qMT Parameters Research Articles

Testing quantitative magnetization transfer models with membrane lipids.

Quantitative magnetization transfer (qMT) models aim to quantify the contributions of lipids and macromolecules to the MRI signal. Hence, a model system that relates qMT parameters and their molecular sources may improve the interpretation of the qMT parameters. Here we used membrane lipid phantoms as a meaningful tool to study qMT models. By controlling the fraction and type of membrane lipids, we could test the accuracy, reliability, and interpretability of different qMT models. We formulated liposomes with various lipid types and water-to-lipids fractions and measured their signals with spoiled gradient-echo MT. We fitted three known qMT models and estimated six parameters for every model. We tested the accuracy and reproducibility of the models and compared the dependency among the qMT parameters. We compared the samples' qMT parameters with their water-to-lipid fractions and with a simple MTnorm (= MTon/MToff) calculation. We found that the three qMT models fit the membrane lipids signals well. We also found that the estimated qMT parameters are highly interdependent. Interestingly, the estimated qMT parameters are a function of the membrane lipid type and also highly related to the water-to-lipid fraction. Finally, we find that most of the lipid sample's information can be captured using the common and easy to estimate MTnorm analysis. qMT parameters are sensitive to both the water-to-lipid fraction and to the lipid type. Estimating the water-to-lipid fraction can improve the characterization of membrane lipids' contributions to qMT parameters. Similar characterizations can be obtained using the MTnorm analysis.

Sensitivity of unconstrained quantitative magnetization transfer MRI to Amyloid burden in preclinical Alzheimer’s disease

Abstract Magnetization transfer MRI is sensitive to semi-solid macromolecules, including amyloid beta, and has previously been used to discriminate Alzheimer’s disease (AD) patients from controls. Here, we fit an unconstrained 2-pool quantitative MT (qMT) model, i.e., without constraints on the longitudinal relaxation rate R1S of semi-solids, and investigate the sensitivity of the estimated parameters to amyloid accumulation in preclinical participants. We scanned 15 cognitively normal volunteers, of which 9 were amyloid positive by [18F]Florbetaben PET. A 12 min hybrid-state qMT scan with an effective resolution of 1.24 mm isotropic and whole-brain coverage was acquired to estimate the unconstrained 2-pool qMT parameters. Group comparisons and correlations with Florbetaben PET standardized uptake value ratios were analyzed at the lobar level. We find that the exchange rate and semi-solid pool’s R1S were sensitive to the amyloid concentration, while morphometric measures of cortical thickness derived from structural MRI were not. Changes in the exchange rate are consistent with previous reports in clinical AD, while changes in R1S have not been reported previously as its value is typically constrained in the literature. Our results demonstrate that qMT MRI may be a promising surrogate marker of amyloid beta without the need for contrast agents or radiotracers.

Sensitivity of unconstrained quantitative magnetization transfer MRI to Amyloid burden in preclinical Alzheimer's disease.

Magnetization transfer MRI is sensitive to semi-solid macromolecules, including amyloid beta, and has previously been used to discriminate Alzheimer's disease (AD) patients from controls. Here, we fit an unconstrained 2-pool quantitative MT (qMT) model, i.e., without constraints on the longitudinal relaxation rate of semi-solids, and investigate the sensitivity of the estimated parameters to amyloid accumulation in preclinical subjects. We scanned 15 cognitively normal volunteers, of which 9 were amyloid positive by [18F]Florbetaben PET. A 12 min hybrid-state qMT scan with an effective resolution of 1.24 mm isotropic and whole-brain coverage was acquired to estimate the unconstrained 2-pool qMT parameters. Group comparisons and correlations with Florbetaben PET standardized uptake value ratios were analyzed at the lobar level. We find that the exchange rate and semi-solid pool's were sensitive to the amyloid concentration, while morphometric measures of cortical thickness derived from structural MRI were not. Changes in the exchange rate are consistent with previous reports in clinical AD, while changes in have not been reported previously as its value is typically constrained in the literature. Our results demonstrate that qMT MRI may be a promising surrogate marker of amyloid beta without the need for contrast agents or radiotracers.

Rapid quantitative magnetization transfer imaging: Utilizing the hybrid state and the generalized Bloch model.

