Quantum dipole interactions and transient hydrogen bond orientation order in cells, cellular membranes and myelin sheath: Implications for MRI signal relaxation, anisotropy, and T1 magnetic field dependence.

PURPOSE: Despite significant impact on the study of human brain, MRI lacks a theory of signal formation that integrates quantum interactions involving proton dipoles (a primary MRI signal source) with brain intricate cellular environment. The purpose of the present study is developing such a theory. METHODS: We introduce the Transient Hydrogen Bond (THB) model, where THB-mediated quantum dipole interactions between water and protons of hydrophilic heads of amphipathic biomolecules forming cells, cellular membranes and myelin sheath serve as a major source of MR signal relaxation. RESULTS: The THB theory predicts the existence of a hydrogen-bond-driven structural order of dipole-dipole connections within THBs as a primary factor for the anisotropy observed in MRI signal relaxation. We have also demonstrated that the conventional Lorentzian spectral density function decreases too fast at high frequencies to adequately capture the field dependence of brain MRI signal relaxation. To bridge this gap, we introduced a stretched spectral density function that surpasses the limitations of Lorentzian dispersion. In human brain, our findings reveal that at any time point only about 4% to 7% of water protons are engaged in quantum encounters within THBs. These ultra-short (2 to 3 ns), but frequent quantum spin exchanges lead to gradual recovery of magnetization toward thermodynamic equilibrium, that is, relaxation of MRI signal. CONCLUSION: By incorporating quantum proton interactions involved in brain imaging, the THB approach introduces new insights on the complex relationship between brain tissue cellular structure and MRI measurements, thus offering a promising new tool for better understanding of brain microstructure in health and disease.

Deep learning-based Accelerated and Noise-Suppressed Estimation (DANSE) of quantitative Gradient-Recalled Echo (qGRE) magnetic resonance imaging metrics associated with human brain neuronal structure and hemodynamic properties.

The purpose of the current study was to introduce a Deep learning-based Accelerated and Noise-Suppressed Estimation (DANSE) method for reconstructing quantitative maps of biological tissue cellular-specific, R2t*, and hemodynamic-specific, R2', metrics of quantitative gradient-recalled echo (qGRE) MRI. The DANSE method adapts a supervised learning paradigm to train a convolutional neural network for robust estimation of R2t* and R2' maps with significantly reduced sensitivity to noise and the adverse effects of macroscopic (B0 ) magnetic field inhomogeneities directly from the gradient-recalled echo (GRE) magnitude images. The R2t* and R2' maps for training were generated by means of a voxel-by-voxel fitting of a previously developed biophysical quantitative qGRE model accounting for tissue, hemodynamic, and B0 -inhomogeneities contributions to multigradient-echo GRE signal using a nonlinear least squares (NLLS) algorithm. We show that the DANSE model efficiently estimates the aforementioned qGRE maps and preserves all the features of the NLLS approach with significant improvements including noise suppression and computation speed (from many hours to seconds). The noise-suppression feature of DANSE is especially prominent for data with low signal-to-noise ratio (SNR ~ 50-100), where DANSE-generated R2t* and R2' maps had up to three times smaller errors than that of the NLLS method. The DANSE method enables fast reconstruction of qGRE maps with significantly reduced sensitivity to noise and magnetic field inhomogeneities. The DANSE method does not require any information about field inhomogeneities during application. It exploits spatial and gradient echo time-dependent patterns in the GRE data and previously gained knowledge from the biophysical model, thus producing high quality qGRE maps, even in environments with high noise levels. These features along with fast computational speed can lead to broad qGRE clinical and research applications.

Microscopic theory of spin-spin and spin-lattice relaxation of bound protons in cellular and myelin membranes-A lateral diffusion model (LDM).

