Showing papers in "NMR in Biomedicine in 2019"
TL;DR: In this article, the authors review, systematize and discuss models of diffusion in neuronal tissue, by putting them into an overarching physical context of coarse-graining over an increasing diffusion length scale.
Abstract: We review, systematize and discuss models of diffusion in neuronal tissue, by putting them into an overarching physical context of coarse-graining over an increasing diffusion length scale. From this perspective, we view research on quantifying brain microstructure as occurring along three major avenues. The first avenue focusses on transient, or time-dependent, effects in diffusion. These effects signify the gradual coarse-graining of tissue structure, which occurs qualitatively differently in different brain tissue compartments. We show that transient effects contain information about the relevant length scales for neuronal tissue, such as the packing correlation length for neuronal fibers, as well as the degree of structural disorder along the neurites. The second avenue corresponds to the long-time limit, when the observed signal can be approximated as a sum of multiple nonexchanging anisotropic Gaussian components. Here, the challenge lies in parameter estimation and in resolving its hidden degeneracies. The third avenue employs multiple diffusion encoding techniques, able to access information not contained in the conventional diffusion propagator. We conclude with our outlook on future directions that could open exciting possibilities for designing quantitative markers of tissue physiology and pathology, based on methods of studying mesoscopic transport in disordered systems.
356 citations
TL;DR: An overview of the key concepts of tractography, the technical considerations at play, and the different types of tractographic algorithm, as well as the common misconceptions and mistakes that surround them are provided.
Abstract: The ability of fiber tractography to delineate non-invasively the white matter fiber pathways of the brain raises possibilities for clinical applications and offers enormous potential for neuroscience In the last decade, fiber tracking has become the method of choice to investigate quantitative MRI parameters in specific bundles of white matter For neurosurgeons, it is quickly becoming an invaluable tool for the planning of surgery, allowing for visualization and localization of important white matter pathways before and even during surgery Fiber tracking has also claimed a central role in the field of “connectomics,” a technique that builds and studies comprehensive maps of the complex network of connections within the brain, and to which significant resources have been allocated worldwide Despite its unique abilities and exciting applications, fiber tracking is not without controversy, in particular when it comes to its interpretation As neuroscientists are eager to study the brain's connectivity, the quantification of tractography-derived “connection strengths” between distant brain regions is becoming increasingly popular However, this practice is often frowned upon by fiber-tracking experts In light of this controversy, this paper provides an overview of the key concepts of tractography, the technical considerations at play, and the different types of tractography algorithm, as well as the common misconceptions and mistakes that surround them We also highlight the ongoing challenges related to fiber tracking While recent methodological developments have vastly increased the biological accuracy of fiber tractograms, one should be aware that, even with state-of-the-art techniques, many issues that severely bias the resulting structural “connectomes” remain unresolved
339 citations
TL;DR: The article summarizes the relevant aspects of brain microanatomy and the range of diffusion‐weighted MR measurements that provide to them and reviews the evolution of mathematical and computational models that relate the diffusion MR signal to brain tissue microstructure.
Abstract: This article gives an overview of microstructure imaging of the brain with diffusion MRI and reviews the state of the art. The microstructure-imaging paradigm aims to estimate and map microscopic properties of tissue using a model that links these properties to the voxel scale MR signal. Imaging techniques of this type are just starting to make the transition from the technical research domain to wide application in biomedical studies. We focus here on the practicalities of both implementing such techniques and using them in applications. Specifically, the article summarizes the relevant aspects of brain microanatomy and the range of diffusion-weighted MR measurements that provide sensitivity to them. It then reviews the evolution of mathematical and computational models that relate the diffusion MR signal to brain tissue microstructure, as well as the expanding areas of application. Next we focus on practicalities of designing a working microstructure imaging technique: model selection, experiment design, parameter estimation, validation, and the pipeline of development of this class of technique. The article concludes with some future perspectives on opportunities in this topic and expectations on how the field will evolve in the short-to-medium term.
283 citations
TL;DR: This review summarizes the last decade of diffusion imaging studies of healthy white matter development spanning childhood to early adulthood and suggests that maturation in association tracts with frontal‐temporal connections continues later than commissural and projection tracts.
Abstract: Understanding typical, healthy brain development provides a baseline from which to detect and characterize brain anomalies associated with various neurological or psychiatric disorders and diseases. Diffusion MRI is well suited to study white matter development, as it can virtually extract individual tracts and yield parameters that may reflect alterations in the underlying neural micro-structure (e.g. myelination, axon density, fiber coherence), though it is limited by its lack of specificity and other methodological concerns. This review summarizes the last decade of diffusion imaging studies of healthy white matter development spanning childhood to early adulthood (4-35 years). Conclusions about anatomical location, rates, and timing of white matter development with age are discussed, as well as the influence of image acquisition, analysis, age range/sample size, and statistical model. Despite methodological variability between studies, some consistent findings have emerged from the literature. Specifically, diffusion studies of neurodevelopment overwhelmingly demonstrate regionally varying increases of fractional anisotropy and decreases of mean diffusivity during childhood and adolescence, some of which continue into adulthood. While most studies use linear fits to model age-related changes, studies with sufficient sample sizes and age range provide clear evidence that white matter development (as indicated by diffusion) is non-linear. Several studies further suggest that maturation in association tracts with frontal-temporal connections continues later than commissural and projection tracts. The emerging contributions of more advanced diffusion methods are also discussed, as they may reveal new aspects of white matter development. Although non-specific, diffusion changes may reflect increases of myelination, axonal packing, and/or coherence with age that may be associated with changes in cognition.
