Joint modeling of diffusion and relaxation has seen growing interest due to its potential to provide complementary information about tissue microstructure. For brain white matter, we designed an optimal diffusion-relaxometry MRI protocol that samples multiple b-values, B-tensor shapes, and echo times (TE). This variable-TE protocol (27 min) has as subsets a fixed-TE protocol (15 min) and a 2-shell dMRI protocol (7 min), both characterizing diffusion only. We assessed the sensitivity, specificity and reproducibility of these protocols with synthetic experiments and in six healthy volunteers. Compared with the fixed-TE protocol, the variable-TE protocol enables estimation of free water fractions while also capturing compartmental T_2 relaxation times. Jointly measuring diffusion and relaxation offers increased sensitivity and specificity to microstructure parameters in brain white matter with voxelwise coefficients of variation below 10

Diffusion magnetic resonance imaging offers unique in vivo sensitivity to tissue microstructure in brain white matter, which undergoes significant changes during development and is compromised in virtually every neurological disorder. Yet, the challenge is to develop biomarkers that are specific to micrometer-scale cellular features in a human MRI scan of a few minutes. Here we quantify the sensitivity and specificity of a multicompartment diffusion modeling framework to the density, orientation and integrity of axons. We demonstrate that using a machine learning based estimator, our biophysical model captures the morphological changes of axons in early development, acute ischemia and multiple sclerosis (total N=821). The methodology of microstructure mapping is widely applicable in clinical settings and in large imaging consortium data to study development, aging and pathology.

Biophysical modeling of diffusion MRI (dMRI) offers the exciting potential of bridging the gap between the macroscopic MRI resolution and microscopic cellular features, effectively turning the MRI scanner into a noninvasive in vivo microscope. In brain white matter, the Standard Model (SM) interprets the dMRI signal in terms of axon dispersion, intra- and extra-axonal water fractions and diffusivities. However, for SM to be fully applicable and correctly interpreted, it needs to be carefully evaluated using histology. Here, we perform a comprehensive histological validation of the SM parameters, by characterizing WM microstructure in sham and injured rat brains using volume (3d) electron microscopy (EM) and ex vivo dMRI. Sensitivity is evaluated by how close each SM metric is to its histological counterpart, and specificity by how independent it is from other, non-corresponding histological features. This comparison reveals that SM is sensitive and specific to microscopic properties, clearing the way for the clinical adoption of in vivo dMRI derived SM parameters as biomarkers for neurological disorders.

In the Funding information, the Grant/Award number of the first funding should be changed from 2017YFC010802 to 2017YFC0108702. The updated funding information section appears below: National Key R&D Program of China, Grant/Award Number: 2017YFC0108702; National Natural Science Foundation of China, Grants/Award Numbers: 81371540, 81571667, and 61801026. We regret this error in our manuscript. The authors do not have any conflicts of interest to report. Research reported in this publication was supported by the National Key R&D Program of China, Grant/Award Number: 2017YFC0108702; National Natural Science Foundation of China, Grants/Award Numbers: 81371540, 81571667, and 61801026.

PurposeTo develop a reproducible and fast method to reconstruct MR fingerprinting arterial spin labeling (MRF‐ASL) perfusion maps using deep learning.MethodA fully connected neural network, denoted as DeepMARS, was trained using simulation data and added Gaussian noise. Two MRF‐ASL models were used to generate the simulation data, specifically a single‐compartment model with 4 unknowns parameters and a two‐compartment model with 7 unknown parameters. The DeepMARS method was evaluated using MRF‐ASL data from healthy subjects (N = 7) and patients with Moymoya disease (N = 3). Computation time, coefficient of determination (R2), and intraclass correlation coefficient (ICC) were compared between DeepMARS and conventional dictionary matching (DM). The relationship between DeepMARS and Look–Locker PASL was evaluated by a linear mixed model.ResultsComputation time per voxel was <0.5 ms for DeepMARS and >4 seconds for DM in the single‐compartment model. Compared with DM, the DeepMARS showed higher R2 and significantly improved ICC for single‐compartment derived bolus arrival time (BAT) and two‐compartment derived cerebral blood flow (CBF) and higher or similar R2/ICC for other parameters. In addition, the DeepMARS was significantly correlated with Look–Locker PASL for BAT (single‐compartment) and CBF (two‐compartment). Moreover, for Moyamoya patients, the location of diminished CBF and prolonged BAT shown in DeepMARS was consistent with the position of occluded arteries shown in time‐of‐flight MR angiography.ConclusionReconstruction of MRF‐ASL with DeepMARS is faster and more reproducible than DM.

Motivation: Currently, brain charts index gray matter brain volume from T1-weighted MRI, whose sensitivity is limited to millimeter resolution, thereby unable to probe early signs of aging and pathology at the cellular level. Goal(s): To introduce normative data for diffusion MRI (dMRI) and apply it to multiple sclerosis (MS) patients to evaluate sensitivity and accuracy. Approach: We created normative data using diffusion tensor, diffusion kurtosis, and standard model imaging metrics in white matter. Then, assessed MS subjects by comparing to these normative data. Results: dMRI metrics from MS patients deviate from normative data, suggesting brain charts may be used to benchmark brain health. Impact: This study is the first step to achieve a brain-age framework from clinically feasible dMRI scans that provides meaningful insight into microstructural processes underlying brain aging and disease– possibly enabling quantitative assessment of treatment response to future disease-modifying therapies.

Multiple sclerosis (MS) is a neurodegenerative and inflammatory disease characterized by focal lesions and damages to normal appearing white matter (NAWM). Diffusion MRI (dMRI) is known for its sensitivity to the microstructural changes in white matter. In this work, we study the sensitivity of dMRI metrics to MS pathology in NAWM by distinguishing MS patients from healthy controls. We found that beyond-spherical-mean rotational invariants of high-b shells contribute mostly to the classification, indicating nontrivial information content of high-b diffusion signal beyond DTI.

Combining diffusion and relaxation is promising in probing the tissue microstructure in brain white matter. We designed an optimal diffusion-relaxation protocol with varying b-values, b-tensor shapes and echo times by minimizing the error of parameter estimation. We compared the Standard Model parameters estimated by diffusion-relaxation and diffusion-only data using sensitivity-specificity matrices and test-retest data on volunteers and found excellent agreement. With a comprehensive acquisition protocol, the Standard Model can accurately capture the signal content in white matter with diffusion MRI.

Conventional diffusion MRI (dMRI) techniques, such as DTI and DKI, are sensitive to pathology but lack specificity. In brain white matter, the “Standard Model” framework of dMRI may provide specificity to microstructural changes. Generally, clinical dMRI is noisy and limited, making SM estimation challenging. Thus, different constraints and techniques have been introduced to robustly extract SM parametric maps. Here, we employ a large clinical dataset of Multiple Sclerosis patient data (N = 134) and noise propagation experiments to study the sensitivity and specificity of these techniques.

Biophysical modeling of diffusion MRI is instrumental in achieving specificity to tissue microstructure in human white matter. The “Standard Model” (SM) framework encompasses many approaches assuming multiple Gaussian compartments. To robustly estimate SM parameters, different constraints and techniques have been applied, resulting in different outcomes. Here we evaluate the precision and accuracy of commonly used implementations and constraints for SM parameter estimation, and compare their results both in simulations and in early human brain development.