PURPOSE: Deciphering salient features of biological tissue cellular microstructure in health and diseases is an ultimate goal of MRI. While most MRI approaches are based on studying MR properties of tissue "free" water indirectly affected by tissue microstructure, other approaches, such as magnetization transfer (MT), directly target signals from tissue-forming macromolecules. However, despite three-decades of successful applications, relationships between MT measurements and tissue microstructure remain elusive, hampering interpretation of experimental results. The goal of this paper is to develop microscopic theory connecting the structure of cellular and myelin membranes to their MR properties. THEORY AND METHODS: Herein we introduce a lateral diffusion model (LDM) that explains the T2 (spin-spin) and T1 (spin-lattice) MRI relaxation properties of the macromolecular-bound protons by their dipole-dipole interaction modulated by the lateral diffusion of long lipid molecules forming cellular and myelin membranes. RESULTS: LDM predicts anisotropic T1 and T2 relaxation of membrane-bound protons. Moreover, their T2 relaxation cannot be described in terms of a standard R2  = 1/T2 relaxation rate parameter, but rather by a relaxation rate function R2 (t) that depends on time t after RF excitation, having, in the main approximation, a logarithmic behavior: R2 (t) ∼ lnt. This anisotropic non-linear relaxation leads to an absorption lineshape that is different from Super-Lorentzian traditionally used in interpreting MT experiments. CONCLUSION: LDM-derived analytical equations connect the membrane-bound protons T1 and T2 relaxation with dynamic distances between protons in neighboring membrane-forming lipid molecules and their lateral diffusion. This sheds new light on relationships between MT parameters and microstructure of cellular and myelin membranes.

Evaluating brain damage in multiple sclerosis with simultaneous multi-angular-relaxometry of tissue.

OBJECTIVE: Multiple sclerosis (MS) is a common demyelinating central nervous system disease. MRI methods that can quantify myelin loss are needed for trials of putative remyelinating agents. Quantitative magnetization transfer MRI introduced the macromolecule proton fraction (MPF), which correlates with myelin concentration. We developed an alternative approach, Simultaneous-Multi-Angular-Relaxometry-of-Tissue (SMART) MRI, to generate MPF. Our objective was to test SMART-derived MPF metric as a potential imaging biomarker of demyelination. METHODS: Twenty healthy control (HC), 11 relapsing-remitting MS (RRMS), 22 progressive MS (PMS), and one subject with a biopsied tumefactive demyelinating lesion were scanned at 3T using SMART MRI. SMART-derived MPF metric was determined in normal-appearing cortical gray matter (NAGM), normal-appearing subcortical white matter (NAWM), and demyelinating lesions. MPF metric was evaluated for correlations with physical and cognitive test scores. Comparisons were made between HC and MS and between MS subtypes. Furthermore, correlations were determined between MPF and neuropathology in the biopsied person. RESULTS: SMART-derived MPF in NAGM and NAWM were lower in MS than HC (p < 0.001). MPF in NAGM, NAWM and lesions differentiated RRMS from PMS (p < 0.01, p < 0.001, p < 0.001, respectively), whereas lesion volumes did not. MPF in NAGM, NAWM and lesions correlated with the Expanded Disability Status Scale (p < 0.01, p < 0.001, p < 0.001, respectively) and nine-hole peg test (p < 0.001, p < 0.001, p < 0.01, respectively). MPF was lower in the histopathologically confirmed inflammatory demyelinating lesion than the contralateral NAWM and increased in the biopsied lesion over time, mirroring improved clinical performance. INTERPRETATION: SMART-derived MPF metric holds potential as a quantitative imaging biomarker of demyelination and remyelination.

Stronger Microstructural Damage Revealed in Multiple Sclerosis Lesions With Central Vein Sign by Quantitative Gradient Echo MRI.

