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Magnetization Transfer Contrast (MTC) and Chemical Exchange Saturation Transfer (CEST) experiments measure the transfer of magnetization from molecular protons to the solvent water protons, an effect that becomes apparent as an MRI signal loss (“saturation”). This allows molecular information to be accessed with the enhanced sensitivity of MRI. In analogy to Magnetic Resonance Spectroscopy (MRS), these saturation data are presented as a function of the chemical shift of participating proton groups, e.g. OH, NH, NH2, which is called a Z-spectrum. In tissue, these Z-spectra contain the convolution of multiple saturation transfer effects, including nuclear Overhauser enhancements (NOEs) and chemical exchange contributions from protons in semi-solid and mobile macromolecules or tissue metabolites. As a consequence, their appearance depends on the magnetic field strength (B0) and pulse sequence parameters such as B1 strength, pulse shape and length, and interpulse delay, which presents a major problem for quantification and reproducibility of MTC and CEST effects. The use of higher B0 can bring several advantages. In addition to higher detection sensitivity (signal-to-noise ratio, SNR), both MTC and CEST studies benefit from longer water T1 allowing the saturation transferred to water to be retained longer. While MTC studies are non-specific at any field strength, CEST specificity is expected to increase at higher field because of a larger chemical shift dispersion of the resonances of interest (similar to MRS). In addition, shifting to a slower exchange regime at higher B0 facilitates improved detection of the guanidinium protons of creatine and the inherently broad resonances of the amine protons in glutamate and the hydroxyl protons in myoinositol, glycogen, and glucosaminoglycans. Finally, due to the higher mobility of the contributing protons in CEST versus MTC, many new pulse sequences can be designed to more specifically edit for CEST signals and to remove MTC contributions. •Basics of nuclear Overhauser enhancement (NOE) and chemical exchange saturation transfer (CEST).•Comprehensive description of the features of the endogenous saturation spectrum (Z-spectrum) in MRI.•Explanation of advantages of using higher magnetic field for CEST MRI.•Critical assessment of CEST data analysis approaches.•Critical assessment of early CEST applications for the brain.

Phosphocreatine (PCr) plays a vital role in neuron and myocyte energy homeostasis. Currently, there are no routine diagnostic tests to noninvasively map PCr distribution with clinically relevant spatial resolution and scan time. Here, we demonstrate that artificial neural network-based chemical exchange saturation transfer (ANNCEST) can be used to rapidly quantify PCr concentration with robust immunity to commonly seen MRI interferences. High-quality PCr mapping of human skeletal muscle, as well as the information of exchange rate, magnetic field and radio-frequency transmission inhomogeneities, can be obtained within 1.5 min on a 3 T standard MRI scanner using ANNCEST. For further validation, we apply ANNCEST to measure the PCr concentrations in exercised skeletal muscle. The ANNCEST outcomes strongly correlate with those from 31 P magnetic resonance spectroscopy ( R  = 0.813, p  < 0.001, t test). These results suggest that ANNCEST has potential as a cost-effective and widely available method for measuring PCr and diagnosing related diseases. Phosphocreatine plays a vital role in cellular energetic homeostasis, but there are no routine diagnostic tests to noninvasively map the distribution with clinically relevant spatial resolution. Here, the authors develop and validate a noninvasive approach for quantifying and imaging phosphocreatine, without contrast agents, on widely available clinical MRI scanners with artificial neural networks.