238 citations
TL;DR: It is argued that diffusion MRI‐based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction.
Abstract: Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments.
214 citations
TL;DR: This review highlights the main steps that have led the field of diffusion imaging to move from the tensor model to the adoption of diffusion and fibre orientation density functions as a more effective way to describe the complexity of white matter organization within each brain voxel.
Abstract: Since the realization that diffusion MRI can probe the microstructural organization and orientation of biological tissue in vivo and non-invasively, a multitude of diffusion imaging methods have been developed and applied to study the living human brain. Diffusion tensor imaging was the first model to be widely adopted in clinical and neuroscience research, but it was also clear from the beginning that it suffered from limitations when mapping complex configurations, such as crossing fibres. In this review, we highlight the main steps that have led the field of diffusion imaging to move from the tensor model to the adoption of diffusion and fibre orientation density functions as a more effective way to describe the complexity of white matter organization within each brain voxel. Among several techniques, spherical deconvolution has emerged today as one of the main approaches to model multiple fibre orientations and for tractography applications. Here we illustrate the main concepts and the reasoning behind this technique, as well as the latest developments in the field. The final part of this review provides practical guidelines and recommendations on how to set up processing and acquisition protocols suitable for spherical deconvolution.
137 citations
TL;DR: This review highlights the invaluable contribution of diffusion‐weighted imaging in neuroscience, presents its limitations and proposes new challenges for future generations who may wish to exploit this powerful technology to gain novel insights.
Abstract: Diffusion-weighted imaging has pushed the boundaries of neuroscience by allowing us to examine the white matter microstructure of the living human brain. By doing so, it has provided answers to fundamental neuroscientific questions, launching a new field of research that had been largely inaccessible. We briefly summarize key questions that have historically been raised in neuroscience concerning the brain's white matter. We then expand on the benefits of diffusion-weighted imaging and its contribution to the fields of brain anatomy, functional models and plasticity. In doing so, this review highlights the invaluable contribution of diffusion-weighted imaging in neuroscience, presents its limitations and proposes new challenges for future generations who may wish to exploit this powerful technology to gain novel insights.
110 citations
TL;DR: Comparisons with classical k‐t FOCUSS, k‐ t SLR, L+S and the state‐of‐the‐art CNN‐based method on in vivo datasets show the proposed DIMENSION method can achieve improved reconstruction results in shorter time.
Abstract: Dynamic MR image reconstruction from incomplete k-space data has generated great research interest due to its capability in reducing scan time. Nevertheless, the reconstruction problem is still challenging due to its ill-posed nature. Most existing methods either suffer from long iterative reconstruction time or explore limited prior knowledge. This paper proposes a dynamic MR imaging method with both k-space and spatial prior knowledge integrated via multi-supervised network training, dubbed as DIMENSION. Specifically, the DIMENSION architecture consists of a frequential prior network for updating the k-space with its network prediction and a spatial prior network for capturing image structures and details. Furthermore, a multi-supervised network training technique is developed to constrain the frequency domain information and the spatial domain information. The comparisons with classical k-t FOCUSS, k-t SLR, L+S and the state-of-the-art CNN-based method on in vivo datasets show our method can achieve improved reconstruction results in shorter time.
91 citations
TL;DR: This review discusses ex vivo diffusion magnetic resonance imaging as an important research tool for neuroanatomical investigations and the validation of in vivo dMRI techniques, with a focus on the human brain, and gives particular emphasis to the delineation of layered gray matter structure with ex vivo d MRI.
Abstract: This review discusses ex vivo diffusion magnetic resonance imaging (dMRI) as an important research tool for neuroanatomical investigations and the validation of in vivo dMRI techniques, with a focus on the human brain. We review the challenges posed by the properties of post-mortem tissue, and discuss state-of-the-art tissue preparation methods and recent advances in pulse sequences and acquisition techniques to tackle these. We then review recent ex vivo dMRI studies of the human brain, highlighting the validation of white matter orientation estimates and the atlasing and mapping of large subcortical structures. We also give particular emphasis to the delineation of layered gray matter structure with ex vivo dMRI, as this application illustrates the strength of its mesoscale resolution over large fields of view. We end with a discussion and outlook on future and potential directions of the field.
90 citations
TL;DR: This review article summarizes the recent literature on glycerophosphocholine metabolism with respect to its cancer biology and its detection by magnetic resonance spectroscopy applications.
Abstract: Activated choline metabolism is a hallmark of carcinogenesis and tumor progression, which leads to elevated levels of phosphocholine and glycerophosphocholine in all types of cancer tested so far. Magnetic resonance spectroscopy applications have played a key role in detecting these elevated choline phospholipid metabolites. To date, the majority of cancer-related studies have focused on phosphocholine and the Kennedy pathway, which constitutes the biosynthesis pathway for membrane phosphatidylcholine. Fewer and more recent studies have reported on the importance of glycerophosphocholine in cancer. In this review article, we summarize the recent literature on glycerophosphocholine metabolism with respect to its cancer biology and its detection by magnetic resonance spectroscopy applications.
