Quantitative interpretation of magnetization transfer

Magnetization transfer explains most of the T1 variability in the MRI literature

PURPOSE

To identify the predominant source of theT 1 $$ {T}_1 $$ variability described in the literature, which ranges from 0.6-1.1 s for brain white matter at 3 T.

METHODS

25T 1 $$ {T}_1 $$ -mapping methods from the literature were simulated with a mono-exponential and various magnetization-transfer (MT) models, each followed by mono-exponential fitting. A single set of model parameters was assumed for the simulation of all methods, and these parameters were estimated by fitting the simulation-based to the corresponding literatureT 1 $$ {T}_1 $$ values of white matter at 3 T. We acquired in vivo data with a quantitative magnetization transfer and threeT 1 $$ {T}_1 $$ -mapping techniques. The former was used to synthesize MR images that correspond to the threeT 1 $$ {T}_1 $$ -mapping methods. A mono-exponential model was fitted to the experimental and corresponding synthesized MR images.

RESULTS

Mono-exponential simulations suggest good inter-method reproducibility and fail to explain the highly variableT 1 $$ {T}_1 $$ estimates in the literature. In contrast, MT simulations suggest that a mono-exponential fit results in a variableT 1 $$ {T}_1 $$ and explain up to 62% of the literature's variability. In our own in vivo experiments, MT explains 70% of the observed variability.

CONCLUSION

The results suggest that a mono-exponential model does not adequately describe longitudinal relaxation in biological tissue. Therefore,T 1 $$ {T}_1 $$ in biological tissue should be considered only a semi-quantitative metric that is inherently contingent upon the imaging methodology, and comparisons between differentT 1 $$ {T}_1 $$ -mapping methods and the use of simplistic spin systems-such as doped-water phantoms-for validation should be viewed with caution.

Fine-Tuning Deep Learning Model for Quantitative Knee Joint Mapping With MR Fingerprinting and Its Comparison to Dictionary Matching Method: Fine-Tuning Deep Learning Model for Quantitative MRF

Magnetic resonance fingerprinting (MRF), as an emerging versatile and noninvasive imaging technique, provides simultaneous quantification of multiple quantitative MRI parameters, which have been used to detect changes in cartilage composition and structure in osteoarthritis. Deep learning (DL)-based methods for quantification mapping in MRF overcome the memory constraints and offer faster processing compared to the conventional dictionary matching (DM) method. However, limited attention has been given to the fine-tuning of neural networks (NNs) in DL and fair comparison with DM. In this study, we investigate the impact of training parameter choices on NN performance and compare the fine-tuned NN with DM for multiparametric mapping in MRF. Our approach includes optimizing NN hyperparameters, analyzing the singular value decomposition (SVD) components of MRF data, and optimization of the DM method. We conducted experiments on synthetic data, the NIST/ISMRM MRI system phantom with ground truth, and in vivo knee data from 14 healthy volunteers. The results demonstrate the critical importance of selecting appropriate training parameters, as these significantly affect NN performance. The findings also show that NNs improve the accuracy and robustness of T1, T2, and T1ρ mappings compared to DM in synthetic datasets. For in vivo knee data, the NN achieved comparable results for T1, with slightly lower T2 and slightly higher T1ρ measurements compared to DM. In conclusion, the fine-tuned NN can be used to increase accuracy and robustness for multiparametric quantitative mapping from MRF of the knee joint.

Accelerated ultrashort echo time quantitative magnetization transfer (UTE-qMT) imaging of macromolecular fraction (MMF) in cortical bone based on a self-attention convolutional neural network

PURPOSE

To combine ultrashort echo time quantitative magnetization transfer (UTE-qMT) imaging with a self-attention convolutional neural network (SAT-Net) for accelerated mapping of macromolecular fraction (MMF) in cortical bone.

MATERIALS AND METHODS

This institutional review board-approved study involved 31 young female subjects (young control, <45 years) and 50 postmenopausal subjects (6 normal (old control), 14 with osteopenia (osteopenia group), and 30 with osteoporosis (OP group)). After written informed consent was obtained from each subject, 15 UTE-qMT images of the tibial midshaft were acquired with three saturation powers (500°, 1000°, and 1500°) and five frequency offsets (2, 5, 10, 20, and 50 kHz) for each power to estimate the baseline MMF using a two-pool model. The densely connected SAT-Net model was used to predict bone MMF maps based on seven evenly distributed UTE-qMT images, which were well separated in terms of MT powers and frequency offsets (namely 5 and 20 kHz for 500° and 1500°, and 2, 10, 50 kHz for 1000°). Errors relative to the baseline MMF were calculated. Linear regression was used to assess the performance of the SAT-Net model. The mean MMF values for different groups were calculated.

RESULTS

Conventional two-pool modeling of seven evenly distributed UTE-qMT input images shows a significant relative error of ∼34 %. In comparison, the SAT-Net model accurately predicted MMF values for the tibial midshafts of 81 human subjects with a high correlation (R2 = 0.97, P < 0.0001) between the baseline and predicted values. The SAT-Net model accelerated UTE-qMT data acquisition by 2.1-fold, with relative errors in MMF mapping less than 2.4 %. The average MMF values were 46.10 ± 13.25 % for the young control group, 40.03 ± 2.56 % for the old control group, 31.22 ± 13.18 % for the osteopenia group, and 22.53 ± 8.12 % for the OP group.

CONCLUSION

While it is difficult to accelerate MMF mapping in bone using conventional two-pool modeling, the SAT-Net model allows accurate MMF mapping with a substantial reduction in the number of UTE-qMT input images. UTE-qMT with SAT-Net makes clinical evaluation of bone matrix possible.

Saturation transfer MR fingerprinting for magnetization transfer contrast and chemical exchange saturation transfer quantification

PURPOSE

The aim of this study was to develop a saturation transfer MR fingerprinting (ST-MRF) technique using a biophysics model-driven deep learning approach.

METHODS

A deep learning-based quantitative saturation transfer framework was proposed to estimate water, magnetization transfer contrast, and amide proton transfer (APT) parameters plus B0 field inhomogeneity. This framework incorporated a Bloch-McConnell simulator during neural network training and enforced consistency between synthesized MRF signals and experimentally acquired ST-MRF signals. Ground-truth numerical phantoms were used to assess the accuracy of estimated tissue parameters, and in vivo tissue parameters were validated using synthetic MRI analysis.

RESULTS

The proposed ST-MRF reconstruction network achieved a normalized root mean square error (nRMSE) of 9.3% when tested against numerical phantoms with a signal-to-noise ratio of 46 dB, which outperformed conventional Bloch-McConnell fitting (nRMSE of 15.3%) and dictionary-matching approaches (nRMSE of 19.5%). Synthetic MRI analysis indicated excellent similarity (RMSE = 3.2%) between acquired and synthesized ST-MRF images, demonstrating high in vivo reconstruction accuracy. In healthy human brains, the APT pool size ratios for gray and white matter were 0.16 ± 0.02% and 0.13 ± 0.02%, respectively, and the exchange rates for gray and white matter were 101 ± 25 Hz and 131 ± 27 Hz, respectively. The reconstruction network processed the eight tissue parameter maps in approximately 27 s for ST-MRF data sized at 256 × 256 × 9 × 103.