To explore efficient encoding schemes for quantitative magnetization transfer (qMT) imaging with few constraints on model parameters. We combine two recently proposed models in a Bloch-McConnell equation: the dynamics of the free spin pool are confined to the hybrid state, and the dynamics of the semi-solid spin pool are described by the generalized Bloch model. We numerically optimize the flip angles and durations of a train of radio frequency pulses to enhance the encoding of three qMT parameters while accounting for all eight parameters of the two-pool model. We sparsely sample each time frame along this spin dynamics with a three-dimensional radial koosh-ball trajectory, reconstruct the data with subspace modeling, and fit the qMT model with a neural network for computational efficiency. We extracted qMT parameter maps of the whole brain with an effective resolution of 1.24 mm from a 12.6-min scan. In lesions of multiple sclerosis subjects, we observe a decreased size of the semi-solid spin pool and longer relaxation times, consistent with previous reports. The encoding power of the hybrid state, combined with regularized image reconstruction, and the accuracy of the generalized Bloch model provide an excellent basis for efficient quantitative magnetization transfer imaging with few constraints on model parameters.

SIMPLEX: Multiple phase-cycled bSSFP quantitative magnetization transfer imaging with physic-guided simulation learning of neural network

Most quantitative magnetization transfer (qMT) imaging methods require acquiring additional quantitative maps (such as T1) for data fitting. A method based on multiple phase-cycled bSSFP was recently proposed to enable high-resolution 3D qMT imaging based on least square fitting without any extra acquisition, and thus has high potential for simplifying the qMT procedure. However, the quantification of qMT parameters with this method was suboptimal, limiting its potential for clinical application despite its simpler protocol and higher spatial resolution. To improve the fitting of qMT data obtained with multiple phase-cycled bSSFP, we propose SIMulation-based Physics-guided Learning of neural network for qMT parameters EXtraction, or SIMPLEX. In contrast to previous deep learning supervised approaches for quantitative MR that require the acquisition of input data and corresponding ground truth for training, we leveraged the MR signal model to generate training samples without expensive data curation. The network was trained exclusively with simulation data by predicting the simulation parameters. The same network was applied directly to in-vivo data without additional training. The approach was verified with both simulation and in-vivo data. SIMPLEX showed a decrease in fitting mean squared error for all simulation data compared to the existing least-square fitting method. The in-vivo experiment revealed that the network performed well with the real in vivo data unseen during training. For all experiments, we observed that SIMPLEX consistently improved the quantification quality of the qMT parameters whilst being more robust to noise compared to the prior technique. The proposed SIMPLEX will expedite the routine clinical application of qMT by providing qMT parameters (exchange rate, pool fraction) as well as T1, T2, and ΔB0 maps simultaneously with high spatial resolution, better reliability, and reduced processing time.

Longitudinal analysis of new multiple sclerosis lesions with magnetization transfer and diffusion tensor imaging

ObjectiveThe potential of magnetization transfer imaging (MTI) and diffusion tensor imaging (DTI) for the detection and evolution of new multiple sclerosis (MS) lesions was analyzed.MethodsNineteen patients with MS obtained conventional MRI, MTI, and DTI examinations bimonthly for 12 months and again after 24 months at 1.5 T MRI. MTI was acquired with balanced steady-state free precession (bSSFP) in 10 min (1.3 mm3 isotropic resolution) yielding both magnetization transfer ratio (MTR) and quantitative magnetization transfer (qMT) parameters (pool size ratio (F), exchange rate (kf), and relaxation times (T1/T2)). DTI provided fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD).ResultsAt the time of their appearance on MRI, the 21 newly detected MS lesions showed significantly reduced MTR/F/kf and prolonged T1/T2 parameters, as well as significantly reduced FA and increased AD/MD/RD. Significant differences were already observed for MTR 4 months and for qMT parameters 2 months prior to lesions’ detection on MRI. DTI did not show any significant pre-lesional differences. Slightly reversed trends were observed for most lesions up to 8 months after their detection for qMT and less pronounced for MTR and three diffusion parameters, while appearing unchanged on MRI.ConclusionsMTI provides more information than DTI in MS lesions and detects tissue changes 2 to 4 months prior to their appearance on MRI. After lesions’ detection, qMT parameter changes promise to be more sensitive than MTR for the lesions’ evolutional assessment. Overall, bSSFP-based MTI adumbrates to be more sensitive than MRI and DTI for the early detection and follow-up assessment of MS lesions.Clinical relevance statementWhen additionally acquired in routine MRI, fast bSSFP-based MTI can complement the MRI/DTI longitudinal lesion assessment by detecting MS lesions 2–4 months earlier than with MRI, which could implicate earlier clinical decisions and better follow-up/treatment assessment in MS patients.Key Points• Magnetization transfer imaging provides more information than DTI in multiple sclerosis lesions and can detect tissue changes 2 to 4 months prior to their appearance on MRI.• After lesions’ detection, quantitative magnetization transfer changes are more pronounced than magnetization transfer ratio changes and therefore promise to be more sensitive for the lesions’ evolutional assessment.• Balanced steady-state free precession–based magnetization transfer imaging is more sensitive than MRI and DTI for the early detection and follow-up assessment of multiple sclerosis lesions.

