New rapid, accurate T2 quantification detects pathology in normal-appearing brain regions of relapsing-remitting MS patients

Accelerated 3D multi-echo spin-echo sequence with a subspace constrained reconstruction for whole mouse brain T 2 mapping

PURPOSE

To accelerate whole-brain quantitativeT 2 $$ {\mathrm{T}}_2 $$ mapping in preclinical imaging setting.

METHODS

A three-dimensional (3D) multi-echo spin echo sequence was highly undersampled with a variable density Poisson distribution to reduce the acquisition time. Advanced iterative reconstruction based on linear subspace constraints was employed to recover high-quality raw images. Different subspaces, generated using exponential or extended-phase graph (EPG) simulations or from low-resolution calibration images, were compared. The subspace dimension was investigated in terms ofT 2 $$ {\mathrm{T}}_2 $$ precision. The method was validated on a phantom containing a wide range ofT 2 $$ {\mathrm{T}}_2 $$ and was then applied to monitor metastasis growth in the mouse brain at 4.7T. Image quality andT 2 $$ {\mathrm{T}}_2 $$ estimation were assessed for 3 acceleration factors (6/8/10).

RESULTS

The EPG-based dictionary gave robust estimations of a large range ofT 2 $$ {\mathrm{T}}_2 $$ . A subspace dimension of 6 was the best compromise betweenT 2 $$ {\mathrm{T}}_2 $$ precision and image quality. Combining the subspace constrained reconstruction with a highly undersampled dataset enabled the acquisition of whole-brainT 2 $$ {\mathrm{T}}_2 $$ maps, the detection and the monitoring of metastasis growth of less than 500μ m 3 $$ \mu {\mathrm{m}}^3 $$ .

CONCLUSION

Subspace-based reconstruction is suitable for 3DT 2 $$ {\mathrm{T}}_2 $$ mapping. This method can be used to reach an acceleration factor up to 8, corresponding to an acquisition time of 25 min for an isotropic 3D acquisition of 156μ $$ \mu $$ m on the mouse brain, used here for monitoring metastases growth.

A comprehensive protocol for quantitative magnetic resonance imaging of the brain at 3 Tesla

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.

Probing muscle recovery following downhill running using precise mapping of MRI T2 relaxation times

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.

Psychophysical Evaluation of Visual vs. Computer-Aided Detection of Brain Lesions on Magnetic Resonance Images

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.

Adaptive model-based Magnetic Resonance

PURPOSE

Conventional sequences are static in nature, fixing measurement parameters in advance in anticipation of a wide range of expected tissue parameter values. We set out to design and benchmark a new, personalized approach-termed adaptive MR-in which incoming subject data is used to update and fine-tune the pulse sequence parameters in real time.

METHODS

We implemented an adaptive, real-time multi-echo (MTE) experiment for estimating T₂ s. Our approach combined a Bayesian framework with model-based reconstruction. It maintained and continuously updated a prior distribution of the desired tissue parameters, including T₂ , which was used to guide the selection of sequence parameters in real time.

RESULTS

Computer simulations predicted accelerations between 1.7- and 3.3-fold for adaptive multi-echo sequences relative to static ones. These predictions were corroborated in phantom experiments. In healthy volunteers, our adaptive framework accelerated the measurement of T₂ for n-acetyl-aspartate by a factor of 2.5.

CONCLUSION

Adaptive pulse sequences that alter their excitations in real time could provide substantial reductions in acquisition times. Given the generality of our proposed framework, our results motivate further research into other adaptive model-based approaches to MRI and MRS.

Fast and accurate T2 mapping using Bloch simulations and low-rank plus sparse matrix decomposition

PURPOSE

MRI's T2 relaxation time is one of the key contrast mechanisms for clinical diagnosis and prognosis of pathologies. Mapping this relaxation time, however, involves extensive scan times, which are needed to collect quantitative data, thereby impeding its integration into clinical routine. This study employs a low-rank plus sparse (L + S) signal decomposition approach in order to reconstruct accurate T2-maps from highly undersampled multi-echo spin-echo (MESE) MRI data.