The purpose of this paper is to extend the single-subject Eve atlas from Johns Hopkins University, which currently contains diffusion tensor and T1-weighted anatomical maps, by including contrast based on quantitative susceptibility mapping. The new atlas combines a “deep gray matter parcellation map” (DGMPM) derived from a single-subject quantitative susceptibility map with the previously established “white matter parcellation map” (WMPM) from the same subject's T1-weighted and diffusion tensor imaging data into an MNI coordinate map named the “Everything Parcellation Map in Eve Space,” also known as the “EvePM.” It allows automated segmentation of gray matter and white matter structures. Quantitative susceptibility maps from five healthy male volunteers (30 to 33years of age) were coregistered to the Eve Atlas with AIR and Large Deformation Diffeomorphic Metric Mapping (LDDMM), and the transformation matrices were applied to the EvePM to produce automated parcellation in subject space. Parcellation accuracy was measured with a kappa analysis for the left and right structures of six deep gray matter regions. For multi-orientation QSM images, the Kappa statistic was 0.85 between automated and manual segmentation, with the inter-rater reproducibility Kappa being 0.89 for the human raters, suggesting “almost perfect” agreement between all segmentation methods. Segmentation seemed slightly more difficult for human raters on single-orientation QSM images, with the Kappa statistic being 0.88 between automated and manual segmentation, and 0.85 and 0.86 between human raters. Overall, this atlas provides a time-efficient tool for automated coregistration and segmentation of quantitative susceptibility data to analyze many regions of interest. These data were used to establish a baseline for normal magnetic susceptibility measurements for over 60 brain structures of 30- to 33-year-old males. Correlating the average susceptibility with age-based iron concentrations in gray matter structures measured by Hallgren and Sourander (1958) allowed interpolation of the average iron concentration of several deep gray matter regions delineated in the EvePM. [Display omitted] •We added a Quantitative Susceptibility Mapping (QSM) template to the Eve atlas.•We utilized MRI Studio software to coregister QSM images to the Eve atlas.•Automated segmentation took less than 24h for over sixty brain regions.•We derived a susceptibility-iron calibration curve based on known iron values.•This curve was used to determine iron concentration in other gray matter regions.

Current chemical exchange saturation transfer (CEST) neuroimaging protocols typically acquire CEST-weighted images, and, as such, do not essentially provide quantitative proton-specific exchange rates (or brain pH) and concentrations. We developed a dictionary-free MR fingerprinting (MRF) technique to allow CEST parameter quantification with a reduced data set. This was accomplished by subgrouping proton exchange models (SPEM), taking amide proton transfer (APT) as an example, into two-pool (water and semisolid macromolecules) and three-pool (water, semisolid macromolecules, and amide protons) models. A variable radiofrequency saturation scheme was used to generate unique signal evolutions for different tissues, reflecting their CEST parameters. The proposed MRF-SPEM method was validated using Bloch-McConnell equation-based digital phantoms with known ground-truth, which showed that MRF-SPEM can achieve a high degree of accuracy and precision for absolute CEST parameter quantification and CEST phantoms. For in-vivo studies at 3 T, using the same model as in the simulations, synthetic Z-spectra were generated using rates and concentrations estimated from the MRF-SPEM reconstruction and compared with experimentally measured Z-spectra as the standard for optimization. The MRF-SPEM technique can provide rapid and quantitative human brain CEST mapping. •A new MR fingerprinting concept was proposed to allow CEST quantification.•A varied RF saturation was designed to generate CEST signal evolutions.•Synthetic CEST MRI was used for validation of in-vivo CEST quantification.•The MRF-SPEM technique can provide rapid and quantitative human brain CEST mapping.

Detection of glycogen in vivo would have utility in the study of normal physiology and many disorders. Presently, the only magnetic resonance (MR) method available to study glycogen metabolism in vivo is ¹³C MR spectroscopy, but this technology is not routinely available on standard clinical scanners. Here, we show that glycogen can be detected indirectly through the water signal by using selective radio frequency (RF) saturation of the hydroxyl protons in the 0.5- to 1.5-ppm frequency range downfield from water. The resulting saturated spins are rapidly transferred to water protons via chemical exchange, leading to partial saturation of the water signal, a process now known as chemical exchange saturation transfer. This effect is demonstrated in glycogen phantoms at magnetic field strengths of 4.7 and 9.4 T, showing improved detection at higher field in adherence with MR exchange theory. Difference images obtained during RF irradiation at 1.0 ppm upfield and downfield of the water signal showed that glycogen metabolism could be followed in isolated, perfused mouse livers at 4.7 T before and after administration of glucagon. Glycogen breakdown was confirmed by measuring effluent glucose and, in separate experiments, by ¹³C NMR spectroscopy. This approach opens the way to image the distribution of tissue glycogen in vivo and to monitor its metabolism rapidly and noninvasively with MRI.