Background: Multiple sclerosis (MS) lesions typically form around a central vein that can be visualized with FLAIR* MRI, creating the central vein sign (CVS) which may reflect lesion pathophysiology. Herein we used gradient echo plural contrast imaging (GEPCI) MRI to simultaneously visualize CVS and measure tissue damage in MS lesions. We examined CVS in relation to tissue integrity in white matter (WM) lesions and among MS subtypes. Objective: We aimed to determine if CVS positive lesions were specific to MS subtype, if CVS can be detected consistently among readers using the GEPCI method, and if there were differences in tissue damage in lesions with vs without CVS. Subjects and Methods: Thirty relapsing-remitting MS (RRMS) subjects and 38 primary and secondary progressive MS (PMS) subjects were scanned with GEPCI protocol at 3T. GEPCI T2*-SWI images were generated to visualize CVS. Two investigators independently evaluated WM lesions for CVS and measured lesion volumes. To estimate tissue damage severity, total lesion volume, and mean lesion volume, R2t*-based tissue damage score (TDS) of individual lesions and tissue damage load (TDL) were measured for CVS+, CVS-, and confluent lesions. Spearman correlations were made between MRI and clinical data. One-way ANCOVA with age and sex as covariates was used to compare measurements of CVS+ vs CVS- lesions in each individual. Results: 398 of 548 lesions meeting inclusion criteria showed CVS. Most patients had ≥40% CVS+ lesions. CVS+ lesions were present in similar proportion among MS subtypes. Interobserver agreement was high for CVS detection. CVS+ and confluent lesions had higher average and total volumes vs CVS- lesions. CVS+ and confluent lesions had more tissue damage than CVS- lesions based on TDL and mean TDS. Conclusion: CVS occurred in RRMS and PMS in similar proportions. CVS+ lesions had greater tissue damage and larger size than CVS- lesions.

Learning-based motion artifact removal networks for quantitative R2∗ mapping.

PURPOSE: To introduce two novel learning-based motion artifact removal networks (LEARN) for the estimation of quantitative motion- and B0 -inhomogeneity-corrected R2∗ maps from motion-corrupted multi-Gradient-Recalled Echo (mGRE) MRI data. METHODS: We train two convolutional neural networks (CNNs) to correct motion artifacts for high-quality estimation of quantitative B0 -inhomogeneity-corrected R2∗ maps from mGRE sequences. The first CNN, LEARN-IMG, performs motion correction on complex mGRE images, to enable the subsequent computation of high-quality motion-free quantitative R2∗ (and any other mGRE-enabled) maps using the standard voxel-wise analysis or machine learning-based analysis. The second CNN, LEARN-BIO, is trained to directly generate motion- and B0 -inhomogeneity-corrected quantitative R2∗ maps from motion-corrupted magnitude-only mGRE images by taking advantage of the biophysical model describing the mGRE signal decay. RESULTS: We show that both CNNs trained on synthetic MR images are capable of suppressing motion artifacts while preserving details in the predicted quantitative R2∗ maps. Significant reduction of motion artifacts on experimental in vivo motion-corrupted data has also been achieved by using our trained models. CONCLUSION: Both LEARN-IMG and LEARN-BIO can enable the computation of high-quality motion- and B0 -inhomogeneity-corrected R2∗ maps. LEARN-IMG performs motion correction on mGRE images and relies on the subsequent analysis for the estimation of R2∗ maps, while LEARN-BIO directly performs motion- and B0 -inhomogeneity-corrected R2∗ estimation. Both LEARN-IMG and LEARN-BIO jointly process all the available gradient echoes, which enables them to exploit spatial patterns available in the data. The high computational speed of LEARN-BIO is an advantage that can lead to a broader clinical application.

Tissue damage detected by quantitative gradient echo MRI correlates with clinical progression in non-relapsing progressive MS.

BACKGROUND: Imaging biomarkers of progressive multiple sclerosis (MS) are needed. Quantitative gradient recalled echo (qGRE) magnetic resonance imaging (MRI) evaluates microstructural tissue damage in MS. OBJECTIVE: To evaluate qGRE-derived R2t* as an imaging biomarker of MS progression compared with atrophy and lesion burden. METHODS: Twenty-three non-relapsing progressive MS (PMS), 22 relapsing-remitting MS (RRMS), and 18 healthy control participants underwent standard MS physical and cognitive neurological assessments and imaging with qGRE, FLAIR, and MPRAGE at 3T. PMS subjects were tested clinically and imaged every 9 months over 45 months. Imaging measures included lesion burden, atrophy, and R2t* in cortical gray matter (GM), deep GM, and normal-appearing white matter (NAWM). Longitudinal analysis of clinical performance and imaging biomarkers in PMS subjects was conducted via linear models with subject as repeated, within-subject factor. Relationship between imaging biomarkers and clinical scores was assessed by Spearman rank correlation. RESULTS: R2t* reductions correlated with neurological impairment cross-sectionally and longitudinally. PMS patients with clinically defined disease progression (N = 13) showed faster decrease of R2t* in NAWM and deep GM compared with the clinically stable PMS group (N = 10). Importantly, tissue damage measured by R2t* outperformed lesion burden and atrophy as a biomarker of progression during the study period. CONCLUSION: qGRE-derived R2t* is a potential imaging biomarker of MS progression.