CONCLUSION

This study highlights the feasibility of the deep learning-based ST-MRF imaging for rapid and accurate quantification of free bulk water, magnetization transfer contrast, APT parameters, and B0 field inhomogeneity.

Simultaneous 3D quantitative magnetization transfer imaging and susceptibility mapping

PURPOSE

Introduce a unified acquisition and modeling strategy to simultaneously quantify magnetization transfer (MT), tissue susceptibility (χ $$ \chi $$ ) andT 2 * $$ {T}_2^{\ast } $$ .

THEORY AND METHODS

Magnetization transfer is induced through the application of off-resonance irradiation between excitation and acquisition of an RF-spoiled gradient-echo scheme, where free pool spin-lattice relaxation (T 1 F $$ {T}_1^{\mathrm{F}} $$ ), macromolecular proton fraction (f $$ f $$ ) and magnetization exchange rate (k F $$ {k}_{\mathrm{F}} $$ ) were calculated by modeling the magnitude of the MR signal using a binary spin-bath MT model withB 1 + $$ {B}_1^{+} $$ inhomogeneity correction via Bloch-Siegert shift. Simultaneously, a multi-echo acquisition is incorporated into this framework to measure the time evolution of both signal magnitude and phase, which was further modeled for estimatingT 2 * $$ {T}_2^{\ast } $$ and tissue susceptibility. In this work, we demonstrate the feasibility of this new acquisition and modeling strategy in vivo on the brain tissue.

RESULTS

In vivo brain experiments were conducted on five healthy subjects to validate our method. Utilizing an analytically derived signal model, we simultaneously obtained 3DT 1 F $$ {T}_1^{\mathrm{F}} $$ ,f $$ f $$ ,k F $$ {k}_{\mathrm{F}} $$ ,χ $$ \chi $$ andT 2 * $$ {T}_2^{\ast } $$ maps of the whole brain. Our results from the brain regional analysis show good agreement with those previously reported in the literature, which used separate MT and QSM methods.

CONCLUSION

A unified acquisition and modeling strategy based on an analytical signal model that fully leverages both the magnitude and phase of the acquired signals was demonstrated and validated for simultaneous MT, susceptibility andT 2 * $$ {T}_2^{\ast } $$ quantification that are free fromB 1 + $$ {B}_1^{+} $$ bias.

Parallel transmit hybrid pulse design for controlled on-resonance magnetization transfer in R1 mapping at 7T

PURPOSE

This work proposes a "hybrid" RF pulse design method for parallel transmit (pTx) systems to simultaneously control flip angle and root-mean-squaredB 1 + $$ {\mathrm{B}}_1^{+} $$ (B 1 rms $$ {B}_1^{\mathrm{rms}} $$ ). These pulses are generally only designed for flip angle, however, this can lead to uncontrolledB 1 rms $$ {B}_1^{\mathrm{rms}} $$ , which then leads to variable magnetization transfer (MT) effects. We demonstrate the hybrid design approach for quantitative imaging where both flip angle andB 1 rms $$ {B}_1^{\mathrm{rms}} $$ are important.

THEORY AND METHODS

A dual cost function optimization is performed containing the normalized mean squared errors of the flip angle andB 1 rms $$ {B}_1^{\mathrm{rms}} $$ distributions weighted by a parameterλ $$ \lambda $$ . Simulations were conducted to study the behavior of both properties when simultaneously optimizing them. In vivo experiments on a 7T MRI system with an 8-channel pTx head coil were carried out to study the effect of the hybrid design approach on variable flip angleR 1 $$ {\mathrm{R}}_1 $$ (= 1/T1) mapping.

RESULTS

Simulations showed that both flip angle andB 1 rms $$ {B}_1^{\mathrm{rms}} $$ can be homogenized simultaneously without detriment to either when compared to an individual optimization. By homogenizing flip angle andB 1 rms $$ {B}_1^{\mathrm{rms}} $$ ,R 1 $$ {\mathrm{R}}_1 $$ maps were more uniform (coefficient of variation 6.6% vs. 13.0%) compared to those acquired with pulses that only homogenized flip angle.

CONCLUSION

The proposed hybrid design homogenizes on-resonance MT effects while homogenizing the flip angle distribution, with only a small detriment in the latter compared to a pulse that just homogenizes flip angle. This improvedR 1 $$ {\mathrm{R}}_1 $$ mapping by controlling incidental MT effects, yielding more uniformR 1 $$ {\mathrm{R}}_1 $$ maps.

An unconstrained four pool model analysis of proton relaxation and magnetization transfer in ex vivo white matter

Understanding proton relaxation in the brain's white matter remains an active field of magnetic resonance imaging research. Models of varying complexity have been proposed to link measurements to tissue composition/microstructure, in particular myelination. Although the presence of multiple aqueous and nonaqueous proton pools is well established experimentally, so-called "quantitative MRI" is usually based on simpler models due to the large number of model parameters. In this work, a comprehensive set of parameters characterizing a four-pool model is obtained. A piece of fixed porcine spinal-cord WM was investigated at 3 T and temperatures between 21 and 35 °C. Measurements included a wide range of preparations of the spin system in combination with long echo trains to achieve sensitivity to all model parameters. The results allow the extraction of all intrinsic relaxation and exchange rates as well as assigning them to specific dynamic processes involving tissue water. A critical assessment indicates that simpler models often lack specificity to myelin.

Rapid whole-brain quantitative MT imaging

PURPOSE

To provide a robust whole-brain quantitative magnetization transfer (MT) imaging method that is not limited by long acquisition times.

METHODS

Two variants of a spiral 2D interleaved multi-slice spoiled gradient echo (SPGR) sequence are used for rapid quantitative MT imaging of the brain at 3 T. A dual flip angle, steady-state prepared, double-contrast method is used for combined B1 and-T1 mapping in combination with a single-contrast MT-prepared acquisition over a range of different saturation flip angles (50 deg to 850 deg) and offset frequencies (1 kHz and 10 kHz). Five sets (containing minimum 6 to maximum 18 scans) with different MT-weightings were acquired. In addition, main magnetic field inhomogeneities (ΔB0) were measured from two Cartesian low-resolution 2D SPGR scans with different echo times. Quantitative MT model parameters were derived from all sets using a two-pool continuous-wave model analysis, yielding the pool-size ratio, F, their exchange rate, kf, and their transverse relaxation time, T2r.