METHODS

Two new algorithms are presented: the first uses standard L + S approach, where both L and S are iteratively updated. The second technique, dubbed SPArse and fixed RanK (SPARK), uses a fixed-rank L, under the assumption that most MESE information is found in the L component and that this rank can be pre-calculated. The utility of these new techniques is demonstrated on in vivo brain and calf data at x2 to x6 acceleration factors.

RESULTS

Accelerated T2 maps showed improved accuracy compared to fully sampled ground truth maps, when using L + S and SPARK techniques vis-à-vis standard GRAPPA acceleration.

CONCLUSION

SPARK provides accurate T2 maps with increased robustness to the selection of reconstruction parameters making it suitable to a wide range of applications and facilitating the use of quantitative T2 information in clinical settings.

Estimation of subvoxel fat infiltration in neurodegenerative muscle disorders using quantitative multi-T2 analysis

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.

Inline dual-echo T2 quantification in brain using a fast mapping reconstruction technique

T2 mapping from 2D proton density and T2-weighted images (PD-T2) using Bloch equation simulations can be time consuming and introduces a latency between image acquisition and T2 map production. A fast T2 mapping reconstruction method is investigated and compared with a previous modeling approach to reduce computation time and allow inline T2 maps on the MRI console. Brain PD-T2 images from five multiple sclerosis patients were used to compare T2 map reconstruction times between the new subtraction method and the Euclidean norm minimization technique. Bloch equation simulations were used to create the lookup table for decay curve matching in both cases. Agreement of the two techniques used Bland-Altman analysis for investigating individual subsets of data and all image points in the five volumes (meta-analysis). The subtraction method resulted in an average reduction of computation time for single slices from 134 s (minimization method) to 0.44 s. Comparing T2 values between the subtraction and minimization methods resulted in a confidence interval ranging from -0.06 to 0.06 ms (95% of values were within ± 0.06 ms between the techniques). Using identical reconstruction code based on the subtraction method, inline T2 maps were produced from PD-T2 images directly on the scanner console. The excellent agreement between the two methods permits the subtraction technique to be interchanged with the previous method, reducing computation time and allowing inline T2 map reconstruction based on Bloch simulations directly on the scanner.

Mapping of magnetic resonance imaging's transverse relaxation time at low signal-to-noise ratio using Bloch simulations and principal component analysis image denoising

High-resolution mapping of magnetic resonance imaging (MRI)'s transverse relaxation time (T₂ ) can benefit many clinical applications by offering improved anatomic details, enhancing the ability to probe tissues' microarchitecture, and facilitating the identification of early pathology. Increasing spatial resolutions, however, decreases data's signal-to-noise ratio (SNR), particularly at clinical scan times. This impairs imaging quality, and the accuracy of subsequent radiological interpretation. Recently, principal component analysis (PCA) was employed for denoising diffusion-weighted MR images and was shown to be effective for improving parameter estimation in multiexponential relaxometry. This study combines the Marchenko-Pastur PCA (MP-PCA) signal model with the echo modulation curve (EMC) algorithm for denoising multiecho spin-echo (MESE) MRI data and improving the precision of EMC-generated single T₂ relaxation maps. The denoising technique was validated on simulations, phantom scans, and in vivo brain and knee data. MESE scans were performed on a 3-T Siemens scanner. The acquired images were denoised using the MP-PCA algorithm and were then provided as input for the EMC T₂ -fitting algorithm. Quantitative analysis of the denoising quality included comparing the standard deviation and coefficient of variation of T₂ values, along with gold standard SNR estimation of the phantom scans. The presented denoising technique shows an increase in T₂ maps' precision and SNR, while successfully preserving the morphological features of the tissue. Employing MP-PCA denoising as a preprocessing step decreases the noise-related variability of T₂ maps produced by the EMC algorithm and thus increases their precision. The proposed method can be useful for a wide range of clinical applications by facilitating earlier detection of pathologies and improving the accuracy of patients' follow-up.