Quantitative magnetization transfer MRI unbiased by on-resonance saturation and dipolar order contributions.

To demonstrate the bias in quantitative MT (qMT) measures introduced by the presence of dipolar order and on-resonance saturation (ONRS) effects using magnetization transfer (MT) spoiled gradient-recalled (SPGR) acquisitions, and propose changes to the acquisition and analysis strategies to remove these biases. The proposed framework consists of SPGR sequences prepared with simultaneous dual-offset frequency-saturation pulses to cancel out dipolar order and associated relaxation (T1D ) effects in Z-spectrum acquisitions, and a matched quantitative MT (qMT) mathematical model that includes ONRS effects of readout pulses. Variable flip angle and MT data were fitted jointly to simultaneously estimate qMT parameters (macromolecular proton fraction [MPF], T2,f , T2,b , R, and free pool T1 ). This framework is compared with standard qMT and investigated in terms of reproducibility, and then further developed to follow a joint single-point qMT methodology for combined estimation of MPF and T1 . Bland-Altman analyses demonstrated a systematic underestimation of MPF (-2.5% and -1.3%, on average, in white and gray matter, respectively) and overestimation of T1 (47.1 ms and 38.6 ms, on average, in white and gray matter, respectively) if both ONRS and dipolar order effects are ignored. Reproducibility of the proposed framework is excellent (ΔMPF = -0.03% and ΔT1 = -19.0 ms). The single-point methodology yielded consistent MPF and T1 values with respective maximum relative average bias of -0.15% and -3.5 ms found in white matter. The influence of acquisition strategy and matched mathematical model with regard to ONRS and dipolar order effects in qMT-SPGR frameworks has been investigated. The proposed framework holds promise for improved accuracy with reproducibility.

High-resolution magnetization-transfer imaging of post-mortem marmoset brain: Comparisons with relaxometry and histology

Cell membranes and macromolecules or paramagnetic compounds interact with water proton spins, which modulates magnetic resonance imaging (MRI) contrast providing information on tissue composition. For a further investigation, quantitative magnetization transfer (qMT) parameters (at 3T), including the ratio of the macromolecular and water proton pools, F, and the exchange-rate constant as well as the (observed) longitudinal and the effective transverse relaxation rates (at 3T and 7T), R1obs and R2*, respectively, were measured at high spatial resolution (200 µm) in a slice of fixed marmoset brain and compared to histology results obtained with Gallyas’ myelin stain and Perls’ iron stain. R1obs and R2* were linearly correlated with the iron content for the entire slice, whereas distinct differences were obtained between gray and white matter for correlations of relaxometry and qMT parameters with myelin content. The combined results suggest that the macromolecular pool interacting with water consists of myelin and (less efficient) non-myelin contributions. Despite strong correlation of F and R1obs, none of these parameters was uniquely specific to myelination. Due to additional sensitivity to iron stores, R1obs and R2* were more sensitive for depicting microstructural differences between cortical layers than F.

Unbiased signal equation for quantitative magnetization transfer mapping in balanced steady-state free precession MRI.

PurposeQuantitative magnetization transfer (qMT) imaging can be used to quantify the proportion of protons in a voxel attached to macromolecules. Here, we show that the original qMT balanced steady‐state free precession (bSSFP) model is biased due to over‐simplistic assumptions made in its derivation.Theory and MethodsWe present an improved model for qMT bSSFP, which incorporates finite radiofrequency (RF) pulse effects as well as simultaneous exchange and relaxation. Furthermore, a correction relating to finite RF pulse effects for sinc‐shaped excitations is derived. The new model is compared to the original one in numerical simulations of the Bloch‐McConnell equations and in previously acquired in vivo data.ResultsOur numerical simulations show that the original signal equation is significantly biased in typical brain tissue structures (by 7%‐20%), whereas the new signal equation outperforms the original one with minimal bias (<1%). It is further shown that the bias of the original model strongly affects the acquired qMT parameters in human brain structures, with differences in the clinically relevant parameter of pool‐size‐ratio of up to 31%. Particularly high biases of the original signal equation are expected in an MS lesion within diseased brain tissue (due to a low T2/T1‐ratio), demanding a more accurate model for clinical applications.ConclusionThe improved model for qMT bSSFP is recommended for accurate qMT parameter mapping in healthy and diseased brain tissue structures.