RESULTS

Whole-brain quantitative MT imaging was feasible for all sets with total acquisition times ranging from 7:15 min down to 3:15 min. For accurate modeling, B1-correction was essential for all investigated sets, whereas ΔB0-correction showed limited bias for the observed maximum off-resonances at 3 T.

CONCLUSION

The combination of rapid B1-T1 mapping and MT-weighted imaging using a 2D multi-slice spiral SPGR research sequence offers excellent prospects for rapid whole-brain quantitative MT imaging in the clinical setting.

Quantitative magnetization transfer and g-ratio imaging of white matter myelin in early psychotic spectrum disorders

Myelin abnormalities in white matter have been implicated in the pathophysiology of psychotic spectrum disorders (PSD), which are characterized by brain dysconnectivity as a core feature. Among evidence from in vivo MRI studies, diffusion imaging findings have largely supported disrupted white matter integrity in PSD; however, they are not specific to myelin changes. Using a multimodal imaging approach, the current study aimed to further delineate myelin and microstructural changes in the white matter of a young PSD cohort. We utilized quantitative magnetization transfer (qMT) imaging combined with advanced diffusion imaging to estimate specific myelin-related biophysical properties in 51 young adult PSD patients compared with 38 age-matched healthy controls. The macromolecular proton fraction (MPF) obtained from qMT was used as a specific marker of myelin content. Additionally, MPF was employed along with diffusion metrics of axonal density (vic) and extra-cellular volume fraction to derive the g-ratio, a measure of relative myelin sheath thickness defined as the ratio of inner to outer axonal diameter. Compared to controls, we observed a widespread MPF reduction and localized g-ratio increase in patients, primarily those with a diagnosis of schizophrenia or depressive schizoaffective disorder. No between-group differences were noted in vic, suggesting similar axonal densities across groups. Correlation analysis revealed that lower MPF was significantly related to poorer working memory performance in PSD, while the HC group showed a positive association for working memory with both g-ratio and vic. The pattern of changes observed in our multimodal imaging markers suggests that PSD, depending on symptomatology, is characterized by specific alterations in white matter integrity and myelin-axonal geometry of major white matter tracts, which may impact working memory function. These findings provide a more detailed view of myelin-related white matter changes in early stages of PSD.

Orientation-independent quantification of macromolecular proton fraction in tissues with suppression of residual dipolar coupling

Quantitative magnetization transfer (MT) imaging enables noninvasive characterization of the macromolecular environment of tissues. However, recent work has highlighted that the quantification of MT parameters using saturation radiofrequency (RF) pulses exhibits orientation dependence in ordered tissue structures, potentially confounding its clinical applications. Notably, in tissues with ordered structures, such as articular cartilage and myelin, the residual dipolar coupling (RDC) effect can arise owing to incomplete averaging of dipolar-dipolar interactions of water protons. In this study, we demonstrated the confounding effect of RDC on quantitative MT imaging in ordered tissues can be suppressed by using an emerging technique known as macromolecular proton fraction mapping based on spin-lock (MPF-SL). The off-resonance spin-lock RF pulse in MPF-SL could be designed to generate a strong effective spin-lock field to suppress RDC without violating the specific absorption rate and hardware limitations in clinical scans. Furthermore, suppressing the water pool contribution in MPF-SL enabled the application of a strong effective spin-lock field without confounding effects from direct water saturation. Our findings were experimentally validated using human knee specimens and healthy human cartilage. The results demonstrated that MPF-SL exhibits lower sensitivity to tissue orientation compared withR 2 $$ {R}_2 $$ ,R 1 ρ $$ {R}_{1\rho } $$ , and saturation-pulse-based MT imaging. Consequently, MPF-SL could serve as a valuable orientation-independent technique for the quantification of MPF.

Magnetic resonance imaging techniques for monitoring glioma response to chemoradiotherapy

PURPOSE

Treatment response assessment for gliomas currently uses changes in tumour size as measured with T1- and T2-weighted MRI. However, changes in tumour size may occur many weeks after therapy completion and are confounded by radiation treatment effects. Advanced MRI techniques sensitive to tumour physiology may provide complementary information to evaluate tumour response at early timepoints during therapy. The objective of this review is to provide a summary of the history and current knowledge regarding advanced MRI techniques for early treatment response evaluation in glioma.

METHODS

The literature survey included perfusion MRI, diffusion-weighted imaging, quantitative magnetization transfer imaging, and chemical exchange transfer MRI. Select articles spanning the history of each technique as applied to treatment response evaluation in glioma were chosen. This report is a narrative review, not formally systematic.

RESULTS

Chemical exchange saturation transfer imaging potentially offers the earliest method to detect tumour response due to changes in metabolism. Diffusion-weighted imaging is sensitive to changes in tumour cellularity later during radiotherapy and is prognostic for progression-free and overall survival. Substantial evidence suggests that perfusion MRI can differentiate between tumour recurrence and treatment effect, but consensus regarding acquisition, processing, and interpretation is still lacking. Magnetization transfer imaging shows promise for detecting subtle white matter damage which could indicate tumour invasion, but more research in this area is needed.

CONCLUSION

Advanced MRI techniques show potential for early treatment response assessment, but each technique alone lacks specificity. Multiparametric imaging may be necessary to aid biological interpretation and enable treatment guidance.

Cramér-Rao Bound Optimized Subspace Reconstruction in Quantitative MRI

OBJECTIVE

We extend the traditional framework for estimating subspace bases in quantitative MRI that maximize the preserved signal energy to additionally preserve the Cramér-Rao bound (CRB) of the biophysical parameters and, ultimately, improve accuracy and precision in the quantitative maps.

METHODS

To this end, we introduce an approximate compressed CRB based on orthogonalized versions of the signal's derivatives with respect to the model parameters. This approximation permits singular value decomposition (SVD)-based minimization of both the CRB and signal losses during compression.

RESULTS

Compared to the traditional SVD approach, the proposed method better preserves the CRB across all biophysical parameters with minimal cost to the preserved signal energy, leading to reduced bias and variance of the parameter estimates in simulation. In vivo, improved accuracy and precision are observed in two quantitative neuroimaging applications.

CONCLUSION

The proposed method permits subspace reconstruction with a more compact basis, thereby offering significant computational savings.

SIGNIFICANCE

Efficient subspace reconstruction facilitates the validation and translation of advanced quantitative MRI techniques, e.g., magnetization transfer and diffusion.