Phase-based fast 3D high-resolution quantitative T2 MRI in 7 T human brain imaging

Magnetic resonance imaging (MRI) is a powerful and versatile technique that offers a range of physiological, diagnostic, structural, and functional measurements. One of the most widely used basic contrasts in MRI diagnostics is transverse relaxation time (T₂)-weighted imaging, but it provides only qualitative information. Realizing quantitative high-resolution T₂ mapping is imperative for the development of personalized medicine, as it can enable the characterization of diseases progression. While ultra-high-field (≥ 7 T) MRI offers the means to gain new insights by increasing the spatial resolution, implementing fast quantitative T₂ mapping cannot be achieved without overcoming the increased power deposition and radio frequency (RF) field inhomogeneity at ultra-high-fields. A recent study has demonstrated a new phase-based T₂ mapping approach based on fast steady-state acquisitions. We extend this new approach to ultra-high field MRI, achieving quantitative high-resolution 3D T₂ mapping at 7 T while addressing RF field inhomogeneity and utilizing low flip angle pulses; overcoming two main ultra-high field challenges. The method is based on controlling the coherent transverse magnetization in a steady-state gradient echo acquisition; achieved by utilizing low flip angles, a specific phase increment for the RF pulses, and short repetition times. This approach simultaneously extracts both T₂ and RF field maps from the phase of the signal. Prior to in vivo experiments, the method was assessed using a 3D head-shaped phantom that was designed to model the RF field distribution in the brain. Our approach delivers fast 3D whole brain images with submillimeter resolution without requiring special hardware, such as multi-channel transmit coil, thus promoting high usability of the ultra-high field MRI in clinical practice.

Quantification of Intra-Muscular Adipose Infiltration in Calf/Thigh MRI Using Fully and Weakly Supervised Semantic Segmentation

Purpose: Infiltration of fat into lower limb muscles is one of the key markers for the severity of muscle pathologies. The level of fat infiltration varies in its severity across and within patients, and it is traditionally estimated using visual radiologic inspection. Precise quantification of the severity and spatial distribution of this pathological process requires accurate segmentation of lower limb anatomy into muscle and fat. Methods: Quantitative magnetic resonance imaging (qMRI) of the calf and thigh muscles is one of the most effective techniques for estimating pathological accumulation of intra-muscular adipose tissue (IMAT) in muscular dystrophies. In this work, we present a new deep learning (DL) network tool for automated and robust segmentation of lower limb anatomy that is based on the quantification of MRI’s transverse (T2) relaxation time. The network was used to segment calf and thigh anatomies into viable muscle areas and IMAT using a weakly supervised learning process. A new disease biomarker was calculated, reflecting the level of abnormal fat infiltration and disease state. A biomarker was then applied on two patient populations suffering from dysferlinopathy and Charcot–Marie–Tooth (CMT) diseases. Results: Comparison of manual vs. automated segmentation of muscle anatomy, viable muscle areas, and intermuscular adipose tissue (IMAT) produced high Dice similarity coefficients (DSCs) of 96.4%, 91.7%, and 93.3%, respectively. Linear regression between the biomarker value calculated based on the ground truth segmentation and based on automatic segmentation produced high correlation coefficients of 97.7% and 95.9% for the dysferlinopathy and CMT patients, respectively. Conclusions: Using a combination of qMRI and DL-based segmentation, we present a new quantitative biomarker of disease severity. This biomarker is automatically calculated and, most importantly, provides a spatially global indication for the state of the disease across the entire thigh or calf.

Correcting for imaging gradients-related bias of T2 relaxation times at high-resolution MRI

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.

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Accelerated reconstruction of dictionary-based T2 relaxation maps based on dictionary compression and gradient descent search algorithms

Background Quantitative T₂-relaxation-based contrast maps have shown to be highly beneficial for clinical diagnosis and follow-up. The generation of quantitative maps, however, is impaired by long acquisition times, and time-consuming post-processing schemes. The EMC platform is a dictionary-based technique, which involves simulating theoretical signal curves for different physical and experimental values, followed by matching the experimentally acquired signals to the set simulated ones. Purpose Although the EMC technique has shown to produce accurate T₂ maps, it involves computationally intensive post-processing procedures. In this work we present an approach for accelerating the reconstruction of T₂ relaxation maps. Methods This work presents two alternative post-processing approaches for accelerating the reconstruction of EMC-based T₂ relaxation maps. These are (a) Dictionary compression using principal component analysis (PCA) and (b) gradient-descent search algorithm. Additional acceleration was achieved by finding the optimal MATLAB C++ compiler. The utility of the two suggested approaches was examined by calculating the relative error, produced by each technique. Results Gradient descent method was in perfect agreement with the ground truth exhaustive search matching process. PCA based acceleration produced root mean square error (RMSE) of up to 4% compared to exhaustive matching process. Overall acceleration of x16 was achieved using gradient descent in addition to x7 acceleration by choosing the optimal MATLAB C++ compiler. Conclusions Postprocessing of EMC-based T₂ relaxation maps can be accelerated without impairing the accuracy of the ensuing T₂ values.