QMTNet: Accelerated quantitative magnetization transfer imaging with artificial neural networks.

To develop a set of artificial neural networks, collectively termed qMTNet, to accelerate data acquisition and fitting for quantitative magnetization transfer (qMT) imaging. Conventional and interslice qMT data were acquired with two flip angles at six offset frequencies from seven subjects for developing the networks and from four young and four older subjects for testing the generalizability. Two subnetworks, qMTNet-acq and qMTNet-fit, were developed and trained to accelerate data acquisition and fitting, respectively. qMTNet-2 is the sequential application of qMTNet-acq and qMTNet-fit to produce qMT parameters (exchange rate, pool fraction) from undersampled qMT data (two offset frequencies rather than six). qMTNet-1 is one single integrated network having the same functionality as qMTNet-2. qMTNet-fit was compared with a Gaussian kernel-based fitting. qMT parameters generated by the networks were compared with those from ground truth fitted with a dictionary-driven approach. The proposed networks achieved high peak signal-to-noise ratio (>30) and structural similarity index (>97) in reference to the ground truth. qMTNet-fit produced qMT parameters in concordance with the ground truth with better performance than the Gaussian kernel-based fitting. qMTNet-2 and qMTNet-1 could accelerate data acquisition at threefold and accelerate fitting at 5800- and 4218-fold, respectively. qMTNet-1 showed slightly better performance than qMTNet-2, whereas qMTNet-2 was more flexible for applications. The proposed single (qMTNet-1) and two joint neural networks (qMTNet-2) can accelerate qMT workflow for both data acquisition and fitting significantly. qMTNet has the potential to accelerate qMT imaging for clinical applications, which warrants further investigation.

Magnetization transfer and frequency distribution effects in the SSFP ellipse.

PurposeTo demonstrate that quantitative magnetization transfer (qMT) parameters can be extracted from steady‐state free‐precession (SSFP) data with no external T 1 map or banding artifacts.MethodsSSFP images with multiple MT weightings were acquired and qMT parameters fitted with a two‐stage elliptical signal model.ResultsMonte Carlo simulations and data from a 3T scanner indicated that most qMT parameters could be recovered with reasonable accuracy. Systematic deviations from theory were observed in white matter, consistent with previous literature on frequency distribution effects.ConclusionsqMT parameters can be extracted from SSFP data alone, in a manner robust to banding artifacts, despite several confounds.

Noninvasive quantitative magnetization transfer MRI reveals tubulointerstitial fibrosis in murine kidney.

Excessive tissue scarring, or fibrosis, is a critical contributor to end stage renal disease, but current clinical tests are not sufficient for assessing renal fibrosis. Quantitative magnetization transfer (qMT) MRI provides indirect information about the macromolecular composition of tissues. We evaluated measurements of the pool size ratio (PSR, the ratio of immobilized macromolecular to free water protons) obtained by qMT as a biomarker of tubulointerstitial fibrosis in a well-established murine model with progressive renal disease. MR images were acquired from 16-week-old fibrotic hHB-EGFTg/Tg mice and normal wild-type (WT) mice (N=12) at 7T. QMT parameters were derived using a two-pool five-parameter fitting model. A normal range of PSR values in the cortex and outer stripe of outer medulla (CR+OSOM) was determined by averaging across voxels within WT kidneys (mean±2SD). Regions in diseased mice whose PSR values exceeded the normal range above a threshold value (tPSR) were identified and measured. The spatial distribution of fibrosis was confirmed using picrosirius red stains. Compared with normal WT mice, scattered clusters of high PSR regions were observed in the OSOM of hHB-EGFTg/Tg mouse kidneys. Moderate increases in mean PSR (mPSR) of CR+OSOM regions were observed across fibrotic kidneys. The abnormally high PSR regions (% area) detected by the tPSR were significantly increased in hHB-EGFTg/Tg mice, and were highly correlated with regions of fibrosis detected by histological fibrosis indices measured from picrosirius red staining. Renal tubulointerstitial fibrosis in OSOM can thus be assessed by qMT MRI using an appropriate analysis of PSR. This technique may be used as an imaging biomarker for chronic kidney diseases.