67 citations
TL;DR: The purpose of this study was to evaluate temporal stability, multi‐center reproducibility and the influence of covariates on a multimodal MR protocol for quantitative muscle imaging and to facilitate its use as a standardized protocol for evaluation of pathology in skeletal muscle.
Abstract: The purpose of this study was to evaluate temporal stability, multi-center reproducibility and the influence of covariates on a multimodal MR protocol for quantitative muscle imaging and to facilitate its use as a standardized protocol for evaluation of pathology in skeletal muscle. Quantitative T2, quantitative diffusion and four-point Dixon acquisitions of the calf muscles of both legs were repeated within one hour. Sixty-five healthy volunteers (31 females) were included in one of eight 3-T MR systems. Five traveling subjects were examined in six MR scanners. Average values over all slices of water-T2 relaxation time, proton density fat fraction (PDFF) and diffusion metrics were determined for seven muscles. Temporal stability was tested with repeated measured ANOVA and two-way random intraclass correlation coefficient (ICC). Multi-center reproducibility of traveling volunteers was assessed by a two-way mixed ICC. The factors age, body mass index, gender and muscle were tested for covariance. ICCs of temporal stability were between 0.963 and 0.999 for all parameters. Water-T2 relaxation decreased significantly (P < 10(-3)) within one hour by similar to 1 ms. Multi-center reproducibility showed ICCs within 0.879-0.917 with the lowest ICC for mean diffusivity. Different muscles showed the highest covariance, explaining 20-40% of variance for observed parameters. Standardized acquisition and processing of quantitative muscle MRI data resulted in high comparability among centers. The imaging protocol exhibited high temporal stability over one hour except for water T2 relaxation times. These results show that data pooling is feasible and enables assembling data from patients with neuromuscular diseases, paving the way towards larger studies of rare muscle disorders.
TL;DR: This review describes each technique, the technical issues involved with CESTMRI and each specific technique, and the merits and challenges associated with applying each CEST MRI technique to study tumor metabolism.
Abstract: Chemical exchange saturation transfer (CEST) is a relatively new contrast mechanism for MRI. CEST MRI exploits a specific MR frequency (chemical shift) of a molecule while generating an image with good spatial resolution using standard MRI techniques, combining the specificity of MRS with the spatial resolution of MRI. Many CEST MRI acquisition methods have been developed to improve analyses of tumor metabolism. GluCEST, CrCEST, and LATEST can map glutamate, creatine, and lactate, which are important metabolites involved in tumor metabolism. GlucoCEST MRI tracks the pharmacokinetics of glucose transport and cell internalization within tumors. CatalyCEST MRI detects enzyme catalysis that changes a substrate CEST agent. AcidoCEST MRI measures extracellular pH of the tumor microenvironment by exploiting a ratio of two pH-dependent CEST signals. This review describes each technique, the technical issues involved with CEST MRI and each specific technique, and the merits and challenges associated with applying each CEST MRI technique to study tumor metabolism.
TL;DR: To determine individual glucose hydroxyl exchange rates at physiological conditions and use this information for numerical optimization of glucoCEST/CESL preparation.
Abstract: Aims To determine individual glucose hydroxyl exchange rates at physiological conditions and use this information for numerical optimization of glucoCEST/CESL preparation. To give guidelines for in vivo glucoCEST/CESL measurement parameters at clinical and ultra-high field strengths. Methods Five glucose solution samples at different pH values were measured at 14.1 T at various B1 power levels. Multi-B1 -Z-spectra Bloch-McConnell fits at physiological pH were further improved by the fitting of Z-spectra of five pH values simultaneously. The obtained exchange rates were used in a six-pool Bloch-McConnell simulation including a tissue-like water pool and semi-solid MT pool with different CEST and CESL presaturation pulse trains. In vivo glucose injection experiments were performed in a tumor mouse model at 7 T. Results and discussion Glucose Z-spectra could be fitted with four exchanging pools at 0.66, 1.28, 2.08 and 2.88 ppm. Corresponding hydroxyl exchange rates could be determined at pH = 7.2, T = 37°C and 1X PBS. Simulation of saturation transfer for this glucose system in a gray matter-like and a tumor-like system revealed optimal pulses at different field strengths of 9.4, 7 and 3 T. Different existing sequences and approaches are simulated and discussed. The optima found could be experimentally verified in an animal model at 7 T. Conclusion For the determined fast exchange regime, presaturation pulses in the spin-lock regime (long recover time, short yet strong saturation) were found to be optimal. This study gives an estimation for optimization of the glucoCEST signal in vivo on the basis of glucose exchange rate at physiological conditions.
TL;DR: This review focuses on the pulse sequences and associated techniques under development that have pushed the limits of image quality and spatial resolution in diffusion‐weighted MRI.
