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Quantitative MRI (qMRI) has been shown to be clinically useful for numerous applications in the brain and body. The development of rapid, accurate, and reproducible qMRI techniques offers access to new multiparametric data, which can provide a comprehensive view of tissue pathology. This work introduces a multiparametric qMRI protocol along with full postprocessing pipelines, optimized for brain imaging at 3 Tesla and using state-of-the-art qMRI tools. The total scan time is under 50 minutes and includes eight pulse-sequences, which produce range of quantitative maps including T1, T2, and T2* relaxation times, magnetic susceptibility, water and macromolecular tissue fractions, mean diffusivity and fractional anisotropy, magnetization transfer ratio (MTR), and inhomogeneous MTR. Practical tips and limitations of using the protocol are also provided and discussed. Application of the protocol is presented on a cohort of 28 healthy volunteers and 12 brain regions-of-interest (ROIs). Quantitative values agreed with previously reported values. Statistical analysis revealed low variability of qMRI parameters across subjects, which, compared to intra-ROI variability, was x4.1 ± 0.9 times higher on average. Significant and positive linear relationship was found between right and left hemispheres' values for all parameters and ROIs with Pearson correlation coefficients of r>0.89 (P<0.001), and mean slope of 0.95 ± 0.04. Finally, scan-rescan stability demonstrated high reproducibility of the measured parameters across ROIs and volunteers, with close-to-zero mean difference and without correlation between the mean and difference values (across map types, mean P value was 0.48 ± 0.27). The entire quantitative data and postprocessing scripts described in the manuscript are publicly available under dedicated GitHub and Figshare repositories. The quantitative maps produced by the presented protocol can promote longitudinal and multi-center studies, and improve the biological interpretability of qMRI by integrating multiple metrics that can reveal information, which is not apparent when examined using only a single contrast mechanism. [ABSTRACT FROM AUTHOR]

Purpose: Postexercise recovery rate is a vital component of designing personalized training protocols and rehabilitation plans. Tracking exercise‐induced muscle damage and recovery requires sensitive tools that can probe the muscles' state and composition noninvasively. Methods: Twenty‐four physically active males completed a running protocol consisting of a 60‐min downhill run on a treadmill at −10% incline and 65% of maximal heart rate. Quantitative mapping of MRI T2 was performed using the echo‐modulation‐curve algorithm before exercise, and at two time points: 1 h and 48 h after exercise. Results: T2 values increased by 2%–4% following exercise in the primary mover muscles and exhibited further elevation of 1% after 48 h. For the antagonist muscles, T2 values increased only at the 48‐h time point (2%–3%). Statistically significant decrease in the SD of T2 values was found following exercise for all tested muscles after 1 h (16%–21%), indicating a short‐term decrease in the heterogeneity of the muscle tissue. Conclusion: MRI T2 relaxation time constitutes a useful quantitative marker for microstructural muscle damage, enabling region‐specific identification for short‐term and long‐term systemic processes, and sensitive assessment of muscle recovery following exercise‐induced muscle damage. The variability in T2 changes across different muscle groups can be attributed to their different role during downhill running, with immediate T2 elevation occurring in primary movers, followed by delayed elevation in both primary and antagonist muscle groups, presumably due to secondary damage caused by systemic processes. [ABSTRACT FROM AUTHOR]

MRI's T2 relaxation time is a valuable biomarker for neuromuscular disorders and muscle dystrophies. One of the hallmarks of these pathologies is the infiltration of adipose tissue and a loss of muscle volume. This leads to a mixture of two signal components, from fat and from water, to appear in each imaged voxel, each having a specific T2 relaxation time. In this proof‐of‐concept work, we present a technique that can separate the signals from water and from fat within each voxel, measure their separate T2 values, and calculate their relative fractions. The echo modulation curve (EMC) algorithm is a dictionary‐based technique that offers accurate and reproducible mapping of T2 relaxation times. We present an extension of the EMC algorithm for estimating subvoxel fat and water fractions, alongside the T2 and proton‐density values of each component. To facilitate data processing, calf and thigh anatomy were automatically segmented using a fully convolutional neural network and FSLeyes software. The preprocessing included creating two signal dictionaries, for water and for fat, using Bloch simulations of the prospective protocol. Postprocessing included voxelwise fitting for two components, by matching the experimental decay curve to a linear combination of the two simulated dictionaries. Subvoxel fat and water fractions and relaxation times were generated and used to calculate a new quantitative biomarker, termed viable muscle index, and reflecting disease severity. This biomarker indicates the fraction of remaining muscle out of the entire muscle region. The results were compared with those using the conventional Dixon technique, showing high agreement (R = 0.98, p < 0.001). It was concluded that the new extension of the EMC algorithm can be used to quantify abnormal fat infiltration as well as identify early inflammatory processes corresponding to elevation in the T2 value of the water (muscle) component. This new ability may improve the diagnostic accuracy of neuromuscular diseases, help stratification of patients according to disease severity, and offer an efficient tool for tracking disease progression. [ABSTRACT FROM AUTHOR]

