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.

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.

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.

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.

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.

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.

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.

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.

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.

In vivo detection of microstructural correlates of brain pathology in preclinical and early Alzheimer Disease with magnetic resonance imaging.

BACKGROUND: Alzheimer disease (AD) affects at least 5 million individuals in the USA alone stimulating an intense search for disease prevention and treatment therapies as well as for diagnostic techniques allowing early identification of AD during a long pre-symptomatic period that can be used for the initiation of prevention trials of disease-modifying therapies in asymptomatic individuals. METHODS: Our approach to developing such techniques is based on the Gradient Echo Plural Contrast Imaging (GEPCI) technique that provides quantitative in vivo measurements of several brain-tissue-specific characteristics of the gradient echo MRI signal (GEPCI metrics) that depend on the integrity of brain tissue cellular structure. Preliminary data were obtained from 34 participants selected from the studies of aging and dementia at the Knight Alzheimer's Disease Research Center at Washington University in St. Louis. Cognitive status was operationalized with the Clinical Dementia Rating (CDR) scale. The participants, assessed as cognitively normal (CDR=0; n=23) or with mild AD dementia (CDR=0.5 or 1; n=11) underwent GEPCI MRI, a collection of cognitive performance tests and CSF amyloid (Aß) biomarker Aß42. A subset of 19 participants also underwent PET PiB studies to assess their brain Aß burden. According to the Aß status, cognitively normal participants were divided into normal (Aß negative; n=13) and preclinical (Aß positive; n=10) groups. RESULTS: GEPCI quantitative measurements demonstrated significant differences between all the groups: normal and preclinical, normal and mild AD, and preclinical and mild AD. GEPCI quantitative metrics characterizing tissue cellular integrity in the hippocampus demonstrated much stronger correlations with psychometric tests than the hippocampal atrophy. Importantly, GEPCI-determined changes in the hippocampal tissue cellular integrity were detected even in the hippocampal areas not affected by the atrophy. Our studies also uncovered strong correlations between GEPCI brain tissue metrics and beta-amyloid (Aß) burden defined by positron emission tomography (PET) - the current in vivo gold standard for detection of cortical Aß, thus supporting GEPCI as a potential surrogate marker for Aß imaging - a known biomarker of early AD. Remarkably, the data show significant correlations not only in the areas of high Aß accumulation (e.g. precuneus) but also in some areas of medial temporal lobe (e.g. parahippocampal cortex), where Aß accumulation is relatively low. CONCLUSION: We have demonstrated that GEPCI provides a new approach for the in vivo evaluation of AD-related tissue pathology in the preclinical and early symptomatic stages of AD. Since MRI is a widely available technology, the GEPCI surrogate markers of AD pathology have a potential for improving the quality of AD diagnostic, and the evaluation of new disease-modifying therapies.

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.

Direct comparison of 129 Xe diffusion measurements with quantitative histology in human lungs.

PURPOSE: Chronic obstructive pulmonary disease (COPD) is an irreversible lung disease characterized by small-airway obstruction and alveolar-airspace destruction. Hyperpolarized 129 Xe diffusion MRI of lung is a promising biomarker for assessing airspace enlargement, but has yet to be validated by direct comparison to lung histology. Here we have compared diffusion measurements of hyperpolarized (HP) 129 Xe in explanted lungs to regionally matched morphological measures of airspace size. METHODS: Explanted lungs from five COPD patients and two idiopathic pulmonary fibrosis (IPF) patients were imaged using MRI with hyperpolarized 129 Xe using a two-b-value gradient-echo diffusion sequence, and 34 histological samples were taken from these lungs for quantitative histology. Mean-linear-intercept (Lm ) was compared with spatially matched measures of apparent diffusion coefficient (ADC) from 129 Xe MRI. RESULTS: The mean ADC from COPD lung samples was 0.071 ± 0.011 cm2 /s, and for IPF lungs was 0.033 ± 0.001 cm2 /s (P < 10-15 between groups). The mean Lm in COPD samples was 0.076 ± 0.027 cm and 0.041 ± 0.004 cm in IPF (P = 2.7 × 10-7 between groups). The Pearson-correlation between ADC and Lm measurements was r = 0.59. CONCLUSIONS: Diffusion MRI of HP 129 Xe quantifies regional airspace enlargement in COPD. 129 Xe ADC showed much less overlap between groups than quantitative histology, consistent with our past experience with 3 He diffusion MRI in COPD. Magn Reson Med 77:265-272, 2017. © 2016 Wiley Periodicals, Inc.