Abstract: Diffusion-weighted imaging, a contrast unique to MRI, is used for assessment of tissue microstructure in vivo. However, this exquisite sensitivity to finer scales far above imaging resolution comes at the cost of vulnerability to errors caused by sources of motion other than diffusion motion. Addressing the issue of motion has traditionally limited diffusion-weighted imaging to a few acquisition techniques and, as a consequence, to poorer spatial resolution than other MRI applications. Advances in MRI imaging methodology have allowed diffusion-weighted MRI to push to ever higher spatial resolution. In this review we focus on the pulse sequences and associated techniques under development that have pushed the limits of image quality and spatial resolution in diffusion-weighted MRI.
TL;DR: Assessment of the validity of MRS measures of human brain metabolite concentrations by comparing multiple M RS measures acquired using different MRS acquisition sequences.
Abstract: Purpose In vivo magnetic resonance spectroscopy (MRS) is the only technique capable of non-invasively assessing metabolite concentrations in the brain. The lack of alternative methods makes validation of MRS measures challenging. The aim of this study is to assess the validity of MRS measures of human brain metabolite concentrations by comparing multiple MRS measures acquired using different MRS acquisition sequences. Methods Single-voxel SPECIAL and MEGA-PRESS MR spectra were acquired from both the dorsolateral prefrontal cortex and primary motor cortices in 15 healthy subjects. The SPECIAL spectrum, as well as both the edit-off and difference spectra of MEGA-PRESS were each analyzed in LCModel to obtain estimates of the absolute concentrations of total choline (TCh; glycerophosphocholine + phosphocholine), total creatine (TCr; creatine + phosphocreatine), N-acetylaspartate (NAA), N-acetylaspartylglutamate (NAAG), NAA + NAAG, glutamate (Glu), glutamine (Gln), Glu + Gln, scyllo-inositol (Scyllo), myo-inositol (Ins), glutathione (GSH), γ-aminobutyric acid (GABA), lactate (Lac) and aspartate (Asp). Then, having obtained up to three independent measures of each metabolite per brain region per subject, correlations between the different measures were assessed. Results The degree of correlation between measures varied greatly across both the metabolites and sequences tested. As expected, metabolites with the most prominent spectral peaks (TCh, TCr, NAA + NAAG, Ins and Glu) had the most well-correlated measures between methods, while metabolites with less prominent spectral peaks (Lac, Gln, GABA, Asp, and NAAG) tended to have poorly-correlated measures between methods. Some metabolites with relatively less prominent spectral peaks (GSH, Scyllo) had fairly well-correlated measures between some methods. Combining metabolites improved the agreement between methods for measures of NAA + NAAG, but not for Glu + Gln. Conclusions Given that the ground truth for in vivo MRS measures is never known, the method proposed here provides a promising means to assess the validity of in vivo MRS measures, which has not yet been explored widely.
TL;DR: A compact format for visualizing two‐dimensional arrays of the distributions, new scalar parameters quantifying intra‐voxel heterogeneity, and a binning procedure giving maps of all relevant parameters for each of the components resolved in the multidimensional distribution space are proposed.
Abstract: Conventional diffusion MRI yields voxel-averaged parameters that suffer from ambiguities for heterogeneous anisotropic materials such as brain tissue. Using principles from solid-state NMR spectroscopy, we have previously introduced the shape of the diffusion encoding tensor as a separate acquisition dimension that disentangles isotropic and anisotropic contributions to the observed diffusivities, thereby allowing for unconstrained data inversion into diffusion tensor distributions with "size," "shape," and orientation dimensions. Here we combine our recent non-parametric data inversion algorithm and data acquisition protocol with an imaging pulse sequence to demonstrate spatial mapping of diffusion tensor distributions using a previously developed composite phantom with multiple isotropic and anisotropic components. We propose a compact format for visualizing two-dimensional arrays of the distributions, new scalar parameters quantifying intra-voxel heterogeneity, and a binning procedure giving maps of all relevant parameters for each of the components resolved in the multidimensional distribution space.
TL;DR: The utility of 3D‐EPSI in differentiating TP from PsP with high sensitivity and specificity in patients with glioblastomas is investigated, indicating the utility of the technique for planning adequate treatment and for estimating clinical outcome measures and future prognosis.
Abstract: Accurate differentiation of true progression (TP) from pseudoprogression (PsP) in patients with glioblastomas (GBMs) is essential for planning adequate treatment and for estimating clinical outcome measures and future prognosis. The purpose of this study was to investigate the utility of three‐dimensional echo planar spectroscopic imaging (3D‐EPSI) in distinguishing TP from PsP in GBM patients. For this institutional review board approved and HIPAA compliant retrospective study, 27 patients with GBM demonstrating enhancing lesions within six months of completion of concurrent chemo‐radiation therapy were included. Of these, 18 were subsequently classified as TP and 9 as PsP based on histological features or follow‐up MRI studies. Parametric maps of choline/creatine (Cho/Cr) and choline/N‐acetylaspartate (Cho/NAA) were computed and co‐registered with post‐contrast T 1‐weighted and FLAIR images. All lesions were segmented into contrast enhancing (CER), immediate peritumoral (IPR), and distal peritumoral (DPR) regions. For each region, Cho/Cr and Cho/NAA ratios were normalized to corresponding metabolite ratios from contralateral normal parenchyma and compared between TP and PsP groups. Logistic regression analyses were performed to obtain the best model to distinguish TP from PsP. Significantly higher Cho/NAA was observed from CER (2.69 ± 1.00 versus 1.56 ± 0.51, p = 0.003), IPR (2.31 ± 0.92 versus 1.53 ± 0.56, p = 0.030), and DPR (1.80 ± 0.68 versus 1.19 ± 0.28, p = 0.035) regions in TP patients compared with those with PsP. Additionally, significantly elevated Cho/Cr (1.74 ± 0.44 versus 1.34 ± 0.26, p = 0.023) from CER was observed in TP compared with PsP. When these parameters were incorporated in multivariate regression analyses, a discriminatory model with a sensitivity of 94% and a specificity of 87% was observed in distinguishing TP from PsP. These results indicate the utility of 3D‐EPSI in differentiating TP from PsP with high sensitivity and specificity.