Background: Magnetic resonance imaging (MRI) diagnosis is usually performed by analyzing contrast‐weighted images, where pathology is detected once it reached a certain visual threshold. Computer‐aided diagnosis (CAD) has been proposed as a way for achieving higher sensitivity to early pathology. Purpose: To compare conventional (i.e., visual) MRI assessment of artificially generated multiple sclerosis (MS) lesions in the brain's white matter to CAD based on a deep neural network. Study Type: Prospective. Population: A total of 25 neuroradiologists (15 males, age 39 ± 9, 9 ± 9.8 years of experience) independently assessed all synthetic lesions. Field Strength/Sequence: A 3.0 T, T2‐weighted multi‐echo spin‐echo (MESE) sequence. Assessment: MS lesions of varying severity levels were artificially generated in healthy volunteer MRI scans by manipulating T2 values. Radiologists and a neural network were tasked with detecting these lesions in a series of 48 MR images. Sixteen images presented healthy anatomy and the rest contained a single lesion at eight increasing severity levels (6%, 9%, 12%, 15%, 18%, 21%, 25%, and 30% elevation in T2). True positive (TP) rates, false positive (FP) rates, and odds ratios (ORs) were compared between radiological diagnosis and CAD across the range lesion severity levels. Statistical Tests: Diagnostic performance of the two approaches was compared using z‐tests on TP rates, FP rates, and the logarithm of ORs across severity levels. A P‐value <0.05 was considered statistically significant. Results: ORs of identifying pathology were significantly higher for CAD vis‐à‐vis visual inspection for all lesions' severity levels. For a 6% change in T2 value (lowest severity), radiologists' TP and FP rates were not significantly different (P = 0.12), while the corresponding CAD results remained statistically significant. Data Conclusion: CAD is capable of detecting the presence or absence of more subtle lesions with greater precision than the representative group of 25 radiologists chosen in this study. Level of Evidence: 1 Technical Efficacy: Stage 3 [ABSTRACT FROM AUTHOR]

The majority of children and adults with reading disabilities exhibit pronounced difficulties on naming-speed measures such as tests of rapid automatized naming (RAN). RAN tasks require speeded naming of serially presented stimuli and share key characteristics with reading, but different versions of the RAN task vary in their sensitivity: The RAN letters task successfully predicts reading ability, whereas the RAN objects task does not reliably predict reading after kindergarten. In this study we used functional magnetic resonance imaging to evaluate the neural substrates that may underlie performance on these tasks. In two scans during the same test session, adult, average readers covertly rapidly named objects or letters or passively viewed a fixation matrix of plus signs. For both rapid naming tasks compared with fixation, activation was found in neural areas associated with eye movement control and attention as well as in a network of structures previously implicated in reading tasks. This reading network included inferior frontal cortex, temporo-parietal areas, and the ventral visual stream. Whereas the inferior frontal areas of the network were similarly activated for both letters and objects, activation in the posterior areas varied by task. The letters task caused greater activation in the angular gyrus, superior parietal lobule, and medial extrastriate areas, whereas object naming only preferentially activated an area of the fusiform gyrus. These results confirm that RAN tasks recruit a network of neural structures also involved in more complex reading tasks and suggest that the RAN letters task specifically pinpoints key components of this network.

This study investigated changes in teachers' and students' perceptions of students' effort, strategy use, and academic difficulties when strategy instruction was infused into the classroom curriculum. The sample consisted of 201 students with learning disabilities, 210 average achievers, and 57 teachers from Grades 4-9 in two urban and suburban communities. After six months of classroom-based strategy instruction, students with learning disabilities reported more consistent use of strategies with their schoolwork and perceived themselves as struggling less in reading, writing, and spelling. Teachers perceived the students with learning disabilities as more strategic and as applying more effort to their schoolwork. Teachers also perceived their students as showing significant improvements in spelling, regardless of whether they had learning disabilities. These findings extended the results of previous investigations and indicated the small, positive impact of classroom-based strategy instruction. Further investigations are critical to evaluate the generalizability of these findings.

Purpose: High‐resolution animal imaging is an integral part of preclinical drug development and the investigation of diseases' pathophysiology. Quantitative mapping of T2 relaxation times (qT2) is a valuable tool for both preclinical and research applications, providing high sensitivity to subtle tissue pathologies. High‐resolution T2 mapping, however, suffers from severe underestimation of T2 values due to molecular diffusion. This affects both single‐echo and multi‐echo spin echo (SSE and MESE), on top of the well‐known contamination of MESE signals by stimulated echoes, and especially on high‐field and preclinical scanners in which high imaging gradients are used in comparison to clinical scanners. Methods: Diffusion bias due to imaging gradients was analyzed by quantifying the effective b‐value for each coherence pathway in SSE and MESE protocols, and incorporating this information in a joint T2‐diffusion reconstruction algorithm. Validation was done on phantoms and in vivo mouse brain using a 9.4T and a 7T MRI scanner. Results: Underestimation of T2 values due to strong imaging gradients can reach up to 70%, depending on scan parameters and on the sample's diffusion coefficient. The algorithm presented here produced T2 values that agreed with reference spectroscopic measurements, were reproducible across scan settings, and reduced the average bias of T2 values from −33.5 ± 20.5% to −0.1 ± 3.6%. Conclusions: A new joint T2‐diffusion reconstruction algorithm is able to negate imaging gradient–related underestimation of T2 values, leading to reliable mapping of T2 values at high resolutions. [ABSTRACT FROM AUTHOR]