Post-Run T2 Mapping Changes in Knees of Adolescent Basketball Players

OBJECTIVE

While articular cartilage defects are common incidental findings among adult athletes, the effect of running on the cartilage of adolescent athletes have rarely been assessed. This study aims to assess the variations in the articular cartilage of the knees in healthy adolescent basketball players using quantitative T₂ MRI (magnetic resonance imaging).

DESIGN

Fifteen adolescent basketball players were recruited (13.8 ± 0.5 years old). Girls were excluded to avoid potential gender-related confounding effects. Players underwent a pre-run MRI scan of both knees. All participants performed a 30-minute run on a treadmill. Within 15 minutes after completion of their run, players underwent a second, post-run MRI scan. Quantitative T₂ maps were generated using the echo modulation curve (EMC) algorithm. Pre-run scans and post-run scans were compared using paired t test.

RESULTS

Participants finished their 30-minute run with a mean running distance of 5.77 ± 0.42 km. Pre-run scans analysis found statistically significant (P < 0.05) changes in 3 regions of the knee lateral compartment representing the cartilaginous tissue. No differences were found in the knee medial compartment. Post-run analysis showed lower T₂ values in the medial compartment compared to the pre-run scans in several weight-bearing regions: femoral condyle central (pre/post mean values of 33.9/32.2 ms, P = 0.020); femoral condyle posterior (38.1/36.8 ms, P = 0.038); and tibial plateau posterior (34.1/31.0 ms, P < 0.001). The lateral regions did not show any significant changes.

CONCLUSIONS

Running leads to microstructural changes in the articular cartilage in several weight-bearing areas of the medial compartment, both in the femoral and the tibial cartilage.

Quantitative platform for accurate and reproducible assessment of transverse (T2 ) relaxation time

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.

Fast T2 mapping using multi-echo spin-echo MRI: A linear order approach

PURPOSE

Multi-echo spin-echo sequence is commonly used for T2 mapping. The estimated values using conventional exponential fit, however, are hampered by stimulated and indirect echoes leading to overestimation of T2 . Here, we present fast analysis of multi-echo spin-echo (FAMESE) as a novel approach to decrease the complexity of the search space, which leads to accelerated measurement of T2 .

METHODS

We developed FAMESE based on mathematical analysis of the Bloch equations in which the search space dimension decreased to only one. Then, we tested it in both phantom and human brain. Bland-Altman plot was used to assess the agreement between the estimated T2 values from FAMESE and the ones estimated from single-echo spin-echo sequence. The reliability of FAMESE was assessed by intraclass correlation coefficients. In addition, we investigated the noise stability of the method in synthetic and experimental data.

RESULTS

In both phantom and healthy participants, FAMESE provided accelerated and SNR-resistant T2 maps. The FAMESE had a very good agreement with the single-echo spin echo for the whole range of T2 values. The intraclass correlation coefficient values for FAMESE were excellent (ie, 0.9998 and 0.9860 < intraclass correlation coefficient < 0.9942 for the phantom and humans, respectively).

CONCLUSION

Our developed method FAMESE could be considered as a candidate for rapid T2 mapping with a clinically feasible scan time.

Potential clinical impact of multiparametric quantitative MR spectroscopy in neurological disorders: A review and analysis

PURPOSE

Unlike conventional MR spectroscopy (MRS), which only measures metabolite concentrations, multiparametric MRS also quantifies their longitudinal (T1 ) and transverse (T2 ) relaxation times, as well as the radiofrequency transmitter inhomogeneity (B1+ ). To test whether knowledge of these additional parameters can improve the clinical utility of brain MRS, we compare the conventional and multiparametric approaches in terms of expected classification accuracy in differentiating controls from patients with neurological disorders.