TL;DR: A novel, data‐driven motion correction technique has been developed that can suppress motion artefacts from motion‐corrupted MR images and outperformed an iterative entropy minimization method in terms of the SSIM index and normalized root mean squared error.
Abstract: The suppression of motion artefacts from MR images is a challenging task. The purpose of this paper was to develop a standalone novel technique to suppress motion artefacts in MR images using a data-driven deep learning approach. A simulation framework was developed to generate motion-corrupted images from motion-free images using randomly generated motion profiles. An Inception-ResNet deep learning network architecture was used as the encoder and was augmented with a stack of convolution and upsampling layers to form an encoder-decoder network. The network was trained on simulated motion-corrupted images to identify and suppress those artefacts attributable to motion. The network was validated on unseen simulated datasets and real-world experimental motion-corrupted in vivo brain datasets. The trained network was able to suppress the motion artefacts in the reconstructed images, and the mean structural similarity (SSIM) increased from 0.9058 to 0.9338. The network was also able to suppress the motion artefacts from the real-world experimental dataset, and the mean SSIM increased from 0.8671 to 0.9145. The motion correction of the experimental datasets demonstrated the effectiveness of the motion simulation generation process. The proposed method successfully removed motion artefacts and outperformed an iterative entropy minimization method in terms of the SSIM index and normalized root mean squared error, which were 5-10% better for the proposed method. In conclusion, a novel, data-driven motion correction technique has been developed that can suppress motion artefacts from motion-corrupted MR images. The proposed technique is a standalone, post-processing method that does not interfere with data acquisition or reconstruction parameters, thus making it suitable for routine clinical practice.
TL;DR: Magnetic resonance fingerprinting is a quantitative imaging technique that maps multiple tissue properties through pseudorandom signal excitation and dictionary‐based reconstruction.
Abstract: Magnetic resonance fingerprinting (MRF) is a quantitative imaging technique that maps multiple tissue properties through pseudorandom signal excitation and dictionary-based reconstruction. The aim of this study is to estimate and validate partial volumes from MRF signal evolutions (PV-MRF), and to characterize possible sources of error. Partial volume model inversion (pseudoinverse) and dictionary-matching approaches to calculate brain tissue fractions (cerebrospinal fluid, gray matter, white matter) were compared in a numerical phantom and seven healthy subjects scanned at 3 T. Results were validated by comparison with ground truth in simulations and ROI analysis in vivo. Simulations investigated tissue fraction errors arising from noise, undersampling artifacts, and model errors. An expanded partial volume model was investigated in a brain tumor patient. PV-MRF with dictionary matching is robust to noise, and estimated tissue fractions are sensitive to model errors. A 6% error in pure tissue T1 resulted in average absolute tissue fraction error of 4% or less. A partial volume model within these accuracy limits could be semi-automatically constructed in vivo using k-means clustering of MRF-mapped relaxation times. Dictionary-based PV-MRF robustly identifies pure white matter, gray matter and cerebrospinal fluid, and partial volumes in subcortical structures. PV-MRF could also estimate partial volumes of solid tumor and peritumoral edema. We conclude that PV-MRF can attribute subtle changes in relaxation times to altered tissue composition, allowing for quantification of specific tissues which occupy a fraction of a voxel.
TL;DR: A technique for simultaneous multislice (SMS) cardiac magnetic resonance fingerprinting (cMRF), which improves the slice coverage when quantifying myocardial T1, T2, and M0 mapping and enables the acquisition of maps with fewer artifacts when using SMS cMRF at higher MB factors.
