 70 publications

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“…Conversely, we found that E[R 1 ] in WM is slightly lower than in GM, which on the other hand agrees with in vivo rat model-based diffusion-relaxation correlation [99]. However, besides the obvious futility of trying to compare quantitative relaxation measures from previous in vivo studies with our data on an ex vivo specimen at rather extreme levels of added contrast agent to reduce the scanning time, there are some more fundamental issues with attempting to compare quantitative R 1 from different studies or different pulse sequences as recently elucidated in detail by Manning et al [8]. The value of R 1 observed by varying some relaxation delay in the pulse sequence does not report on just the properties of the water molecules giving rise to signal intensity in the actually detected images, but instead results from a complex interplay between partial excitation by the radiofrequency pulses, relaxation, and exchange of molecules, protons, or magnetization between numerous proton pools, for instance intra/extracellular water, myelin water, nonaqueous myelin, and non-aqueous non-myelin protons, all having have their distinct NMR properties in terms of R 1 , R 2 , and linewidth.…”

Section: Discussionsupporting

confidence: 79%

“…, are sensitive to chemical exchange of protons between water and hydroxyl and amine groups on metabolites and macromolecules [6,7], molecular exchange between compartments [8], magnetization transfer/cross relaxation between exchangeable and non-exchangeable protons [9], and paramagnetic relaxation from iron or added contrast agents [8,10]. Especially for studies of the nervous system, the diffusion is often expressed in terms of a diffusion tensor D related to cell shapes and orientations [11].…”

Section: Introductionmentioning

confidence: 99%

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Massively Multidimensional Diffusion-Relaxation Correlation MRI

et al. 2022

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Diverse approaches such as oscillating gradients, tensor-valued encoding, and diffusion-relaxation correlation have been used to study microstructure and heterogeneity in healthy and pathological biological tissues. Recently, acquisition schemes with free gradient waveforms exploring both the frequency-dependent and tensorial aspects of the encoding spectrum b(ω) have enabled estimation of nonparametric distributions of frequency-dependent diffusion tensors. These “D(ω)-distributions” allow investigation of restricted diffusion for each distinct component resolved in the diffusion tensor trace, anisotropy, and orientation dimensions. Likewise, multidimensional methods combining longitudinal and transverse relaxation rates, R1 and R2, with (ω-independent) D-distributions capitalize on the component resolution offered by the diffusion dimensions to investigate subtle differences in relaxation properties of sub-voxel water populations in the living human brain, for instance nerve fiber bundles with different orientations. By measurements on an ex vivo rat brain, we here demonstrate a “massively multidimensional” diffusion-relaxation correlation protocol joining all the approaches mentioned above. Images acquired as a function of the magnitude, normalized anisotropy, orientation, and frequency content of b(ω), as well as the repetition time and echo time, yield nonparametric D(ω)-R1-R2-distributions via a Monte Carlo data inversion algorithm. The obtained per-voxel distributions are converted to parameter maps commonly associated with conventional lower-dimensional methods as well as unique statistical descriptors reporting on the correlations between restriction, anisotropy, and relaxation.

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Section: Discussionsupporting

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Massively Multidimensional Diffusion-Relaxation Correlation MRI

et al. 2022

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“…We have presented a generalization of the Bloch model to nonexponential decays or, equivalently, to non-Lorentzian lineshapes. We derived and tested the model in the context of pulsed magnetization transfer 1,2,10 and demonstrated that the generalized Bloch model-in contrast to existing pulsed-MT models 10,11 -unifies the original Bloch model and Henkelman's steady-state model, while also adequately describing the rotation induced by short RF-pulses 12 (cf. Fig.…”

Section: Discussionmentioning

confidence: 99%

“…In order to describe magnetization transfer, we incorporate the generalized Bloch equations into a 2-pool model. We choose this model over the more accurate 4-pool model described by Manning et al 12 because it is difficult to determine the large number of parameters of the 4-pool model in vivo due to experimental constraints. However, the generalized Bloch model can be incorporated in models with an arbitrary number of pools.…”