Are Collagen Protons Visible with the Zero Echo Time (ZTE) Magnetic Resonance Imaging Sequence: A D2O Exchange and Freeze-Drying Study

It is known that ultrashort echo time (UTE) magnetic resonance imaging (MRI) sequences can detect signals from water protons but not collagen protons in short T2 species such as cortical bone and tendons. However, whether collagen protons are visible with the zero echo time (ZTE) MRI sequence is still unclear. In this study, we investigated the potential of the ZTE MRI sequence on a clinical 3T scanner to directly image collagen protons via D2O exchange and freeze-drying experiments. ZTE and UTE MRI sequences were employed to image fully hydrated bovine cortical bone (n = 10) and human patellar tendon (n = 1) specimens. Then, each specimen was kept in a 30 mL syringe filled with D2O solution for two days. Fresh D2O was flushed every 2 h to reach a more complete D2O-H2O exchange. Later, the samples were lyophilized for over 40 h and then sealed in tubes. Finally, the samples were brought to room temperature and visualized using the identical 3D ZTE and UTE sequences. All hydrated bone and tendon specimens showed high signals with ZTE and UTE sequences. However, all specimens showed zero signal after the D2O exchange and freeze-drying procedures. Therefore, similar to UTE imaging, the signal source in ZTE imaging is water. The ZTE sequence cannot directly detect signals from collagen protons in bone and tendons.

Ultra-low-field magnetization transfer imaging at 0.055T with low specific absorption rate

PURPOSE

To demonstrate magnetization transfer (MT) effects with low specific absorption rate (SAR) on ultra-low-field (ULF) MRI.

METHODS

MT imaging was implemented by using sinc-modulated RF pulse train (SPT) modules to provide bilateral off-resonance irradiation. They were incorporated into 3D gradient echo (GRE) and fast spin echo (FSE) protocols on a shielding-free 0.055T head scanner. MT effects were first verified using phantoms. Brain MT imaging was conducted in both healthy subjects and patients.

RESULTS

MT effects were clearly observed in phantoms using six SPT modules with total flip angle 3600° at central primary saturation bands of approximate offset ±786 Hz, even in the presence of large relative B0 inhomogeneity. For brain, strong MT effects were observed in gray matter, white matter, and muscle in 3D GRE and FSE imaging using six and sixteen SPT modules with total flip angle 3600° and 9600°, respectively. Fat, cerebrospinal fluid, and blood exhibited relatively weak MT effects. MT preparation enhanced tissue contrasts in T2-weighted and FLAIR-like images, and improved brain lesion delineation. The estimated MT SAR was 0.0024 and 0.0008 W/kg for two protocols, respectively, which is far below the US Food and Drug Administration (FDA) limit of 3.0 W/kg.

CONCLUSION

Robust MT effects can be readily obtained at ULF with extremely low SAR, despite poor relative B0 homogeneity in ppm. This unique advantage enables flexible MT pulse design and implementation on low-cost ULF MRI platforms to achieve strong MT effects in brain and beyond, potentially augmenting their clinical utility in the future.

T1 relaxation and axon fibre configuration in human white matter

Understanding the effects of white matter (WM) axon fibre microstructure on T1 relaxation is important for neuroimaging. Here, we have studied the interrelationship between T1 and axon fibre configurations at 3T and 7T. T1 and S0 (=signal intensity at zero TI) were computed from MP2RAGE images acquired with six inversion recovery times. Multishell diffusion MRI images were analysed for fractional anisotropy (FA); MD; V1; the volume fractions for the first (f1), second (f2) and third (f3) fibre configuration; and fibre density cross-section images for the first (fdc1), second (fdc2) and third (fdc3) fibres. T1 values were plotted as a function of FA, f1, f2, f3, fdc1, fdc2 and fdc3 to examine interrelationships between the longitudinal relaxation and the diffusion MRI microstructural measures. T1 values decreased with increasing FA, f1 and f2 in a nonlinear fashion. At low FA values (from 0.2 to 0.4), a steep shortening of T1 was followed by a shallow shortening by 6%-10% at both fields. The steep shortening was associated with decreasing S0 and MD. T1 also decreased with increasing fdc1 values in a nonlinear fashion. Instead, only a small T1 change as a function of either f3 or fdc3 was observed. In WM areas selected by fdc1 only masks, T1 was shorter than in those with fdc2/fdc3. In WM areas with high single fibre populations, as delineated by f1/fdc1 masks, T1 was shorter than in tissue with high complex fibre configurations, as segmented by f2/fdc2 or f3/fdc3 masks. T1 differences between these WM areas are attributable to combined effects by T1 anisotropy and lowered FA. The current data show strong interrelationships between T1, axon fibre configuration and orientation in healthy WM. It is concluded that diffusion MRI microstructural measures are essential in the effort to interpret quantitative T1 images in terms of tissue state in health and disease.

Unraveling contributions to the Z-spectrum signal at 3.5 ppm of human brain tumors

PURPOSE

To evaluate the influence of the confounding factors, direct water saturation (DWS), and magnetization transfer contrast (MTC) effects on measured Z-spectra and amide proton transfer (APT) contrast in brain tumors.

METHODS

High-grade glioma patients were scanned using an RF saturation-encoded 3D MR fingerprinting (MRF) sequence at 3 T. For MRF reconstruction, a recurrent neural network was designed to learn free water and semisolid macromolecule parameter mappings of the underlying multiple tissue properties from saturation-transfer MRF signals. The DWS spectra and MTC spectra were synthesized by solving Bloch-McConnell equations and evaluated in brain tumors.

RESULTS

The dominant contribution to the saturation effect at 3.5 ppm was from DWS and MTC effects, but 25%-33% of the saturated signal in the gadolinium-enhancing tumor (13%-20% for normal tissue) was due to the APT effect. The APT# signal of the gadolinium-enhancing tumor was significantly higher than that of the normal-appearing white matter (10.1% vs. 8.3% at 1 μT and 11.2% vs. 7.8% at 1.5 μT).

CONCLUSION

The RF saturation-encoded MRF allowed us to separate contributions to the saturation signal at 3.5 ppm in the Z-spectrum. Although free water and semisolid MTC are the main contributors, significant APT contrast between tumor and normal tissues was observed.

Towards assessing and improving the reliability of ultrashort echo time quantitative magnetization transfer (UTE-qMT) MRI of cortical bone: In silico and ex vivo study

OBJECTIVE

To assess and improve the reliability of the ultrashort echo time quantitative magnetization transfer (UTE-qMT) modeling of the cortical bone.

MATERIALS AND METHODS

Simulation-based digital phantoms were created that mimic the UTE-qMT properties of cortical bones. A wide range of SNR from 25 to 200 was simulated by adding different levels of noise to the synthesized MT-weighted images to assess the effect of SNR on UTE-qMT fitting results. Tensor-based denoising algorithm was applied to improve the fitting results. These results from digital phantom studies were validated via ex vivo rat leg bone scans.

RESULTS

The selection of initial points for nonlinear fitting and the number of data points tested for qMT analysis have minimal effect on the fitting result. Magnetization exchange rate measurements are highly dependent on the SNR of raw images, which can be substantially improved with an appropriate denoising algorithm that gives similar fitting results from the raw images with an 8-fold higher SNR.

DISCUSSION

The digital phantom approach enables the assessment of the reliability of bone UTE-qMT fitting by providing the known ground truth. These findings can be utilized for optimizing the data acquisition and analysis pipeline for UTE-qMT imaging of cortical bones.