A major problem in the clinical management of patients with brain tumors is distinguishing tumor recurrence from radiation-induced necrosis after brain tumor therapy. Zhou et al . use an MRI technique called amide proton transfer imaging to noninvasively differentiate between these two pathologies. The approach is successfully evaluated by comparing two orthotopic glioma models with a radiation necrosis model in rats. It remains difficult to distinguish tumor recurrence from radiation necrosis after brain tumor therapy. Here we show that these lesions can be distinguished using the amide proton transfer (APT) magnetic resonance imaging (MRI) signals of endogenous cellular proteins and peptides as an imaging biomarker. When comparing two models of orthotopic glioma (SF188/V+ glioma and 9L gliosarcoma) with a model of radiation necrosis in rats, we could clearly differentiate viable glioma (hyperintense) from radiation necrosis (hypointense to isointense) by APT MRI. When we irradiated rats with U87MG gliomas, the APT signals in the irradiated tumors had decreased substantially by 3 d and 6 d after radiation. The amide protons that can be detected by APT provide a unique and noninvasive MRI biomarker for distinguishing viable malignancy from radiation necrosis and predicting tumor response to therapy.

The classic definition of the ischemic penumbra is a hypoperfused region in which metabolism is impaired, but still sufficient to maintain cellular polarization. Perfusion- and diffusion-weighted MRI (PWI, DWI) can identify regions of reduced perfusion and cellular depolarization, respectively, but it often remains unclear whether a PWI—DWI mismatch corresponds to benign oligemia or a true penumbra. We hypothesized that pH-weighted MRI (pHWI) can subdivide the PWI—DWI mismatch into these regions. Twenty-one rats underwent permanent middle cerebral artery occlusion and ischemic evolution over the first 3.5 h post-occlusion was studied using multiparametric MRI. End point was the stroke area defined by T2-hyperintensity at 24 h. In the acute phase, areas of reduced pH were always larger than or equal to DWI deficits and smaller than or equal to PWI deficits. Group analysis showed that pHWI deficits during this phase coincided with the resulting infarct area at endpoint. Final infarcts were smaller than PWI deficits (range 65% to 90%, depending on the severity of the occlusion) and much larger than acute DWI deficits. These data suggest that the outer boundary of the hypoperfused area showing a decrease in pH without DWI abnormality may correspond to the outer boundary of the ischemic penumbra, while the hypoperfused region at normal pH may correspond to benign oligemia. These first results show that pHWI can provide information complementary to PWI and DWI in the delineation of ischemic tissue.