Rapid framework for quantitative magnetization transfer imaging with interslice magnetization transfer and dictionary-driven fitting approaches.

To develop a rapid framework for quantitative magnetization transfer (qMT) imaging based on the 2D interslice MT and dictionary-driven fitting approaches. Sequential balanced steady-state free precession (bSSFP) scanning was performed on a whole human brain in a total of 12 conditions from six different interslice gaps and two different flip angles. To obtain qMT maps, the acquired 12 datasets were fitted to a dictionary predefined by using Bloch equation simulations based on the two-pool MT model. The proposed qMT method was compared to the conventional qMT methods, in terms of qMT parameter maps and processing time. The proposed method yielded qMT maps similar to those of the conventional method, indicating feasibility of modulating MT saturation frequency and power with the interslice gap and flip angle. The whole-brain qMT imaging could be completed in 8min because of the absence of separate MT pulses. The time for processing qMT parameters was significantly reduced through the dictionary-driven approach; it is 1000 times shorter than that without the dictionary-driven approach and 3 times shorter than that with the spoiled gradient recalled echo-qMT method that uses an analytical solution. The proposed dictionary-driven interslice qMT imaging provides qMT maps close to those from the conventional method with significantly reduced scan time and postprocessing time, which can make qMT imaging more clinically acceptable.

Spatiotemporal trajectories of quantitative magnetization transfer measurements in injured spinal cord using simplified acquisitions.

PurposeThis study aims to systematically evaluate the accuracy and precision of pool size ratio (PSR) measurements from quantitative magnetization transfer (qMT) acquisitions using simplified models in the context of assessing injury-associated spatiotemporal changes in spinal cords of non-human primates. This study also aims to characterize changes in the spinal tissue pathology in individual subjects, both regionally and longitudinally, in order to demonstrate the relationship between regional tissue compositional changes and sensorimotor behavioral recovery after cervical spinal cord injury (SCI).MethodsMRI scans were recorded on anesthetized monkeys at 9.4 T, before and serially after a unilateral section of the dorsal column tract. Images were acquired following saturating RF pulses at different offset frequencies. Models incorporating two pools of protons but with differing numbers of variable parameters were used to fit the data to derive qMT parameters. The results using different amounts of measured data and assuming different numbers of variable model parameters were compared. Behavioral impairments and recovery were assessed by a food grasping-retrieving task. Histological sections were obtained post mortem for validation of the injury.ResultsQMT fitting provided maps of pool size ratio (PSR), the relative amounts of immobilized protons exchanging magnetization compared to the “free” water. All the selected modeling approaches detected a lesion/cyst at the site of injury as significant reductions in PSR values. The regional contrasts in the PSR maps obtained using the different fittings varied, but the 2-parameter fitting results showed strong positive correlations with results from 5-parameter modeling. 2-parameter fitting results with modest (>3) RF offsets showed comparable sensitivity for detecting demyelination in white matter and loss of macromolecules in gray matter around lesion sites compared to 5-parameter fitting with fully-sampled data acquisitions. Histology confirmed that decreases of PSR corresponded to regional demyelination around lesion sites, especially when demyelination occurred along the dorsal column on the injury side. Longitudinally, PSR values of injured dorsal column tract and gray matter horns exhibited remarkable recovery that associated with behavioral improvement.ConclusionSimplified qMT modeling approaches provide efficient and sensitive means to detect and characterize injury-associated demyelination in white matter tracts and loss of macromolecules in gray matter and to monitor its recovery over time.

Sensitivity regularization of the Cramér-Rao lower bound to minimize B1 nonuniformity effects in quantitative magnetization transfer imaging.