Single-point macromolecular proton fraction mapping using a 0.3 T permanent magnet MRI system: phantom and healthy volunteer study

In a 0.3 T permanent-magnet magnetic resonance imaging (MRI) system, quantifying myelin content is challenging owing to long imaging times and low signal-to-noise ratio. macromolecular proton fraction (MPF) offers a quantitative assessment of myelin in the nervous system. We aimed to demonstrate the practical feasibility of MPF mapping in the brain using a 0.3 T MRI. Both 0.3 T and 3.0 T MRI systems were used. The MPF-mapping protocol used a standard 3D fast spoiled gradient-echo sequence based on the single-point reference method. Proton density, T1, and magnetization transfer-weighted images were obtained from a protein phantom at 0.3 T and 3.0 T to calculate MPF maps. MPF was measured in all phantom sections to assess its relationship to protein concentration. We acquired MPF maps for 16 and 8 healthy individuals at 0.3 T and 3.0 T, respectively, measuring MPF in nine brain tissues. Differences in MPF between 0.3 T and 3.0 T, and between 0.3 T and previously reported MPF at 0.5 T, were investigated. Pearson's correlation coefficient between protein concentration and MPF at 0.3 T and 3.0 T was 0.92 and 0.90, respectively. The 0.3 T MPF of brain tissue strongly correlated with 3.0 T MPF and literature values measured at 0.5 T. The absolute mean differences in MPF between 0.3 T and 0.5 T were 0.42% and 1.70% in white and gray matter, respectively. Single-point MPF mapping using 0.3 T permanent-magnet MRI can effectively assess myelin content in neural tissue.

Self-supervised learning for denoising of multidimensional MRI data

PURPOSE

To develop a fast denoising framework for high-dimensional MRI data based on a self-supervised learning scheme, which does not require ground truth clean image.

THEORY AND METHODS

Quantitative MRI faces limitations in SNR, because the variation of signal amplitude in a large set of images is the key mechanism for quantification. In addition, the complex non-linear signal models make the fitting process vulnerable to noise. To address these issues, we propose a fast deep-learning framework for denoising, which efficiently exploits the redundancy in multidimensional MRI data. A self-supervised model was designed to use only noisy images for training, bypassing the challenge of clean data paucity in clinical practice. For validation, we used two different datasets of simulated magnetization transfer contrast MR fingerprinting (MTC-MRF) dataset and in vivo DWI image dataset to show the generalizability.

RESULTS

The proposed method drastically improved denoising performance in the presence of mild-to-severe noise regardless of noise distributions compared to previous methods of the BM3D, tMPPCA, and Patch2self. The improvements were even pronounced in the following quantification results from the denoised images.

CONCLUSION

The proposed MD-S2S (Multidimensional-Self2Self) denoising technique could be further applied to various multi-dimensional MRI data and improve the quantification accuracy of tissue parameter maps.

Testing quantitative magnetization transfer models with membrane lipids

PURPOSE

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Characterization of the orientation dependence of magnetization transfer measures in single and crossing-fiber white matter

PURPOSE

To fully characterize the orientation dependence of magnetization transfer (MT) and inhomogeneous MT (ihMT) measures in the whole white matter (WM), for both single-fiber and crossing-fiber voxels.

METHODS

A characterization method was developed using the fiber orientation obtained from diffusion MRI (dMRI) with diffusion tensor imaging (DTI) and constrained spherical deconvolution. This allowed for characterization of the orientation dependence of measures in all of WM, regardless of the number of fiber orientation in a voxel. Furthermore, the orientation dependence inside 31 different WM bundles was characterized to evaluate the homogeneity of the effect. Variation of the results within and between-subject was assessed from a 12-subject dataset.

RESULTS

Previous results for single-fiber voxels were reproduced and a novel characterization was produced in voxels of crossing fibers, which seems to follow trends consistent with single-fiber results. Heterogeneity of the orientation dependence across bundles was observed, but homogeneity within similar bundles was also highlighted. Differences in behavior between MT and ihMT measures, as well as the ratio and saturation versions of these, were noted.

CONCLUSION

Orientation dependence characterization was proven possible over the entirety of WM. The vast range of effects and subtleties of the orientation dependence on MT measures showed the need for, but also the challenges of, a correction method.

Collagen degradation assessment with an in vitro rotator cuff tendinopathy model using multiparametric ultrashort-TE magnetization transfer (UTE-MT) imaging

PURPOSE

This study aims to assess ultrashort-TE magnetization transfer (UTE-MT) imaging of collagen degradation using an in vitro model of rotator cuff tendinopathy.

METHODS

Thirty-six supraspinatus tendon specimens were divided into three groups and treated with 600 U collagenase (Group 1), 150 U collagenase (Group 2), and phosphate buffer saline (Group 3). UTE-MT imaging was performed to assess changes in macromolecular fraction (MMF), macromolecule transverse relaxation time (T2m), water longitudinal relaxation rate constant (R1m), the magnetization exchange rate from the macromolecular to water pool (Rm0 w) and from water to the macromolecular pool (Rm0 m), and magnetization transfer ratio (MTR) at baseline and following digestion and their differences between groups. Biochemical and histological studies were conducted to determine the extent of collagen degradation. Correlation analyses were performed with MMF, T2m, R1m, Rm0 w, Rm0 m, and MTR, respectively. Univariate and multivariate linear regression analyses were performed to evaluate combinations of UTE-MT parameters to predict collagen degradation.

RESULTS

MMF, T2m, R1m, Rm0 m, and MTR decreased after digestion. MMF (r = -0.842, p < 0.001), MTR (r = -0.78, p < 0.001), and Rm0 m (r = -0.662, p < 0.001) were strongly negatively correlated with collagen degradation. The linear regression model of differences in MMF and Rm0 m before and after digestion explained 68.9% of collagen degradation variation in the tendon. The model of postdigestion in MMF and T2m and the model of MTR explained 54.2% and 52.3% of collagen degradation variation, respectively.

CONCLUSION

This study highlighted the potential of UTE-MT parameters for evaluation of supraspinatus tendinopathy.

Fast magnetization transfer saturation imaging of the brain using MP2RAGE T1 mapping

PURPOSE

Magnetization transfer saturation (MTsat) mapping is commonly used to examine the macromolecular content of brain tissue. This study compared variable flip angle (VFA) T1 mapping against compressed-sensing MP2RAGE (csMP2RAGE) T1 mapping for accelerating MTsat imaging.

METHODS

VFA, MP2RAGE, and csMP2RAGE were compared against inversion-recovery T1 in an aqueous phantom at 3 T. The same 1-mm VFA, MP2RAGE, and csMP2RAGE protocols were acquired in 4 healthy subjects to compare T1 and MTsat. Bloch-McConnell simulations were used to investigate differences between the phantom and in vivo T1 results. Ten healthy controls were imaged twice with the csMP2RAGE MTsat protocol to quantify repeatability.