Abstract: This study introduces a technique for simultaneous multislice (SMS) cardiac magnetic resonance fingerprinting (cMRF), which improves the slice coverage when quantifying myocardial T1, T2 , and M0 . The single-slice cMRF pulse sequence was modified to use multiband (MB) RF pulses for SMS imaging. Different RF phase schedules were used to excite each slice, similar to POMP or CAIPIRINHA, which imparts tissues with a distinguishable and slice-specific magnetization evolution over time. Because of the high net acceleration factor (R = 48 in plane combined with the slice acceleration), images were first reconstructed with a low rank technique before matching data to a dictionary of signal timecourses generated by a Bloch equation simulation. The proposed method was tested in simulations with a numerical relaxation phantom. Phantom and in vivo cardiac scans of 10 healthy volunteers were also performed at 3 T. With single-slice acquisitions, the mean relaxation times obtained using the low rank cMRF reconstruction agree with reference values. The low rank method improves the precision in T1 and T2 for both single-slice and SMS cMRF, and it enables the acquisition of maps with fewer artifacts when using SMS cMRF at higher MB factors. With this technique, in vivo cardiac maps were acquired from three slices simultaneously during a breathhold lasting 16 heartbeats. SMS cMRF improves the efficiency and slice coverage of myocardial T1 and T2 mapping compared with both single-slice cMRF and conventional cardiac mapping sequences. Thus, this technique is a first step toward whole-heart simultaneous T1 and T2 quantification with cMRF.
TL;DR: It was demonstrated that identifying spectral redundancies of Cest data by principal component analysis (PCA) in combination with an appropriate data–driven extraction of relevant information can be used for an effective and robust denoising of CEST spectra.
Abstract: High image signal-to-noise ratio (SNR) is required to reliably detect the inherently small chemical exchange saturation transfer (CEST) effects in vivo. In this study, it was demonstrated that identifying spectral redundancies of CEST data by principal component analysis (PCA) in combination with an appropriate data-driven extraction of relevant information can be used for an effective and robust denoising of CEST spectra. The relationship between the number of relevant principal components and SNR was studied on fitted in vivo Z-spectra with artificially introduced noise. Three different data-driven criteria to automatically determine the optimal number of necessary components were investigated. In addition, these criteria facilitate straightforward assessment of data quality that could provide guidance for CEST MR protocols in terms of SNR. Insights were applied to achieve a robust denoising of highly sampled low power Z-spectra of the human brain at 3 and 7 T. The median criterion provided the best estimation for the optimal number of components consistently for all three investigated artificial noise levels. Application of the denoising technique to in vivo data revealed a considerable increase in image quality for the amide and rNOE contrast with a considerable SNR gain. At 7 T the denoising capability was quantified to be comparable or even superior to an averaging of six measurements. The proposed denoising algorithm enables an efficient and robust denoising of CEST data by combining PCA with appropriate data-driven truncation criteria. With this generally applicable technique at hand, small CEST effects can be reliably detected without the need for repeated measurements.
TL;DR: The ASL‐MRICloud tool was implemented to be compatible with data acquired by scanners from all major MRI manufacturers, is capable of processing several common forms of AsL, including pseudo‐continuous ASL and pulsed ASL, and can process single‐delay and multi‐delay ASL data.
Abstract: Arterial spin labeling (ASL) MRI is increasingly used in research and clinical settings. The purpose of this work is to develop a cloud-based tool for ASL data processing, referred to as ASL-MRICloud, which may be useful to the MRI community. In contrast to existing ASL toolboxes, which are based on software installation on the user's local computer, ASL-MRICloud uses a web browser for data upload and results download, and the computation is performed on the remote server. As such, this tool is independent of the user's operating system, software version, and CPU speed. The ASL-MRICloud tool was implemented to be compatible with data acquired by scanners from all major MRI manufacturers, is capable of processing several common forms of ASL, including pseudo-continuous ASL and pulsed ASL, and can process single-delay and multi-delay ASL data. The outputs of ASL-MRICloud include absolute and relative values of cerebral blood flow, arterial transit time, voxel-wise masks indicating regions with potential hyper-perfusion and hypo-perfusion, and an image quality index. The ASL tool is also integrated with a T1 -based brain segmentation and normalization tool in MRICloud to allow generation of parametric maps in standard brain space as well as region-of-interest values. The tool was tested on a large data set containing 309 ASL scans as well as on publicly available ASL data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study.
TL;DR: Dynamic 129Xe spectroscopy is a simple and sensitive tool that probes the temporal variability of gas exchange and may prove useful in discerning the underlying causes of its impairment.
Abstract: The spectral parameters of hyperpolarized 129 Xe exchanging between airspaces, interstitial barrier, and red blood cells (RBCs) are sensitive to pulmonary pathophysiology. This study sought to evaluate whether the dynamics of 129 Xe spectroscopy provide additional insight, with particular focus on quantifying cardiogenic oscillations in the RBC resonance. 129 Xe spectra were dynamically acquired in eight healthy volunteers and nine subjects with idiopathic pulmonary fibrosis (IPF). 129 Xe FIDs were collected every 20 ms (TE = 0.932 ms, 512 points, dwell time = 32 μs, flip angle ≈ 20°) during a 16 s breathing maneuver. The FIDs were pre-processed using the spectral improvement by Fourier thresholding technique (SIFT) and fit in the time domain to determine the airspace, interstitial barrier, and RBC spectral parameters. The RBC and gas resonances were fit to a Lorentzian lineshape, while the barrier was fit to a Voigt lineshape to account for its greater structural heterogeneity. For each complex resonance the amplitude, chemical shift, linewidth(s), and phase were calculated. The time-averaged spectra confirmed that the RBC to barrier amplitude ratio (RBC:barrier ratio) and RBC chemical shift are both reduced in IPF subjects. Their temporal dynamics showed that all three 129 Xe resonances are affected by the breathing maneuver. Most notably, several RBC spectral parameters exhibited prominent oscillations at the cardiac frequency, and their peak-to-peak variation differed between IPF subjects and healthy volunteers. In the IPF cohort, oscillations were more prominent in the RBC amplitude (16.8 ± 5.2 versus 9.7 ± 2.9%; P = 0.008), chemical shift (0.43 ± 0.33 versus 0.083 ± 0.05 ppm; P < 0.001), and phase (7.7 ± 5.6 versus 1.4 ± 0.8°; P < 0.001). Dynamic 129 Xe spectroscopy is a simple and sensitive tool that probes the temporal variability of gas exchange and may prove useful in discerning the underlying causes of its impairment.