To develop and validate a regularization approach of optimizing B1 insensitivity of the quantitative magnetization transfer (qMT) pool-size ratio (F). An expression describing the impact of B1 inaccuracies on qMT fitting parameters was derived using a sensitivity analysis. To simultaneously optimize for robustness against noise and B1 inaccuracies, the optimization condition was defined as the Cramér-Rao lower bound (CRLB) regularized by the B1 -sensitivity expression for the parameter of interest (F). The qMT protocols were iteratively optimized from an initial search space, with and without B1 regularization. Three 10-point qMT protocols (Uniform, CRLB, CRLB+B1 regularization) were compared using Monte Carlo simulations for a wide range of conditions (e.g., SNR, B1 inaccuracies, tissues). The B1 -regularized CRLB optimization protocol resulted in the best robustness of F against B1 errors, for a wide range of SNR and for both white matter and gray matter tissues. For SNR = 100, this protocol resulted in errors of less than 1% in mean F values for B1 errors ranging between -10 and 20%, the range of B1 values typically observed in vivo in the human head at field strengths of 3 T and less. Both CRLB-optimized protocols resulted in the lowest σF values for all SNRs and did not increase in the presence of B1 inaccuracies. This work demonstrates a regularized optimization approach for improving the robustness of auxiliary measurements (e.g., B1 ) sensitivity of qMT parameters, particularly the pool-size ratio (F). Predicting substantially less B1 sensitivity using protocols optimized with this method, B1 mapping could even be omitted for qMT studies primarily interested in F.

Optimization of selective inversion recovery magnetization transfer imaging for macromolecular content mapping in the human brain.

To optimize a selective inversion recovery (SIR) sequence for macromolecular content mapping in the human brain at 3.0T. SIR is a quantitative method for measuring magnetization transfer (qMT) that uses a low-power, on-resonance inversion pulse. This results in a biexponential recovery of free water signal that can be sampled at various inversion/predelay times (tI/ tD ) to estimate a subset of qMT parameters, including the macromolecular-to-free pool-size-ratio (PSR), the R1 of free water (R1f ), and the rate of MT exchange (kmf ). The adoption of SIR has been limited by long acquisition times (≈4 min/slice). Here, we use Cramér-Rao lower bound theory and data reduction strategies to select optimal tI /tD combinations to reduce imaging times. The schemes were experimentally validated in phantoms, and tested in healthy volunteers (N = 4) and a multiple sclerosis patient. Two optimal sampling schemes were determined: (i) a 5-point scheme (kmf estimated) and (ii) a 4-point scheme (kmf assumed). In phantoms, the 5/4-point schemes yielded parameter estimates with similar SNRs as our previous 16-point scheme, but with 4.1/6.1-fold shorter scan times. Pair-wise comparisons between schemes did not detect significant differences for any scheme/parameter. In humans, parameter values were consistent with published values, and similar levels of precision were obtained from all schemes. Furthermore, fixing kmf reduced the sensitivity of PSR to partial-volume averaging, yielding more consistent estimates throughout the brain. qMT parameters can be robustly estimated in ≤1 min/slice (without independent measures of ΔB0 , B1+, and T1 ) when optimized tI -tD combinations are selected.

Quantitative Magnetization Transfer in Monitoring Glioblastoma (GBM) Response to Therapy

Quantitative magnetization transfer (qMT) was used as a biomarker to monitor glioblastoma (GBM) response to chemo-radiation and identify the earliest time-point qMT could differentiate progressors from non-progressors. Nineteen GBM patients were recruited and MRI-scanned before (Day0), two weeks (Day14), and four weeks (Day28) into the treatment, and one month after the end of the treatment (Day70). Comprehensive qMT data was acquired, and a two-pool MT model was fit to the data. Response was determined at 3–8 months following the end of chemo-radiation. The amount of magnetization transfer ({bf{R}}{{bf{M}}}_{{bf{0b}}}/{{bf{R}}}_{{bf{a}}}) was significantly lower in GBM compared to normal appearing white matter (p < 0.001). Statistically significant difference was observed in {bf{R}}{{bf{M}}}_{{bf{0b}}}/{{bf{R}}}_{{bf{a}}} at Day0 between non-progressors (1.06 ± 0.24) and progressors (1.64 ± 0.48), with p = 0.006. Changes in several qMT parameters between Day14 and Day0 were able to differentiate the two cohorts with {bf{R}}{{bf{M}}}_{{bf{0b}}}/{{bf{R}}}_{{bf{a}}} providing the best separation (relative {bf{R}}{{bf{M}}}_{{bf{0b}}}/{{bf{R}}}_{{bf{a}},{bf{Non}}-{bf{progressor}}} = 1.34 ± 0.21, relative {bf{R}}{{bf{M}}}_{{bf{0b}}}/{{bf{R}}}_{{bf{a}},{bf{progressor}}} = 1.07 ± 0.08, p = 0.031). Thus, qMT characteristics of GBM are more sensitive to treatment effects compared to clinically used metrics. qMT could assess tumor aggressiveness and identify early progressors even before the treatment. Changes in qMT parameters within the first 14 days of the treatment were capable of separating early progressors from non-progressors, making qMT a promising biomarker to guide adaptive radiotherapy for GBM.