THEORY AND METHODS

A literature review was conducted to compile metabolic concentrations and relaxation times in a wide range of neuropathologies and regions of interest. Simulations were performed to construct receiver operating characteristic curves and compute the associated areas (area under the curve) to examine the sensitivity and specificity of MRS for detecting each pathology in each region. Classification accuracy was assessed using metabolite concentrations corrected using population-averages for T1 , T2 , and B1+ (conventional MRS); using metabolite concentrations corrected using per-subject values (multiparametric MRS); and using an optimal linear multiparametric estimator comprised of the metabolites' concentrations and relaxation constants (multiparametric MRS). Additional simulations were conducted to find the minimal intra-subject precision needed for each parameter.

RESULTS

Compared with conventional MRS, multiparametric approaches yielded area under the curve improvements for almost all neuropathologies and regions of interest. The median area under the curve increased by 0.14 over the entire dataset, and by 0.24 over the 10 instances with the largest individual increases.

CONCLUSIONS

Multiparametric MRS can substantially improve the clinical utility of MRS in diagnosing and assessing brain pathology, motivating the design and use of novel multiparametric sequences.

White Matter Abnormalities in Multiple Sclerosis Evaluated by Quantitative Synthetic MRI, Diffusion Tensor Imaging, and Neurite Orientation Dispersion and Density Imaging

BACKGROUND AND PURPOSE

A number of MR-derived quantitative metrics have been suggested to assess the pathophysiology of MS, but the reports about combined analyses of these metrics are scarce. Our aim was to assess the spatial distribution of parameters for white matter myelin and axon integrity in patients with relapsing-remitting MS by multiparametric MR imaging.

MATERIALS AND METHODS

Twenty-four patients with relapsing-remitting MS and 24 age- and sex-matched controls were prospectively scanned by quantitative synthetic and 2-shell diffusion MR imaging. Synthetic MR imaging data were used to retrieve relaxometry parameters (R1 and R2 relaxation rates and proton density) and myelin volume fraction. Diffusion tensor metrics (fractional anisotropy and mean, axial, and radial diffusivity) and neurite orientation and dispersion index metrics (intracellular volume fraction, isotropic volume fraction, and orientation dispersion index) were retrieved from diffusion MR imaging data. These data were analyzed using Tract-Based Spatial Statistics.

RESULTS

Patients with MS showed significantly lower fractional anisotropy and myelin volume fraction and higher isotropic volume fraction in widespread white matter areas. Areas with different isotropic volume fractions were included within areas with lower fractional anisotropy. Myelin volume fraction showed no significant difference in some areas with significantly decreased fractional anisotropy in MS, including in the genu of the corpus callosum and bilateral anterior corona radiata, whereas myelin volume fraction was significantly decreased in some areas where fractional anisotropy showed no significant difference, including the bilateral posterior limb of the internal capsule, external capsule, sagittal striatum, fornix, and uncinate fasciculus.

CONCLUSIONS

We found differences in spatial distribution of abnormality in fractional anisotropy, isotropic volume fraction, and myelin volume fraction distribution in MS, which might be useful for characterizing white matter in patients with MS.

Analysis of magnetization transfer (MT) influence on quantitative mapping of T2 relaxation time

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 .

Autoantibodies as diagnostic biomarkers for the detection and subtyping of multiple sclerosis

The goal of this preliminary proof-of-concept study was to use human protein microarrays to identify blood-based autoantibody biomarkers capable of diagnosing multiple sclerosis (MS). Using sera from 112 subjects, including 51 MS subjects, autoantibody biomarkers effectively differentiated MS subjects from age- and gender-matched normal and breast cancer controls with 95.0% and 100% overall accuracy, but not from subjects with Parkinson's disease. Autoantibody biomarkers were also useful in distinguishing subjects with the relapsing-remitting form of MS from those with the secondary progressive subtype. These results demonstrate that autoantibodies can be used as noninvasive blood-based biomarkers for the detection and subtyping of MS.