Quantitative Gradient Echo MRI Identifies Dark Matter as a New Imaging Biomarker of Neurodegeneration that Precedes Tisssue Atrophy in Early Alzheimer's Disease.

BACKGROUND: Currently, brain tissue atrophy serves as an in vivo MRI biomarker of neurodegeneration in Alzheimer's disease (AD). However, postmortem histopathological studies show that neuronal loss in AD exceeds volumetric loss of tissue and that loss of memory in AD begins when neurons and synapses are lost. Therefore, in vivo detection of neuronal loss prior to detectable atrophy in MRI is essential for early AD diagnosis. OBJECTIVE: To apply a recently developed quantitative Gradient Recalled Echo (qGRE) MRI technique for in vivo evaluation of neuronal loss in human hippocampus. METHODS: Seventy participants were recruited from the Knight Alzheimer Disease Research Center, representing three groups: Healthy controls [Clinical Dementia Rating® (CDR®) = 0, amyloid ß (Aß)-negative, n = 34]; Preclinical AD (CDR = 0, Aß-positive, n = 19); and mild AD (CDR = 0.5 or 1, Aß-positive, n = 17). RESULTS: In hippocampal tissue, qGRE identified two types of regions: one, practically devoid of neurons, we designate as "Dark Matter", and the other, with relatively preserved neurons, "Viable Tissue". Data showed a greater loss of neurons than defined by atrophy in the mild AD group compared with the healthy control group; neuronal loss ranged between 31% and 43%, while volume loss ranged only between 10% and 19%. The concept of Dark Matter was confirmed with histopathological study of one participant who underwent in vivo qGRE 14 months prior to expiration. CONCLUSION: In vivo qGRE method identifies neuronal loss that is associated with impaired AD-related cognition but is not recognized by MRI measurements of tissue atrophy, therefore providing new biomarkers for early AD detection.

The Role of the Human Brain Neuron-Glia-Synapse Composition in Forming Resting-State Functional Connectivity Networks.

While significant progress has been achieved in studying resting-state functional networks in a healthy human brain and in a wide range of clinical conditions, many questions related to their relationship to the brain's cellular constituents remain. Here, we use quantitative Gradient-Recalled Echo (qGRE) MRI for mapping the human brain cellular composition and BOLD (blood-oxygen level-dependent) MRI to explore how the brain cellular constituents relate to resting-state functional networks. Results show that the BOLD signal-defined synchrony of connections between cellular circuits in network-defined individual functional units is mainly associated with the regional neuronal density, while the between-functional units' connectivity strength is also influenced by the glia and synaptic components of brain tissue cellular constituents. These mechanisms lead to a rather broad distribution of resting-state functional network properties. Visual networks with the highest neuronal density (but lowest density of glial cells and synapses) exhibit the strongest coherence of the BOLD signal as well as the strongest intra-network connectivity. The Default Mode Network (DMN) is positioned near the opposite part of the spectrum with relatively low coherence of the BOLD signal but with a remarkably balanced cellular contents, enabling DMN to have a prominent role in the overall organization of the brain and hierarchy of functional networks.

In vivo evaluation of heme and non-heme iron content and neuronal density in human basal ganglia.