RESULTS

The MP2RAGE and csMP2RAGE protocols were 13.7% and 32.4% faster than the VFA protocol, respectively. At these scan times, all approaches provided strong repeatability and accurate T1 times (< 5% difference) in the phantom, but T1 accuracy was more impacted by T2 for VFA than for MP2RAGE. In vivo, VFA estimated longer T1 times than MP2RAGE and csMP2RAGE. Simulations suggest that the differences in the T1 measured using VFA, MP2RAGE, and inversion recovery could be explained by the magnetization-transfer effects. In the test-retest experiment, we found that the csMP2RAGE has a minimum detectable change of 2.3% for T1 mapping and 7.8% for MTsat imaging.

CONCLUSIONS

We demonstrated that MP2RAGE can be used in place of VFA T1 mapping in an MTsat protocol. Furthermore, a shorter scan time and high repeatability can be achieved using the csMP2RAGE sequence.

Bias-reduced neural networks for parameter estimation in quantitative MRI

PURPOSE

To develop neural network (NN)-based quantitative MRI parameter estimators with minimal bias and a variance close to the Cramér-Rao bound.

THEORY AND METHODS

We generalize the mean squared error loss to control the bias and variance of the NN's estimates, which involves averaging over multiple noise realizations of the same measurements during training. Bias and variance properties of the resulting NNs are studied for two neuroimaging applications.

RESULTS

In simulations, the proposed strategy reduces the estimates' bias throughout parameter space and achieves a variance close to the Cramér-Rao bound. In vivo, we observe good concordance between parameter maps estimated with the proposed NNs and traditional estimators, such as nonlinear least-squares fitting, while state-of-the-art NNs show larger deviations.

CONCLUSION

The proposed NNs have greatly reduced bias compared to those trained using the mean squared error and offer significantly improved computational efficiency over traditional estimators with comparable or better accuracy.

Spin-lock based fast whole-brain 3D macromolecular proton fraction mapping of relapsing-remitting multiple sclerosis

A sensitive and efficient imaging technique is required to assess the subtle abnormalities occurring in the normal-appearing white matter (NAWM) and normal-appearing grey matter (NAGM) in patients with relapsing-remitting multiple sclerosis (RRMS). In this study, a fast 3D macromolecular proton fraction (MPF) quantification based on spin-lock (fast MPF-SL) sequence was proposed for brain MPF mapping. Thirty-four participants, including 17 healthy controls and 17 RRMS patients were prospectively recruited. We conducted group comparison and correlation between conventional MPF-SL, fast MPF-SL, and DWI, and compared differences in quantified parameters within MS lesions and the regional NAWM, NAGM, and normal-appearing deep grey matter (NADGN). MPF of MS lesions was significantly reduced (7.17% ± 1.15%, P < 0.01) compared to all corresponding normal-appearing regions. MS patients also showed significantly reduced mean MPF values compared with controls in NAGM (4.87% ± 0.38% vs 5.21% ± 0.32%, P = 0.01), NAWM (9.49% ± 0.69% vs 10.32% ± 0.59%, P < 0.01) and NADGM (thalamus 5.59% ± 0.67% vs 6.00% ± 0.41%, P = 0.04; caudate 5.10% ± 0.55% vs 5.53% ± 0.58%, P = 0.03). MPF and ADC showed abnormalities in otherwise normal appearing close to lesion areas (P < 0.01). In conclusion, time-efficient MPF mapping of the whole brain can be acquired efficiently (< 3 min) using fast MPF-SL. It offers a promising alternative way to detect white matter abnormalities in MS.

Protocol for a randomised controlled unblinded feasibility trial of HD-DRUM: a rhythmic movement training application for cognitive and motor symptoms in people with Huntington's disease

INTRODUCTION

Huntington's disease (HD) is an inherited neurodegenerative disease causing progressive cognitive and motor decline, largely due to basal ganglia (BG) atrophy. Rhythmic training offers promise as therapy to counteract BG-regulated deficits. We have developed HD-DRUM, a tablet-based app to enhance movement synchronisation skills and improve cognitive and motor abilities in people with HD. This paper outlines a randomised controlled unblinded trial protocol to determine the feasibility of a larger effectiveness trial for HD-DRUM. Additionally, the trial investigates cognitive and motor function measures, along with brain microstructure, aiming to advance our understanding of the neural mechanisms underlying training effects.

METHODS, DESIGN AND ANALYSIS

50 individuals with HD, confirmed by genetic testing, and a Total Functional Capacity (TFC) score of 9-13, will be recruited into a two-arm randomised controlled feasibility trial. Consenting individuals with HD will be randomised to the intervention group, which entails 8 weeks of at-home usage of HD-DRUM or a usual-activity control group. All participants will undergo cognitive and motor assessments, alongside ultra-strong gradient (300 mT/m) brain microstructural MRI before and after the 8-week period. The feasibility assessment will encompass recruitment, retention, adherence and acceptability of HD-DRUM following prespecified criteria. The study will also evaluate variations in cognitive and motor performance and brain microstructure changes resulting from the intervention to determine effect size estimates for future sample size calculations.

ETHICS AND DISSEMINATION

The study has received favourable ethical opinion from the Wales Research Ethics Committee 2 (REC reference: 22/WA/0147) and is sponsored by Cardiff University (SPON1895-22) (Research Integrity, Governance and Ethics Team, Research & Innovation Services, Cardiff University, second Floor, Lakeside Building, University Hospital of Wales, Cardiff, CF14 4XW). Findings will be disseminated to researchers and clinicians in peer-reviewed publications and conference presentations, and to participants, carers and the general public via newsletters and public engagement activities. Data will be shared with the research community via the Enroll-HD platform.

TRIAL REGISTRATION NUMBER

ISRCTN11906973.

The myelin water imaging transcriptome: myelin water fraction regionally varies with oligodendrocyte-specific gene expression

Identifying sensitive and specific measures that can quantify myelin are instrumental in characterizing microstructural changes in neurological conditions. Neuroimaging transcriptomics is emerging as a valuable technique in this regard, offering insights into the molecular basis of promising candidates for myelin quantification, such as myelin water fraction (MWF). We aimed to demonstrate the utility of neuroimaging transcriptomics by validating MWF as a myelin measure. We utilized data from a normative MWF brain atlas, comprised of 50 healthy subjects (mean age = 25 years, range = 17-42 years) scanned at 3 Tesla. Magnetic resonance imaging data included myelin water imaging to extract MWF and T1 anatomical scans for image registration and segmentation. We investigated the inter-regional distributions of gene expression data from the Allen Human Brain Atlas in conjunction with inter-regional MWF distribution patterns. Pearson correlations were used to identify genes with expression profiles mirroring MWF. The Single Cell Type Atlas from the Human Protein Atlas was leveraged to classify genes into gene sets with high cell type specificity, and a control gene set with low cell type specificity. Then, we compared the Pearson correlation coefficients for each gene set to determine if cell type-specific gene expression signatures correlate with MWF. Pearson correlation coefficients between MWF and gene expression for oligodendrocytes and adipocytes were significantly higher than for the control gene set, whereas correlations between MWF and inhibitory/excitatory neurons were significantly lower. Our approach in integrating transcriptomics with neuroimaging measures supports an emerging technique for understanding and validating MRI-derived markers such as MWF.