Longitudinal assessment of spinal cord injuries in nonhuman primates with quantitative magnetization transfer.

This study aimed to evaluate the reproducibility and specificity of quantitative magnetization transfer (qMT) imaging for monitoring spinal cord injuries (SCIs). MRI scans were performed in anesthetized monkeys at 9.4T, before and serially after a unilateral lesion of the cervical spinal cord. A two-pool fitting model was used to derive qMT parameters. qMT measures were reproducible across normal subjects, with an average pool size ratio (PSR) of 0.086 ± 0.003 (mean ± SD) for gray matter, and 0.120 ± 0.005 for white matter, respectively. Compared with normal gray matter, the PSR of abnormal tissues rostral and caudal to the injury site decreased by 19.5% (P < 0.05), while the PSR of the cyst-like volume decreased drastically weeks after SCI. Strong correlations in cyst-like regions were observed between PSR and other MRI measures including longitudinal relaxation rate (R1 ), apparent diffusion coefficient and fractional anisotropy (FA). Decreased PSR and FA values correlated well with demyelination in abnormal tissues. The qMT parameters provide robust and specific information about the molecular and cellular changes produced by SCI. PSR detected demyelination and loss of macromolecules in abnormal tissue regions rostral and caudal to the cyst/lesion sites.

Removal of cerebrospinal fluid partial volume effects in quantitative magnetization transfer imaging using a three-pool model with nonexchanging water component.

Parameters of the two-pool model describing magnetization transfer (MT) in macromolecule-rich tissues may be significantly biased in partial volume (PV) voxels containing cerebrospinal fluid (CSF). The purpose of this study was to develop a quantitative MT (qMT) method that provides indices insensitive to CSF PV averaging. We propose a three-pool MT model, in which PV macro-compartment is modeled as an additional nonexchanging water pool. We demonstrate the feasibility of model parameter estimation from several MT-weighted spoiled gradient echo datasets. We validated the three-pool model in numerical, phantom, and in vivo studies. PV averaging with the free water compartment reduces all qMT parameters, most significantly affecting macromolecular proton fraction (MPF) and cross-relaxation rate. Monte-Carlo simulations confirmed stability of the three-pool model fit. Unlike the standard two-pool model, the three-pool model qMT parameters were not affected by PV averaging in simulations and phantom studies. The three-pool model fit allowed CSF PV correction in brain PV voxels and resulted in good correlation with standard two-pool model parameters in non-PV voxels. Quantitative MT imaging based on a three-pool model with a non-exchanging water component yields a set of CSF-insensitive qMT parameters, which may improve MPF-based assessment of myelination in structures strongly affected by CSF PV averaging such as brain gray matter.

Cross-relaxation imaging of human patellar cartilage in vivo at 3.0T

To compare quantitative magnetization transfer (qMT) parameters of patellar cartilage measured using cross-relaxation imaging (CRI) in asymptomatic volunteers and patients with osteoarthritis. The study was performed with Institutional Review Board approval and with all subjects signing informed consent. CRI of the knee joint was performed at 3.0T on 20 asymptomatic volunteers and 11 patients with osteoarthritis. The fraction of macromolecular bound protons (f), the exchange rate constant between macromolecular bound protons and free water protons (k), and the T2 relaxation time of macromolecular bound protons (T2(B)) of patellar cartilage were measured. Mann-Whitney-Wilcoxon rank-sum tests were used to compare qMT parameters between asymptomatic volunteers and patients with osteoarthritis. Average f, k, and T2(B) of patellar cartilage was 12.46%, 7.22 s(-1), and 6.49 μs respectively for asymptomatic volunteers and 12.80%, 6.13 s(-1), and 6.80 μs respectively for patients with osteoarthritis. There were statistically significant differences between groups of subjects for k (P < 0.01) and T2(B) (P < 0.0001) but not f (P = 0.38) of patellar cartilage. Patients with osteoarthritis had significantly lower k and significantly higher T2(B) of patellar cartilage than asymptomatic volunteers which suggests that qMT parameters can detect changes in the macromolecular matrix of degenerative cartilage.