Non-heme iron is an important element supporting the structure and functioning of biological tissues. Imbalance in non-heme iron can lead to different neurological disorders. Several MRI approaches have been developed for iron quantification relying either on the relaxation properties of MRI signal or measuring tissue magnetic susceptibility. Specific quantification of the non-heme iron can, however, be constrained by the presence of the heme iron in the deoxygenated blood and contribution of cellular composition. The goal of this paper is to introduce theoretical background and experimental MRI method allowing disentangling contributions of heme and non-heme irons simultaneously with evaluation of tissue neuronal density in the iron-rich basal ganglia. Our approach is based on the quantitative Gradient Recalled Echo (qGRE) MRI technique that allows separation of the total R2* metric characterizing decay of GRE signal into tissue-specific (R2t*) and the baseline blood oxygen level-dependent (BOLD) contributions. A combination with the QSM data (also available from the qGRE signal phase) allowed further separation of the tissue-specific R2t* metric in a cell-specific and non-heme-iron-specific contributions. It is shown that the non-heme iron contribution to R2t* relaxation can be described with the previously developed Gaussian Phase Approximation (GPA) approach. qGRE data were obtained from 22 healthy control participants (ages 26-63 years). Results suggest that the ferritin complexes are aggregated in clusters with an average radius about 100nm comprising approximately 2600 individual ferritin units. It is also demonstrated that the concentrations of heme and non-heme iron tend to increase with age. The strongest age effect was seen in the pallidum region, where the highest age-related non-heme iron accumulation was observed.

Deep learning using a biophysical model for robust and accelerated reconstruction of quantitative, artifact-free and denoised R2* images.

PURPOSE: To introduce a novel deep learning method for Robust and Accelerated Reconstruction (RoAR) of quantitative and B0-inhomogeneity-corrected R2* maps from multi-gradient recalled echo (mGRE) MRI data. METHODS: RoAR trains a convolutional neural network (CNN) to generate quantitative R2∗ maps free from field inhomogeneity artifacts by adopting a self-supervised learning strategy given (a) mGRE magnitude images, (b) the biophysical model describing mGRE signal decay, and (c) preliminary-evaluated F-function accounting for contribution of macroscopic B0 field inhomogeneities. Importantly, no ground-truth R2* images are required and F-function is only needed during RoAR training but not application. RESULTS: We show that RoAR preserves all features of R2* maps while offering significant improvements over existing methods in computation speed (seconds vs. hours) and reduced sensitivity to noise. Even for data with SNR = 5 RoAR produced R2* maps with accuracy of 22% while voxel-wise analysis accuracy was 47%. For SNR = 10 the RoAR accuracy increased to 17% vs. 24% for direct voxel-wise analysis. CONCLUSIONS: RoAR is trained to recognize the macroscopic magnetic field inhomogeneities directly from the input magnitude-only mGRE data and eliminate their effect on R2∗ measurements. RoAR training is based on the biophysical model and does not require ground-truth R2* maps. Since RoAR utilizes signal information not just from individual voxels but also accounts for spatial patterns of the signals in the images, it reduces the sensitivity of R2* maps to the noise in the data. These features plus high computational speed provide significant benefits for the potential usage of RoAR in clinical settings.

In vivo evolution of biopsy-proven inflammatory demyelination quantified by R2t* mapping.

A 35-year-old man with an enhancing tumefactive brain lesion underwent biopsy, revealing inflammatory demyelination. We used quantitative Gradient-Recalled-Echo (qGRE) MRI to visualize and measure tissue damage in the lesion. Two weeks after biopsy, qGRE showed significant R2t* reduction in the left optic radiation and surrounding tissue, consistent with the histopathological and clinical findings. qGRE was repeated 6 and 14 months later, demonstrating partially recovered optic radiation R2t*, in concert with improvement of the hemianopia to ultimately involve only the lower right visual quadrant. These results support qGRE metrics as in vivo biomarkers for tissue damage and longitudinal monitoring of demyelinating disease.

A Novel Gradient Echo Plural Contrast Imaging Method Detects Brain Tissue Abnormalities in Patients With TBI Without Evident Anatomical Changes on Clinical MRI: A Pilot Study.