TL;DR: The purpose of this review is to highlight recent computational and statistical advances in diffusion MRI and to put these advances into context by comparison with the more traditional computational methods that are in popular clinical and scientific use.
Abstract: Computational methods are crucial for the analysis of diffusion magnetic resonance imaging (MRI) of the brain. Computational diffusion MRI can provide rich information at many size scales, including local microstructure measures such as diffusion anisotropies or apparent axon diameters, whole-brain connectivity information that describes the brain's wiring diagram and population-based studies in health and disease. Many of the diffusion MRI analyses performed today were not possible five, ten or twenty years ago, due to the requirements for large amounts of computer memory or processor time. In addition, mathematical frameworks had to be developed or adapted from other fields to create new ways to analyze diffusion MRI data. The purpose of this review is to highlight recent computational and statistical advances in diffusion MRI and to put these advances into context by comparison with the more traditional computational methods that are in popular clinical and scientific use. We aim to provide a high-level overview of interest to diffusion MRI researchers, with a more in-depth treatment to illustrate selected computational advances.
TL;DR: The metabolic and neurotransmitter pathways that can be measured by 13C MRS are reviewed and key findings on the linkage between neuroenergetics, neurotransmitter cycling, and brain function are found.
Abstract: In the last 25 years 13 C MRS has been established as the only noninvasive method for measuring glutamate neurotransmission and cell specific neuroenergetics. Although technically and experimentally challenging 13 C MRS has already provided important new information on the relationship between neuroenergetics and neuronal function, the high energy cost of brain function in the resting state and the role of altered neuroenergetics and neurotransmitter cycling in disease. In this paper we review the metabolic and neurotransmitter pathways that can be measured by 13 C MRS and key findings on the linkage between neuroenergetics, neurotransmitter cycling, and brain function. Applications of 13 C MRS to neurological and psychiatric disease as well as brain cancer are reviewed. Recent technological developments that may help to overcome spatial resolution and brain coverage limitations of 13 C MRS are discussed.
TL;DR: How diffusion imaging can be exploited to investigate micro‐, meso‐ and macro‐scale properties of the brain structure and discuss how they are affected by different pathological substrates is described.
Abstract: Diffusion imaging has been instrumental in understanding damage to the central nervous system as a result of its sensitivity to microstructural changes. Clinical applications of diffusion imaging have grown exponentially over the past couple of decades in many neurological and neurodegenerative diseases, such as multiple sclerosis (MS). For several reasons, MS has been extensively researched using advanced neuroimaging techniques, which makes it an 'example disease' to illustrate the potential of diffusion imaging for clinical applications. In addition, MS pathology is characterized by several key processes competing with each other, such as inflammation, demyelination, remyelination, gliosis and axonal loss, enabling the specificity of diffusion to be challenged. In this review, we describe how diffusion imaging can be exploited to investigate micro-, meso- and macro-scale properties of the brain structure and discuss how they are affected by different pathological substrates. Conclusions from the literature are that larger studies are needed to confirm the exciting results from initial investigations before current trends in diffusion imaging can be translated to the neurology clinic. Also, for a comprehensive understanding of pathological processes, it is essential to take a multiple-level approach, in which information at the micro-, meso- and macroscopic scales is fully integrated.
TL;DR: This work demonstrates that, in a well‐controlled environment, both PC‐MRI and CFD might bring reliable and correlated flow quantities when a proper methodology to reduce the errors is followed.
Abstract: Several well-resolved 4D Flow MRI acquisitions of an idealized rigid flow phantom featuring an aneurysm, a curved channel as well as a bifurcation were performed under pulsatile regime. The resulting hemodynamics were processed to remove MRI artifacts. Subsequently, they were compared with CFD predictions computed on the same flow domain, using an in-house high-order low dissipative flow solver. Results show that reaching a good agreement is not straightforward but requires proper treatments of both techniques. Several sources of discrepancies are highlighted and their impact on the final correlation evaluated. While a very poor correlation ($r^2$ = 0.63) is found in the entire domain between raw MRI and CFD data, correlation as high as $r^2$ = 0.97 is found when artifacts are removed by post-processing the MR data and down sampling the CFD results to match the MRI spatial and temporal resolutions. This work demonstrates that, in a well-controlled environment, both PC-MRI and CFD might bring reliable and correlated flow quantities when a proper methodology to reduce the errors is followed.
TL;DR: It can be concluded that determination of on‐resonance water (sh)MOLLI T1 independent of fat, iron and macroscopic field inhomogeneities was possible in phantoms and human subjects.