Effects of biological tissue structural anisotropy and anisotropy of magnetic susceptibility on the gradient echo MRI signal phase: theoretical background.

Quantitative susceptibility mapping is a potentially powerful technique for mapping tissue magnetic susceptibility from gradient recalled echo (GRE) MRI signal phase. In this review, we present up-to-date theoretical developments in analyzing the relationships between GRE signal phase and the underlying tissue microstructure and magnetic susceptibility at the cellular level. Two important phenomena contributing to the GRE signal phase are at the focus of this review - tissue structural anisotropy (e.g. cylindrical axonal bundles in white matter) and magnetic susceptibility anisotropy. One of the most intriguing and challenging problems in this field is calculating the so-called Lorentzian contribution to the phase shift induced by the local environment - magnetized tissue structures that have dimensions smaller than the imaging voxel (e.g. cells, cellular components, blood capillaries). In this review, we briefly discuss a "standard" approach to this problem, based on introduction of an imaginary Lorentzian cavity, as well as a more recent method - the generalized Lorentzian tensor approach (GLTA) - that is based on a statistical approach and a direct solution of the magnetostatic Maxwell equations. The latter adequately accounts for both types of anisotropy: the anisotropy of magnetic susceptibility and the structural tissue anisotropy. In the GLTA the frequency shift due to the local environment is characterized by the Lorentzian tensor L^, which has a substantially different structure than the susceptibility tensor χ^. While the components of χ^ are compartmental susceptibilities "weighted" by their volume fractions, the components of L^ are weighted by specific numerical factors depending on tissue geometrical microsymmetry. In multi-compartment structures, the components of the Lorentzian tensor also depend on the compartmental relaxation properties, hence the MR pulse sequence settings. Copyright © 2016 John Wiley & Sons, Ltd.

Diffusion lung imaging with hyperpolarized gas MRI.

Lung imaging using conventional 1 H MRI presents great challenges because of the low density of lung tissue, lung motion and very fast lung tissue transverse relaxation (typical T2 * is about 1-2 ms). MRI with hyperpolarized gases (3 He and 129 Xe) provides a valuable alternative because of the very strong signal originating from inhaled gas residing in the lung airspaces and relatively slow gas T2 * relaxation (typical T2 * is about 20-30 ms). However, in vivo human experiments should be performed very rapidly - usually during a single breath-hold. In this review, we describe the recent developments in diffusion lung MRI with hyperpolarized gases. We show that a combination of the results of modeling of gas diffusion in lung airspaces and diffusion measurements with variable diffusion-sensitizing gradients allows the extraction of quantitative information on the lung microstructure at the alveolar level. From an MRI scan of less than 15 s, this approach, called in vivo lung morphometry, allows the provision of quantitative values and spatial distributions of the same physiological parameters as measured by means of 'standard' invasive stereology (mean linear intercept, surface-to-volume ratio, density of alveoli, etc.). In addition, the approach makes it possible to evaluate some advanced Weibel parameters characterizing lung microstructure: average radii of alveolar sacs and ducts, as well as the depth of their alveolar sleeves. Such measurements, providing in vivo information on the integrity of pulmonary acinar airways and their changes in different diseases, are of great importance and interest to a broad range of physiologists and clinicians. We also discuss a new type of experiment based on the in vivo lung morphometry technique combined with quantitative computed tomography measurements, as well as with gradient echo MRI measurements of hyperpolarized gas transverse relaxation in the lung airspaces. Such experiments provide additional information on the blood vessel volume fraction, specific gas volume and length of the acinar airways, and allow the evaluation of lung parenchymal and non-parenchymal tissue. Copyright © 2015 John Wiley & Sons, Ltd.

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.

On the relationship between cellular and hemodynamic properties of the human brain cortex throughout adult lifespan.