RESEARCH OBJECTIVES: It is widely accepted that mild traumatic brain injury (mTBI) causes injury to the white matter, but the extent of gray matter (GM) damage in mTBI is less clear. METHODS: We tested 26 civilian healthy controls and 14 civilian adult subacute-chronic mTBI patients using quantitative features of MRI-based Gradient Echo Plural Contrast Imaging (GEPCI) technique. GEPCI data were reconstructed using previously developed algorithms allowing the separation of R2t*, a cellular-specific part of gradient echo MRI relaxation rate constant, from global R2* affected by BOLD effect and background gradients. RESULTS: Single-subject voxel-wise analysis (comparing each mTBI patient to the sample of 26 control subjects) revealed GM abnormalities that were not visible on standard MRI images (T1w and T2w). Analysis of spatial overlap for voxels with low R2t* revealed tissue abnormalities in multiple GM regions, especially in the frontal and temporal regions, that are frequently damaged after mTBI. The left posterior insula was the region with abnormalities found in the highest proportion (50%) of mTBI patients. CONCLUSIONS: Our data suggest that GEPCI quantitative R2t* metric has potential to detect abnormalities in GM cellular integrity in individual TBI patients, including abnormalities that are not detectable by a standard clinical MRI.

Single scan quantitative gradient recalled echo MRI for evaluation of tissue damage in lesions and normal appearing gray and white matter in multiple sclerosis.

BACKGROUND: Multiple sclerosis (MS) is a chronic disease affecting the human central nervous system (CNS) and leading to neurologic disability. Although conventional MRI techniques can readily detect focal white matter (WM) lesions, it remains challenging to quantify tissue damage in normal-appearing gray matter (GM) and WM. PURPOSE: To demonstrate that a new MRI biomarker, R2t*, can provide quantitative analysis of tissue damage across the brain in MS patients in a single scan. STUDY TYPE: Prospective. SUBJECTS: Forty-four MS patients and 19 healthy controls (HC). FIELD STRENGTH/SEQUENCE: 3T, quantitative gradient-recalled-echo (qGRE), Magnetization-prepared rapid gradient-echo, fluid-attenuated inversion recovery. ASSESSMENT: Severity of tissue damage was assessed by reduced R2t*. Tissue atrophy was assessed by cortical thickness and cervical spinal cord cross-sectional area (CSA). Multiple Sclerosis Functional Composite was used for clinical assessment. RESULTS: R2t* in cortical GM was more sensitive to MS damage than cortical atrophy. Using more than two standard deviations (SD) reduction versus age-matched HC as the cutoff, 48% of MS patients showed lower R2t*, versus only 9% with lower cortical thickness. Significant correlations between severities of tissue injury were identified among 1) upper cervical cord and several cortical regions, including motor cortex (P < 0.001), and 2) adjacent regions of GM and subcortical WM (P < 0.001). R2t*-defined tissue cellular damage in cortical GM was greater relative to adjacent WM. Reductions in cortical R2t* correlated with cognitive impairment (P < 0.01). Motor-related clinical signs correlated most with cervical cord CSA (P < 0.001). DATA CONCLUSION: Reductions in R2t* within cortical GM was more sensitive to tissue damage than atrophy, potentially allowing a reduced sample size in clinical trials. R2t* together with structural morphometry suggested topographic patterns of regions showing correlated tissue damage throughout the brain and the cervical spinal cord of MS patients. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;49:487-498.

Genetically defined cellular correlates of the baseline brain MRI signal.

fMRI revolutionized neuroscience by allowing in vivo real-time detection of human brain activity. While the nature of the fMRI signal is understood as resulting from variations in the MRI signal due to brain-activity-induced changes in the blood oxygenation level (BOLD effect), these variations constitute a very minor part of a baseline MRI signal. Hence, the fundamental (and not addressed) questions are how underlying brain cellular composition defines this baseline MRI signal and how a baseline MRI signal relates to fMRI. Herein we investigate these questions by using a multimodality approach that includes quantitative gradient recalled echo (qGRE), volumetric and functional connectivity MRI, and gene expression data from the Allen Human Brain Atlas. We demonstrate that in vivo measurement of the major baseline component of a GRE signal decay rate parameter (R2t*) provides a unique genetic perspective into the cellular constituents of the human cortex and serves as a previously unidentified link between cortical tissue composition and fMRI signal. Data show that areas of the brain cortex characterized by higher R2t* have high neuronal density and have stronger functional connections to other brain areas. Interestingly, these areas have a relatively smaller concentration of synapses and glial cells, suggesting that myelinated cortical axons are likely key cortical structures that contribute to functional connectivity. Given these associations, R2t* is expected to be a useful signal in assessing microstructural changes in the human brain during development and aging in health and disease.