Purpose: Multicomponent analysis of MRI T2 relaxation time (mcT2) is commonly used for estimating myelin content by separating the signal at each voxel into its underlying distribution of T2 values. This voxel‐based approach is challenging due to the large ambiguity in the multi‐T2 space and the low SNR of MRI signals. Herein, we present a data‐driven mcT2 analysis, which utilizes the statistical strength of identifying spatially global mcT2 motifs in white matter segments before deconvolving the local signal at each voxel. Methods: Deconvolution is done using a tailored optimization scheme, which incorporates the global mcT2 motifs without additional prior assumptions regarding the number of microscopic components. The end results of this process are voxel‐wise myelin water fraction maps. Results: Validations are shown for computer‐generated signals, uniquely designed subvoxel mcT2 phantoms, and in vivo human brain. Results demonstrated excellent fitting accuracy, both for the numerical and the physical mcT2 phantoms, exhibiting excellent agreement between calculated myelin water fraction and ground truth. Proof‐of‐concept in vivo validation is done by calculating myelin water fraction maps for white matter segments of the human brain. Interscan stability of myelin water fraction values was also estimated, showing good correlation between scans. Conclusion: We conclude that studying global tissue motifs prior to performing voxel‐wise mcT2 analysis stabilizes the optimization scheme and efficiently overcomes the ambiguity in the T2 space. This new approach can improve myelin water imaging and the investigation of microstructural compartmentation in general. [ABSTRACT FROM AUTHOR]

MRI's transverse relaxation time (T2) is sensitive to tissues' composition and pathological state. While variations in T2 values can be used as clinical biomarkers, it is challenging to quantify this parameter in vivo due to the complexity of the MRI signal model, differences in protocol implementations, and hardware imperfections. Herein, we provide a detailed analysis of the echo modulation curve (EMC) platform, offering accurate and reproducible mapping of T2 values, from 2D multi‐slice multi‐echo spin‐echo (MESE) protocols. Computer simulations of the full Bloch equations are used to generate an advanced signal model, which accounts for stimulated echoes and transmit field (B1+) inhomogeneities. In addition to quantifying T2 values, the EMC platform also provides proton density (PD) maps, and fat‐water fraction maps. The algorithm's accuracy, reproducibility, and insensitivity to T1 values are validated on a phantom constructed by the National Institute of Standards and Technology and on in vivo human brains. EMC‐derived T2 maps show excellent agreement with ground truth values for both in vitro and in vivo models. Quantitative values are accurate and stable across scan settings and for the physiological range of T2 values, while showing robustness to main field (B0) inhomogeneities, to variations in T1 relaxation time, and to magnetization transfer. Extension of the algorithm to two‐component fitting yields accurate fat and water T2 maps along with their relative fractions, similar to a reference three‐point Dixon technique. Overall, the EMC platform allows to generate accurate and stable T2 maps, with a full brain coverage using a standard MESE protocol and at feasible scan times. The utility of EMC‐based T2 maps was demonstrated on several clinical applications, showing robustness to variations in other magnetic properties. The algorithm is available online as a full stand‐alone package, including an intuitive graphical user interface. [ABSTRACT FROM AUTHOR]

Purpose: Multi‐echo spin‐echo (MESE) protocol is the most effective tool for mapping T2 relaxation in vivo. Still, MESE extensive use of radiofrequency pulses causes magnetization transfer (MT)‐related bias of the water signal, instigated by the presence of macromolecules (MMP). Here, we analyze the effects of MT on MESE signal, alongside their impact on quantitative T2 measurements. Methods: Study used 3 models: in vitro urea phantom, ex vivo horse brain, and in vivo human brain. MT ratio (MTR) was measured between single‐SE and MESE protocols under different scan settings including varying echo train lengths, number of slices, and inter‐slice gap. MTR and T2 values were extracted for each model and protocol. Results: MT interactions biased MESE signals, and in certain settings, the corresponding T2 values. T2 underestimation of up to 4.3% was found versus single‐SE values in vitro and up to 13.8% ex vivo, correlating with the MMP content. T2 bias originated from intra‐slice saturation of the MMP, rather than from indirect saturation in multi‐slice acquisitions. MT‐related signal attenuation was caused by slice crosstalk and/or partial T1 recovery, whereas smaller contribution was caused by MMP interactions. Inter‐slice gap had a similar effect on in vivo MTR (21.2%), in comparison to increasing the number of slices (18.9%). Conclusions: MT influences MESE protocols either by uniformly attenuating the entire echo train or by cumulatively attenuating the signal along the train. Although both processes depend on scan settings and MMP content, only the latter will cause underestimation of T2. [ABSTRACT FROM AUTHOR]