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 rateR 1 s 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'sR 1 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 inR 1 s 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.

So You Want to Image Myelin Using MRI: Magnetic Susceptibility Source Separation for Myelin Imaging

In MRI, researchers have long endeavored to effectively visualize myelin distribution in the brain, a pursuit with significant implications for both scientific research and clinical applications. Over time, various methods such as myelin water imaging, magnetization transfer imaging, and relaxometric imaging have been developed, each carrying distinct advantages and limitations. Recently, an innovative technique named as magnetic susceptibility source separation has emerged, introducing a novel surrogate biomarker for myelin in the form of a diamagnetic susceptibility map. This paper comprehensively reviews this cutting-edge method, providing the fundamental concepts of magnetic susceptibility, susceptibility imaging, and the validation of the diamagnetic susceptibility map as a myelin biomarker that indirectly measures myelin content. Additionally, the paper explores essential aspects of data acquisition and processing, offering practical insights for readers. A comparison with established myelin imaging methods is also presented, and both current and prospective clinical and scientific applications are discussed to provide a holistic understanding of the technique. This work aims to serve as a foundational resource for newcomers entering this dynamic and rapidly expanding field.

Multi-Parametric MRI for Evaluating Variations in Renal Structure, Function, and Endogenous Metabolites in an Animal Model With Acute Kidney Injury Induced by Ischemia Reperfusion

BACKGROUND

Ischemia reperfusion injury (IRI)-induced acute kidney injury (AKI) may occur after renal ischemic injury. There is a lack of an accurate and comprehensive detection technique for IRI-AKI.

PURPOSE

To longitudinally evaluate IRI-AKI in rats by renal structure, function, and metabolites using multi-parametric MRI (mpMRI).

STUDY TYPE

Prospective.

ANIMAL MODEL

Forty-eight rats undergoing IRI-AKI.

FIELD STRENGTH/SEQUENCE

7-T, T1 mapping, and arterial spin labeling (ASL): echo planar imaging (EPI) sequence; blood oxygen level-dependent (BOLD): gradient recalled echo (GRE) sequence; T2 mapping, quantitative magnetization transfer (qMT), and chemical exchange saturation transfer (CEST): rapid acquisition with relaxation enhancement (RARE) sequence.

ASSESSMENT

The mpMRI for IRI-AKI was conducted at 0 (control), 1, 3, 7, 14, and 28 days, all included eight rats. The longitudinal mpMRI signal of manually outlined cortex, outer stripe of the outer medulla (OSOM), inner stripe of the outer medulla, and medulla plus pelvis were calculated and compared, their diagnosis performance for IRI-AKI also been evaluated.

STATISTICAL TESTS

Pearson correlations analysis for correlation between mpMRI signal and renal injury, unpaired t-tests for comparing the signal changes, and receiver operating characteristics (ROC) analysis was used to identify most sensitive indicator of mpMRI. A P-value <0.05 was considered statistically significant.

RESULTS

Compared with control kidneys, the T1 and T2 values of the cortex and medulla in IRI kidneys increased and reached their highest values on day 14, and the kidneys also showed the most severe edema and segments blurred. The RBF in the cortex and OSOM showed a significant decline after day 3. The BOLD signal in the OSOM largest increased on day 28. The cortical PSR and the amine-CEST both decreased with IRI-AKI progression, and amine-CEST achieved the highest AUC for the diagnosis (0.899).

DATA CONCLUSION

Multi-parametric MRI may show comprehensive variations in IRI-AKI, and amine-CEST may exhibit the highest accuracy for diagnosis of IRI-AKI.

LEVEL OF EVIDENCE

1 TECHNICAL EFFICACY: Stage 2.

Anisotropic longitudinal water proton relaxation in white matter investigated ex vivo in porcine spinal cord with sample rotation

A variation of the longitudinal relaxation time T 1 in brain regions that differ in their main fiber direction has been occasionally reported, however, with inconsistent results. Goal of the present study was to clarify such inconsistencies, and the origin of potential T 1 orientation dependence, by applying direct sample rotation and comparing the results from different approaches to measure T 1 . A section of fixed porcine spinal cord white matter was investigated at 3 T with variation of the fiber-to-field angle θ FB . The experiments included one-dimensional inversion-recovery, MP2RAGE, and variable flip-angle T 1 measurements at 22 °C and 36 °C as well as magnetization-transfer (MT) and diffusion-weighted acquisitions. Depending on the technique, different degrees of T 1 anisotropy (between 2 and 10%) were observed as well as different dependencies on θ FB (monotonic variation or T 1 maximum at 30-40°). More pronounced anisotropy was obtained with techniques that are more sensitive to MT effects. Furthermore, strong correlations of θ FB -dependent MT saturation and T 1 were found. A comprehensive analysis based on the binary spin-bath model for MT revealed an interplay of several orientation-dependent parameters, including the transverse relaxation times of the macromolecular and the water pool as well as the longitudinal relaxation time of the macromolecular pool.

Multimodal imaging with magnetization transfer and diffusion tensor imaging reveals evidence of myelin damage in children and youth treated for a brain tumor

Background

The microstructural damage underlying compromise of white matter following treatment for pediatric brain tumors is unclear. We use multimodal imaging employing advanced diffusion tensor imaging (DTI) and magnetization transfer imaging (MTI) MRI methods to examine chronic microstructural damage to white matter in children and adolescents treated for pediatric brain tumor. Notably, MTI may be more sensitive to macromolecular content, including myelin, than DTI.

Methods

Fifty patients treated for brain tumors (18 treated with surgery ± chemotherapy and 32 treated with surgery followed by cranial-spinal radiation; time from diagnosis to scan ~6 years) and 45 matched healthy children completed both MTI and DTI scans. Voxelwise and region-of-interest approaches were employed to compare white matter microstructure metrics (magnetization transfer ratio (MTR); DTI- fractional anisotropy [FA], radial diffusivity [RD], axial diffusivity [AD], mean diffusivity [MD]) between patients and healthy controls.

Results

MTR was decreased across multiple white matter tracts in patients when compared to healthy children, P < .001. These differences were observed for both patients treated with radiation and those treated with only surgery, P < .001. We also found that children and adolescents treated for brain tumors exhibit decreased FA and increased RD/AD/MD compared to their healthy counterparts in several white matter regions, Ps < .02. Finally, we observed that MTR and DTI metrics were related to multiple white matter tracts in patients, Ps < .01, but not healthy control children.