Quantification of tissue magnetic susceptibility using MRI offers a non-invasive measure of important tissue components in the brain, such as iron and myelin, potentially providing valuable information about normal and pathological conditions during aging. Despite many advances made in recent years on imaging techniques of quantitative susceptibility mapping (QSM), accurate and robust automated segmentation tools for QSM images that can help generate universal and sharable susceptibility measures in a biologically meaningful set of structures are still not widely available. In the present study, we developed an automated process to segment brain nuclei and quantify tissue susceptibility in these regions based on a susceptibility multi-atlas library, consisting of 10 atlases with T1-weighted images, gradient echo (GRE) magnitude images and QSM images of brains with different anatomic patterns. For each atlas in this library, 10 regions of interest in iron-rich deep gray matter structures that are better defined by QSM contrast were manually labeled, including caudate, putamen, globus pallidus internal/external, thalamus, pulvinar, subthalamic nucleus, substantia nigra, red nucleus and dentate nucleus in both left and right hemispheres. We then tested different pipelines using different combinations of contrast channels to bring the set of labels from the multi-atlases to each target brain and compared them with the gold standard manual delineation. The results showed that the segmentation accuracy using dual contrasts QSM/T1 pipeline outperformed other dual-contrast or single-contrast pipelines. The dice values of 0.77 ± 0.09 using the QSM/T1 multi-atlas pipeline rivaled with the segmentation reliability obtained from multiple evaluators with dice values of 0.79 ± 0.07 and gave comparable or superior performance in segmenting subcortical nuclei in comparison with standard FSL FIRST or recent multi-atlas package of volBrain. The segmentation performance of the QSM/T1 multi-atlas was further tested on QSM images acquired using different acquisition protocols and platforms and showed good reliability and reproducibility with average dice of 0.79 ± 0.08 to manual labels and 0.89 ± 0.04 in an inter-protocol manner. The extracted quantitative magnetic susceptibility values in the deep gray matter nuclei also correlated well between different protocols with inter-protocol correlation constants all larger than 0.97. Such reliability and performance was ultimately validated in an external dataset acquired at another study site with consistent susceptibility measures obtained using the QSM/T1 multi-atlas approach in comparison to those using manual delineation. In summary, we designed a susceptibility multi-atlas tool for automated and reliable segmentation of QSM images and for quantification of magnetic susceptibilities. It is publicly available through our cloud-based platform (www.mricloud.org). Further improvement on the performance of this multi-atlas tool is expected by increasing the number of atlases in the future. •Susceptibility multi-atlas tools developed for improved segmentation of QSM.•Dual contrast QSM/T1 pipeline outperformed other pipelines.•Consistent QSM and volume quantification as compared to manual labeling.•Good reproducibility observed over acquisition protocols and platforms.•Consistent performance in QSM quantification with data from different sites.

MRI is a sensitive method for detecting subtle anatomic abnormalities in the neonatal brain. To optimize the usefulness for neonatal and pediatric care, systematic research, based on quantitative image analysis and functional correlation, is required. Normalization-based image analysis is one of the most effective methods for image quantification and statistical comparison. However, the application of this methodology to neonatal brain MRI scans is rare. Some of the difficulties are the rapid changes in T1 and T2 contrasts and the lack of contrast between brain structures, which prohibits accurate cross-subject image registration. Diffusion tensor imaging (DTI), which provides rich and quantitative anatomical contrast in neonate brains, is an ideal technology for normalization-based neonatal brain analysis. In this paper, we report the development of neonatal brain atlases with detailed anatomic information derived from DTI and co-registered anatomical MRI. Combined with a diffeomorphic transformation, we were able to normalize neonatal brain images to the atlas space and three-dimensionally parcellate images into 122 regions. The accuracy of the normalization was comparable to the reliability of human raters. This method was then applied to babies of 37–53 post-conceptional weeks to characterize developmental changes of the white matter, which indicated a posterior-to-anterior and a central-to-peripheral direction of maturation. We expect that future applications of this atlas will include investigations of the effect of prenatal events and the effects of preterm birth or low birth weights, as well as clinical applications, such as determining imaging biomarkers for various neurological disorders. ► We developed neonatal brain MRI atlases for normalization-based whole-brain analyses. ► Group-averaged atlases and a single-subject atlas were created. ► Each atlas includes T 1- and T 2-weighted images and DTI. ► The single-subject atlas was parcellated into 122 brain structures for the atlas-based analysis. ► The atlases are now available through our website ( http://lbam.med.jhmi.edu/).

Diffusion tensor imaging (DTI) is an exciting new MRI modality that can reveal detailed anatomy of the white matter. DTI also allows us to approximate the 3D trajectories of major white matter bundles. By combining the identified tract coordinates with various types of MR parameter maps, such as T 2 and diffusion properties, we can perform tract-specific analysis of these parameters. Unfortunately, 3D tract reconstruction is marred by noise, partial volume effects, and complicated axonal structures. Furthermore, changes in diffusion anisotropy under pathological conditions could alter the results of 3D tract reconstruction. In this study, we created a white matter parcellation atlas based on probabilistic maps of 11 major white matter tracts derived from the DTI data from 28 normal subjects. Using these probabilistic maps, automated tract-specific quantification of fractional anisotropy and mean diffusivity were performed. Excellent correlation was found between the automated and the individual tractography-based results. This tool allows efficient initial screening of the status of multiple white matter tracts.