Establishing baseline MRI biomarkers for normal brain aging is significant and valuable for separating normal changes in the brain structure and function from different neurological diseases. In this paper for the first time we have simultaneously measured a variety of tissue specific contributions defining R2* relaxation of the gradient recalled echo (GRE) MRI signal in human brains of healthy adults (ages 22 to 74years) and related these measurements to tissue structural and functional properties. This was accomplished by separating tissue (R2t(⁎)) and extravascular BOLD contributions to the total tissue specific GRE MRI signal decay (R2(⁎)) using an advanced version of previously developed Gradient Echo Plural Contrast Imaging (GEPCI) approach and the acquisition and post-processing methods that allowed the minimization of artifacts related to macroscopic magnetic field inhomogeneities, and physiological fluctuations. Our data (20 healthy subjects) show that in most cortical regions R2t(⁎) increases with age while tissue hemodynamic parameters, i.e. relative oxygen extraction fraction (OEFrel), deoxygenated cerebral blood volume (dCBV) and tissue concentration of deoxyhemoglobin (Cdeoxy) remain practically constant. We also found the important correlations characterizing the relationships between brain structural and hemodynamic properties in different brain regions. Specifically, thicker cortical regions have lower R2t(⁎) and these regions have lower OEF. The comparison between GEPCI-derived tissue specific structural and functional metrics and literature information suggests that (a) regions in a brain characterized by higher R2t(⁎) contain higher concentration of neurons with less developed cellular processes (dendrites, spines, etc.), (b) regions in a brain characterized by lower R2t(⁎) represent regions with lower concentration of neurons but more developed cellular processes, and (c) the age-related increases in the cortical R2t(⁎) mostly reflect the age-related increases in the cellular packing density. The baseline GEPCI-based biomarkers obtain herein could serve to help distinguish age-related changes in brain cellular and hemodynamic properties from changes which occur due to the neurodegenerative diseases.

Separation of cellular and BOLD contributions to T2* signal relaxation.

PURPOSE: The development of a reliable clinical technique for quantitative measurements of the parameters defining the BOLD effect, i.e., oxygen extraction fraction (OEF), and deoxygenated cerebral blood volume, dCBV, is needed to study brain function in health and disease. Herein we propose such a technique that is based on a widely available gradient recalled echo (GRE) MRI. THEORY AND METHODS: Our method is based on GRE with multiple echoes and a model of signal decay (Yablonskiy, MRM 1998) that takes into account microscopic cellular (R2), mesoscopic (BOLD), and macroscopic (background field gradients) contributions to the GRE signal decay with additional accounting for physiologic fluctuations. RESULTS: Using 3 Tesla MRI, we generate high resolution quantitative maps of R2*, R2, R2', and tissue concentration of deoxyhemoglobin, the latter providing a quantitative version of SWI. Our results for OEF and dCBV in gray matter are in a reasonable agreement with the literature data. CONCLUSION: The proposed approach allows generating high resolution maps of hemodynamic parameters using clinical MRI. The technique can be applied to study such tissues as gray matter, tumors, etc.; however, it requires further development for use in tissues where extra- and intracellular compartments possess substantially different frequencies and relaxation properties (e.g., white matter).

Evidence for adult lung growth in humans.

A 33-year-old woman underwent a right-sided pneumonectomy in 1995 for treatment of a lung adenocarcinoma. As expected, there was an abrupt decrease in her vital capacity, but unexpectedly, it increased during the subsequent 15 years. Serial computed tomographic (CT) scans showed progressive enlargement of the remaining left lung and an increase in tissue density. Magnetic resonance imaging (MRI) with the use of hyperpolarized helium-3 gas showed overall acinar-airway dimensions that were consistent with an increase in the alveolar number rather than the enlargement of existing alveoli, but the alveoli in the growing lung were shallower than in normal lungs. This study provides evidence that new lung growth can occur in an adult human.

Generalized Lorentzian Tensor Approach (GLTA) as a biophysical background for quantitative susceptibility mapping.

PURPOSE: Quantitative susceptibility mapping (QSM) is a potentially powerful technique for mapping tissue magnetic susceptibility from gradient recalled echo (GRE) MRI. Herein we aim to derive the relationships between GRE signal phase and the underlying tissue microstructure and magnetic susceptibility at the cellular level. METHODS: We use Maxwell's equations and a statistical approach to derive the expression for the magnetic-susceptibility-induced MR signal frequency shift of the GRE signal in single- and multicompartment systems, in which inhomogeneous magnetic field is induced by the cellular constituents (proteins, lipids, iron, etc.) distributed in intra- and extracellular spaces. RESULTS: We introduce the Generalized Lorentzian Tensor Approach (GLTA) that accounts for both types of anisotropy: the anisotropy of magnetic susceptibility and the structural tissue anisotropy. In the GLTA the frequency shift due to the local environment is characterized by the Lorentzian tensor L⁁ which has a substantially different structure than the susceptibility tensor χ⁁. While components of χ⁁ are simply compartmental susceptibilities "weighted" by their relative volumes, the components of L⁁ are weighted by specific numerical factors depending on tissue micro-symmetry and parameters related to the MR pulse sequence. We also provide equations bridging phenomenological and microscopic considerations. CONCLUSION: The GLTA provides a consistent background for deciphering phase data.