Lorentzian effects in magnetic susceptibility mapping of anisotropic biological tissues.

The ultimate goal of MRI is to provide information on biological tissue microstructure and function. Quantitative Susceptibility Mapping (QSM) is one of the newer approaches for studying tissue microstructure by means of measuring phase of Gradient Recalled Echo (GRE) MRI signal. The fundamental question in the heart of this approach is: what is the relationship between the net phase/frequency of the GRE signal from an imaging voxel and the underlying tissue microstructure at the cellular and sub-cellular levels? In the presence of external magnetic field, biological media (e.g. cells, cellular components, blood) become magnetized leading to the MR signal frequency shift that is affected not only by bulk magnetic susceptibility but by the local cellular environment as well. The latter effect is often termed the Lorentzian contribution to the frequency shift. Evaluating the Lorentzian contribution - one of the most intriguing and challenging problems in this field - is the main focus of this review. While the traditional approach to this problem is based on introduction of an imaginary Lorentzian cavity, a more rigorous treatment was proposed recently based on a statistical approach and a direct solution of the Maxwell equations. This approach, termed the Generalized Lorentzian Tensor Approach (GLTA), is especially fruitful for describing anisotropic biological media. The GLTA adequately accounts for two types of anisotropy: anisotropy of magnetic susceptibility and tissue structural anisotropy (e.g., cylindrical axonal bundles in white matter). In the framework of the GLTA the frequency shift due to the local environment is described in terms of the Lorentzian tensor L̂ which can have a substantially different structure than the susceptibility tensor χ̂. While the components of χ̂ are compartmental susceptibilities "weighted" by their volume fractions, the components of L̂ are additionally weighted by specific numerical factors depending on cellular geometrical symmetry. In addition to describing the GLTA that is a phenomenological approach largely based on considering the system symmetry, we also briefly discuss a microscopic approaches to the problem that are based on modeling of the MR signal in different regimes (i.e. static dephasing vs. motion narrowing) and in different cellular environments (e.g., accounting for WM microstructure).

Phase-sensitive B1 mapping: Effects of relaxation and RF spoiling.

PURPOSE: To develop a phase-based B1 mapping technique accounting for the effects of imperfect RF spoiling and magnetization relaxation. THEORY AND METHODS: The technique is based on a multi-gradient-echo sequence with 2 successive orthogonal radiofrequency (RF) excitation pulses followed by the train of gradient echoes measurements. We have derived a theoretical expression relating the MR signal phase produced by the 2 successive RF pulses to the B1 field and B0 -related frequency shift. The expression takes into account effects of imperfections of RF spoiling and T1 and T2* relaxations. RESULTS: Our computer simulations and experiments revealed that imperfections of RF spoiling cause significant errors in B1 mapping if not accounted for. By accounting for these effects along with effects of magnetization relaxation and frequency shift, we demonstrated the high accuracy of our approach. The technique has been tested on spherical phantoms and a healthy volunteer. CONCLUSION: In this paper, we have proposed, implemented, and demonstrated the accuracy of a new phase-based technique for fast and robust B1 mapping based on the measured MR signal phase, frequency, and relaxation. Because imperfect RF spoiling effects are accounted for, this technique can be applied with short TRs and therefore substantially reduces the scan time. Magn Reson Med 80:101-111, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Limbic system damage in MS: MRI assessment and correlations with clinical testing.