Section: Magnetization Transfer To and From The Free Poolmentioning

confidence: 99%

“…a strong and infinitesimally short application of RF field, they are not equivalent to a rotation by the appropriate flip angle. 12 This paper aims to overcome these shortcomings with a classical theory that generalizes the Bloch equations to arbitrary lineshapes to capture the macroscopic effect of dissipative dipole-dipole interactions in large molecule structures.…”

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confidence: 99%

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Generalized Bloch model: a theory for pulsed magnetization transfer

Assländer,

Gultekin,

Flassbeck

et al. 2021

Preprint

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We developed a classical model to describe the dynamics of large spin-1/2 ensembles associated with nuclei bound in large molecule structures, commonly referred to as the semi-solid spin pool, and their magnetization transfer (MT) to spins of nuclei in water. Like quantummechanical descriptions of spin dynamics and like the original Bloch equations, but unlike existing MT models, the proposed model is based on the algebra of angular momentum in the sense that it explicitly models the rotations induced by radio-frequency (RF) pulses. It generalizes the original Bloch model to non-exponential decays, which are, e.g., observed for semi-solid spin pools. The combination of rotations with non-exponential decays is facilitated by describing the latter as Green's functions. The resulting model is expressed as an integro-differential equation that unifies the original Bloch model, Henkelman's steadystate theory for magnetization transfer, and the commonly assumed rotation induced by hard pulses (i.e., strong and infinitesimally short applications of RF fields). Our model describes the data of an inversion-recovery magnetization-transfer experiment with varying durations of the inversion pulse substantially better than established models. Furthermore, we provide a linear approximation of the generalized Bloch model that reduces the simulation time by approximately a factor 15,000, enabling simulation of the spin dynamics caused by a rectangular RF-pulse in roughly 2µs.

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In vivo disentanglement of diffusion frequency‐dependence, tensor shape, and relaxation using multidimensional MRI

Johnson,

Irfanoglu,

Manninen

et al. 2024

Human Brain Mapping

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Diffusion MRI with free gradient waveforms, combined with simultaneous relaxation encoding, referred to as multidimensional MRI (MD‐MRI), offers microstructural specificity in complex biological tissue. This approach delivers intravoxel information about the microstructure, local chemical composition, and importantly, how these properties are coupled within heterogeneous tissue containing multiple microenvironments. Recent theoretical advances incorporated diffusion time dependency and integrated MD‐MRI with concepts from oscillating gradients. This framework probes the diffusion frequency, , in addition to the diffusion tensor, , and relaxation, , , correlations. A clinical imaging protocol was then introduced, with limited brain coverage and 3 mm3 voxel size, which hinder brain segmentation and future cohort studies. In this study, we introduce an efficient, sparse in vivo MD‐MRI acquisition protocol providing whole brain coverage at 2 mm3 voxel size. We demonstrate its feasibility and robustness using a well‐defined phantom and repeated scans of five healthy individuals. Additionally, we test different denoising strategies to address the sparse nature of this protocol, and show that efficient MD‐MRI encoding design demands a nuanced denoising approach. The MD‐MRI framework provides rich information that allows resolving the diffusion frequency dependence into intravoxel components based on their distribution, enabling the creation of microstructure‐specific maps in the human brain. Our results encourage the broader adoption and use of this new imaging approach for characterizing healthy and pathological tissues.

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Understanding aqueous and non-aqueous proton T1 relaxation in brain

Cited by 35 publications

References 70 publications

Massively Multidimensional Diffusion-Relaxation Correlation MRI

Massively Multidimensional Diffusion-Relaxation Correlation MRI

Generalized Bloch model: a theory for pulsed magnetization transfer

In vivo disentanglement of diffusion frequency‐dependence, tensor shape, and relaxation using multidimensional MRI

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