Abstract: Modified Look-Locker inversion recovery (MOLLI) T1 mapping sequences can be useful in cardiac and liver tissue characterization, but determining underlying water T1 is confounded by iron, fat and frequency offsets. This article proposes an algorithm that provides an independent water MOLLI T1 (referred to as on-resonance water T1 ) that would have been measured if a subject had no fat and normal iron, and imaging had been done on resonance. Fifteen NiCl2 -doped agar phantoms with different peanut oil concentrations and 30 adults with various liver diseases, nineteen (63.3%) with liver steatosis, were scanned at 3 T using the shortened MOLLI (shMOLLI) T1 mapping, multiple-echo spoiled gradient-recalled echo and 1 H MR spectroscopy sequences. An algorithm based on Bloch equations was built in MATLAB, and water shMOLLI T1 values of both phantoms and human participants were determined. The quality of the algorithm's result was assessed by Pearson's correlation coefficient between shMOLLI T1 values and spectroscopically determined T1 values of the water, and by linear regression analysis. Correlation between shMOLLI and spectroscopy-based T1 values increased, from r = 0.910 (P < 0.001) to r = 0.998 (P < 0.001) in phantoms and from r = 0.493 (for iron-only correction; P = 0.005) to r = 0.771 (for iron, fat and off-resonance correction; P < 0.001) in patients. Linear regression analysis revealed that the determined water shMOLLI T1 values in patients were independent of fat and iron. It can be concluded that determination of on-resonance water (sh)MOLLI T1 independent of fat, iron and macroscopic field inhomogeneities was possible in phantoms and human subjects.
TL;DR: The influence of three distinct isoflurane protocols was studied with pseudo‐continuous ASL in two different mouse strains, and CVR was shown to be dependent on baseline CBF, regardless of the anesthesia protocol used.
Abstract: Arterial spin labeling (ASL)-MRI can noninvasively map cerebral blood flow (CBF) and cerebrovascular reactivity (CVR), potential biomarkers of cognitive impairment and dementia. Mouse models of disease are frequently used in translational MRI studies, which are commonly performed under anesthesia. Understanding the influence of the specific anesthesia protocol used on the measured parameters is important for accurate interpretation of hemodynamic studies with mice. Isoflurane is a frequently used anesthetic with vasodilative properties. Here, the influence of three distinct isoflurane protocols was studied with pseudo-continuous ASL in two different mouse strains. The first protocol was a free-breathing set-up with medium concentrations, the second a free-breathing set-up with low induction and maintenance concentrations, and the third a set-up with medium concentrations and mechanical ventilation. A protocol with the vasoconstrictive anesthetic medetomidine was used as a comparison. As expected, medium isoflurane anesthesia resulted in significantly higher CBF and lower CVR values than medetomidine (median whole-brain CBF of 157.7 vs 84.4 mL/100 g/min and CVR of 0.54 vs 51.7% in C57BL/6 J mice). The other two isoflurane protocols lowered the CBF and increased the CVR values compared with medium isoflurane anesthesia, without obvious differences between them (median whole-brain CBF of 138.9 vs 131.7 mL/100 g/min and CVR of 10.0 vs 9.6%, in C57BL/6 J mice). Furthermore, CVR was shown to be dependent on baseline CBF, regardless of the anesthesia protocol used.
TL;DR: To test the feasibility of regional fully quantitative ventilation measurement in free breathing derived by phase‐resolved functional lung (PREFUL) MRI in the supine and prone positions, the influence of T2* relaxation time on ventilation quantification is assessed.
Abstract: PURPOSE To test the feasibility of regional fully quantitative ventilation measurement in free breathing derived by phase-resolved functional lung (PREFUL) MRI in the supine and prone positions. In addition, the influence of T2 * relaxation time on ventilation quantification is assessed. METHODS Twelve healthy volunteers underwent functional MRI at 1.5 T using a 2D triple-echo spoiled gradient echo sequence allowing for quantitative measurement of T2 * relaxation time. Minute ventilation (ΔV) was quantified by conventional fractional ventilation (FV) and the newly introduced regional ventilation (VR), which corrects volume errors due to image registration. ΔVFV versus ΔVVR and ΔVVR versus ΔVVR with T2 * correction were compared using Bland-Altman plots and correlation analysis. The repeatability and physiological plausibility of all measurements were tested in the supine and prone positions. RESULTS On global and regional scales a strong correlation was observed between ΔVFV versus ΔVVR and ΔVVR versus ΔVVRT2* (r > 0.93); however, regional Bland-Altman analysis showed systematic differences (p < 0.0001). Unlike ΔVVRT2* , ΔVVR and ΔVFV showed expected physiologic anterior-posterior gradients, which decreased in the supine but not in the prone position at second measurement during 3 min in the same position. For all quantification methods a moderate repeatability (coefficient of variation <20%) of ventilation was found. CONCLUSION A fully quantified regional ventilation measurement using ΔVVR in free breathing is feasible and shows physiologically plausible results. In contrast to conventional ΔVFV, volume errors due to image registration are eliminated with the ΔVVR approach. However, correction for the T2 * effect remains challenging.