Volume loss in some limbic region structures has been observed in multiple sclerosis (MS) patients. However, in vivo evaluation of existing tissue cellular microstructure integrity has received less attention. The goal of studies reported here was to quantitatively assess loss of limbic system volumes and tissue integrity, and to evaluate associations of these measures with cognitive and physical dysfunction in MS patients. Thirty-one healthy controls (HC) and 80 MS patients, including 32 relapsing remitting (RRMS), 32 secondary progressive (SPMS) and 16 primary progressive (PPMS), participated in this study. Tissue cellular integrity was evaluated by means of recently introduced tissue-specific parameter R2t* that was calculated from multi-gradient-echo MRI signals using a recently developed method that separates R2t* from BOLD (blood oxygen level dependent) contributions to GRE signal decay rate constant (R2*), and accounting for physiological fluctuations and artifacts from background gradients. Volumes in limbic system regions, normalized to skull size (NV), were measured from standard MPRAGE images. MS patients had lower R2t* and smaller normalized volumes in the hippocampus, amygdala, and several other limbic system regions, compared to HC. Alterations in R2t* of several limbic system regions correlated with clinical and neurocognitive test scores in MS patients. In contrast, smaller normalized volumes in MS were only correlated with neurocognitive test scores in the hippocampus and amygdala. This study reports the novel finding that R2t*, a measure that estimates tissue integrity, is more sensitive to tissue damage in limbic system structures than is atrophy. R2t* measurements can serve as a biomarker that is distinct from and complementary to volume measurements.

What makes a good pediatric transplant lung: Insights from in vivo lung morphometry with hyperpolarized 3 He magnetic resonance imaging.

Obtaining information on transplanted lung microstructure is an important part of the current care for monitoring transplant recipients. However, until now this information was only available from invasive lung biopsy. The objective of this study was to evaluate the use of an innovative non-invasive technique, in vivo lung morphometry with hyperpolarized ³He MRI-to characterize lung microstructure in the pediatric lung transplant population. This technique yields quantitative measurements of acinar airways' (alveolar ducts and sacs) parameters, such as acinar airway radii and alveolar depth. Six pediatric lung transplant recipients with cystic fibrosis underwent in vivo lung morphometry MRI, pulmonary function testing, and quantitative CT. We found a strong correlation between lung lifespan and alveolar depth-patients with more shallow alveoli were likely to have a negative outcome sooner than those with larger alveolar depth. Combining morphometric results with CT, we also determined mean alveolar wall thickness and found substantial increases in this parameter in some patients that negatively correlated with DLCO. In vivo lung morphometry uniquely provides previously unavailable information on lung microstructure that may be predictive of a negative outcome and has a potential to aid in lung selection for transplantation.

Simultaneous multi-angular relaxometry of tissue with MRI (SMART MRI): Theoretical background and proof of concept.

PURPOSE: Accurate measurement of tissue-specific relaxation parameters is an ultimate goal of quantitative MRI. The objective of this study is to introduce a new technique, simultaneous multiangular relaxometry of tissue with MRI (SMART MRI), which provides naturally coregistered quantitative spin density, longitudinal and transverse relaxation rate constant maps along with parameters characterizing magnetization transfer (MT) effects. THEORY AND METHODS: SMART MRI is based on a gradient-recalled echo MRI sequence with multiple flip angles and multiple gradient echoes and a derived theoretical expression for the MR signal generated in this experimental conditions. The theory, based on Bloch-McConnell equations, takes into consideration cross-relaxation between two water pools: "free" and "bound" to macromolecules. It describes the role of cross-relaxation effects in formation of longitudinal and transverse relaxation of "free" water signal, thus providing background for measurements of these effects without using MT pulses. Bayesian analysis is used to optimize SMART MRI sequence parameters. RESULTS: Data obtained on three participants demonstrate feasibility of the proposed approach. CONCLUSION: SMART MRI provides quantitative measurements of longitudinal and transverse relaxation rate constants of "free" water signal affected by cross-relaxation effects. It also provides information on some essential MT parameters without requiring off-resonance MT pulses. Magn Reson Med 77:1296-1306, 2017. © 2016 International Society for Magnetic Resonance in Medicine.