Optimization of multidimensional MR data acquisition for relaxation and diffusion

Adaptive control for downhole nuclear magnetic resonance excitation

Nuclear magnetic resonance (NMR) measurements are performed with the pulse sequence and acquisition parameters set by the operator, which cannot be adjusted in real time according to sample characteristics. In one acquisition cycle, usually thousands of high-power pulses are transmitted and thousands of echo points are acquired. The power consumption cause the RF amplifier to overheat, and large amounts of acquired data may be invalid. Therefore, the optimization of excitation and acquisition processes is necessary to improve measurement efficiency. We explore a scheme for the real-time measurement of the samples by adaptively regulating the pulse sequence, which adapts the variable TE pulse sequence as the reconnaissance mode. The appropriate pulse sequence and reasonable parameters (NE, TE) can be selected according to the relaxation characteristics of the samples.This adaptive control strategy has great significance in guiding both dynamic and static measurements, and it is especially suitable for occasions where low magnetic field gradients and diffusion terms can be ignored. We also design a test circuit for adaptive control, which has the function of automatic parameter adjustment. By adjusting parameters such as the number of refocusing pulses, echo spacing, etc., the effective measurement of the samples can be achieved in practice.

Stabilization of parameter estimates from multiexponential decay through extension into higher dimensions

Analysis of multiexponential decay has remained a topic of active research for over 200 years. This attests to the widespread importance of this problem and to the profound difficulties in characterizing the underlying monoexponential decays. Here, we demonstrate the fundamental improvement in stability and conditioning of this classic problem through extension to a second dimension; we present statistical analysis, Monte-Carlo simulations, and experimental magnetic resonance relaxometry data to support this remarkable fact. Our results are readily generalizable to higher dimensions and provide a potential means of circumventing conventional limits on multiexponential parameter estimation.

Comparison of experimental times in T1-D and D-T2 correlation experiments in single-sided NMR

Diffusion-relaxation correlation experiments in nuclear magnetic resonance are a powerful technique for the characterization of fluid dynamics in confined geometries or soft matter, in which relaxation may be either spin-spin (T₂) or spin-lattice (T₁). The general approach is to acquire a set of bidimensional data in which diffusion is codified by the evolution of the magnetization with either Hahn or stimulated echoes (STE) in the presence of a constant magnetic field gradient. While T₂ is codified by a Carr-Purcell-Meiboom-Gil (CPMG) sequence, T₁ is either encoded by saturation or inversion-recovery methods. In this work, we analyse the measurement time of diffusion-relaxation times in single-sided NMR and show that T₁-D acquisition is always shorter than D-T₂. Depending on the hardware characteristics, this time reduction can be up to an order of magnitude. We present analytical calculations and examples in model porous media saturated with water and in a dairy product.

Nonparametric D-R1-R2 distribution MRI of the living human brain

Diffusion-relaxation correlation NMR can simultaneously characterize both the microstructure and the local chemical composition of complex samples that contain multiple populations of water. Recent developments on tensor-valued diffusion encoding and Monte Carlo inversion algorithms have made it possible to transfer diffusion-relaxation correlation NMR from small-bore scanners to clinical MRI systems. Initial studies on clinical MRI systems employed 5D D-R₁ and D-R₂ correlation to characterize healthy brain in vivo. However, these methods are subject to an inherent bias that originates from not including R₂ or R₁ in the analysis, respectively. This drawback can be remedied by extending the concept to 6D D-R₁-R₂ correlation. In this work, we present a sparse acquisition protocol that records all data necessary for in vivo 6D D-R₁-R₂ correlation MRI across 633 individual measurements within 25 min-a time frame comparable to previous lower-dimensional acquisition protocols. The data were processed with a Monte Carlo inversion algorithm to obtain nonparametric 6D D-R₁-R₂ distributions. We validated the reproducibility of the method in repeated measurements of healthy volunteers. For a post-therapy glioblastoma case featuring cysts, edema, and partially necrotic remains of tumor, we present representative single-voxel 6D distributions, parameter maps, and artificial contrasts over a wide range of diffusion-, R₁-, and R₂-weightings based on the rich information contained in the D-R₁-R₂ distributions.

Combined diffusion-relaxometry microstructure imaging: Current status and future prospects

Microstructure imaging seeks to noninvasively measure and map microscopic tissue features by pairing mathematical modeling with tailored MRI protocols. This article reviews an emerging paradigm that has the potential to provide a more detailed assessment of tissue microstructure-combined diffusion-relaxometry imaging. Combined diffusion-relaxometry acquisitions vary multiple MR contrast encodings-such as b-value, gradient direction, inversion time, and echo time-in a multidimensional acquisition space. When paired with suitable analysis techniques, this enables quantification of correlations and coupling between multiple MR parameters-such as diffusivity, T 1 , T 2 , and T 2 ∗ . This opens the possibility of disentangling multiple tissue compartments (within voxels) that are indistinguishable with single-contrast scans, enabling a new generation of microstructural maps with improved biological sensitivity and specificity.

Toward nonparametric diffusion- T1 characterization of crossing fibers in the human brain

PURPOSE

To estimate T 1 for each distinct fiber population within voxels containing multiple brain tissue types.

METHODS

A diffusion- T 1 correlation experiment was carried out in an in vivo human brain using tensor-valued diffusion encoding and multiple repetition times. The acquired data were inverted using a Monte Carlo algorithm that retrieves nonparametric distributions P ( D , R 1 ) of diffusion tensors and longitudinal relaxation rates R 1 = 1 / T 1 . Orientation distribution functions (ODFs) of the highly anisotropic components of P ( D , R 1 ) were defined to visualize orientation-specific diffusion-relaxation properties. Finally, Monte Carlo density-peak clustering (MC-DPC) was performed to quantify fiber-specific features and investigate microstructural differences between white matter fiber bundles.

RESULTS

Parameter maps corresponding to P ( D , R 1 ) 's statistical descriptors were obtained, exhibiting the expected R 1 contrast between brain tissue types. Our ODFs recovered local orientations consistent with the known anatomy and indicated differences in R 1 between major crossing fiber bundles. These differences, confirmed by MC-DPC, were in qualitative agreement with previous model-based works but seem biased by the limitations of our current experimental setup.

CONCLUSIONS

Our Monte Carlo framework enables the nonparametric estimation of fiber-specific diffusion- T 1 features, thereby showing potential for characterizing developmental or pathological changes in T 1 within a given fiber bundle, and for investigating interbundle T 1 differences.