Conclusions

Our findings provide evidence that the white matter damage observed in patients years after treatment of pediatric posterior fossa tumors, likely reflects myelin disruption.

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

PURPOSE

To explore efficient encoding schemes for quantitative magnetization transfer (qMT) imaging with few constraints on model parameters.

THEORY AND METHODS

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.

RESULTS

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.

CONCLUSION

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.

CEST and nuclear Overhauser enhancement imaging with deep learning-extrapolated semisolid magnetization transfer reference: Scan-rescan reproducibility and reliability studies

PURPOSE

To develop a novel MR physics-driven, deep-learning, extrapolated semisolid magnetization transfer reference (DeepEMR) framework to provide fast, reliable magnetization transfer contrast (MTC) and CEST signal estimations, and to determine the reproducibility and reliability of the estimates from the DeepEMR.

METHODS

A neural network was designed to predict a direct water saturation and MTC-dominated signal at a certain CEST frequency offset using a few high-frequency offset features in the Z-spectrum. The accuracy, scan-rescan reproducibility, and reliability of MTC, CEST, and relayed nuclear Overhauser enhancement (rNOE) signals estimated from the DeepEMR were evaluated on numerical phantoms and in heathy volunteers at 3 T. In addition, we applied the DeepEMR method to brain tumor patients and compared tissue contrast with other CEST calculation metrics.

RESULTS

The DeepEMR method demonstrated a high degree of accuracy in the estimation of reference MTC signals at ±3.5 ppm for APT and rNOE imaging, and computational efficiency (˜190-fold) compared with a conventional fitting approach. In addition, the DeepEMR method achieved high reproducibility and reliability (intraclass correlation coefficient = 0.97, intersubject coefficient of variation = 3.5%, and intrasubject coefficient of variation = 1.3%) of the estimation of MTC signals at ±3.5 ppm. In tumor patients, DeepEMR-based amide proton transfer images provided higher tumor contrast than a conventional MT ratio asymmetry image, particularly at higher B1 strengths (>1.5 μT), with a distinct delineation of the tumor core from normal tissue or peritumoral edema.

CONCLUSION

The DeepEMR approach is feasible for measuring clean APT and rNOE effects in longitudinal and cross-sectional studies with low scan-rescan variability.

Neuromelanin-sensitive MRI as a promising biomarker of catecholamine function

Disruptions to dopamine and noradrenergic neurotransmission are noted in several neurodegenerative and psychiatric disorders. Neuromelanin-sensitive (NM)-MRI offers a non-invasive approach to visualize and quantify the structural and functional integrity of the substantia nigra and locus coeruleus. This method may aid in the diagnosis and quantification of longitudinal changes of disease and could provide a stratification tool for predicting treatment success of pharmacological interventions targeting the dopaminergic and noradrenergic systems. Given the growing clinical interest in NM-MRI, understanding the contrast mechanisms that generate this signal is crucial for appropriate interpretation of NM-MRI outcomes and for the continued development of quantitative MRI biomarkers that assess disease severity and progression. To date, most studies associate NM-MRI measurements to the content of the neuromelanin pigment and/or density of neuromelanin-containing neurons, while recent studies suggest that the main source of the NM-MRI contrast is not the presence of neuromelanin but the high-water content in the dopaminergic and noradrenergic neurons. In this review, we consider the biological and physical basis for the NM-MRI contrast and discuss a wide range of interpretations of NM-MRI. We describe different acquisition and image processing approaches and discuss how these methods could be improved and standardized to facilitate large-scale multisite studies and translation into clinical use. We review the potential clinical applications in neurological and psychiatric disorders and the promise of NM-MRI as a biomarker of disease, and finally, we discuss the current limitations of NM-MRI that need to be addressed before this technique can be utilized as a biomarker and translated into clinical practice and offer suggestions for future research.

Is pulsed saturation transfer sufficient for differentiating radiation necrosis from tumor progression in brain metastases?

Background

Stereotactic radiosurgery (SRS) for the treatment of brain metastases delivers a high dose of radiation with excellent local control but comes with the risk of radiation necrosis (RN), which can be difficult to distinguish from tumor progression (TP). Magnetization transfer (MT) and chemical exchange saturation transfer (CEST) are promising techniques for distinguishing RN from TP in brain metastases. Previous studies used a 2D continuous-wave (ie, block radiofrequency [RF] saturation) MT/CEST approach. The purpose of this study is to investigate a 3D pulsed saturation MT/CEST approach with perfusion MRI for distinguishing RN from TP in brain metastases.

Methods

The study included 73 patients scanned with MT/CEST MRI previously treated with SRS or fractionated SRS who developed enhancing lesions with uncertain diagnoses of RN or TP. Perfusion MRI was acquired in 49 of 73 patients. Clinical outcomes were determined by at least 6 months of follow-up or via pathologic confirmation (in 20% of the lesions).

Results

Univariable logistic regression resulted in significant variables of the quantitative MT parameter 1/(RA·T2A), with 5.9 ± 2.7 for RN and 6.5 ± 2.9 for TP. The highest AUC of 75% was obtained using a multivariable logistic regression model for MT/CEST parameters, which included the CEST parameters of AREXAmide,0.625µT (P = .013), AREXNOE,0.625µT (P = .008), 1/(RA·T2A) (P = .004), and T1 (P = .004). The perfusion rCBV parameter did not reach significance.

Conclusions

Pulsed saturation transfer was sufficient for achieving a multivariable AUC of 75% for differentiating between RN and TP in brain metastases, but had lower AUCs compared to previous studies that used a block RF approach.

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.

MR myelin imaging in multiple sclerosis: A scoping review

The inability of disease-modifying therapies to stop the progression of multiple sclerosis (MS), has led to the development of a new therapeutic strategy focussing on myelin repair. While conventional MRI lacks sensitivity for quantifying myelin damage, advanced MRI techniques are proving effective. The development of targeted therapeutics requires histological validation of myelin imaging results, alongside the crucial task of establishing correlations between myelin imaging results and clinical assessments, so that the effectiveness of therapeutic interventions can be evaluated. The aims of this scoping review were to identify myelin imaging methods - some of which have been histologically validated, and to determine how these approaches correlate with clinical assessments of people with MS (pwMS), thus allowing for effective therapeutic evaluation. A search of two databases was undertaken for publications relating to studies on adults MS using either MRI/MR-histology of the MS brain in the range 1990-to-2022. The myelin imaging methods specified were relaxometry, magnetization transfer, and quantitative susceptibility. Relaxometry was used most frequently, with myelin water fraction (MWF) being the primary metric. Studies conducted on tissue from various regions of the brain showed that MWF was significantly lower in pwMS than in healthy controls. Magnetization transfer ratio indicated that the macromolecular content of lesions was lower than that of normal-appearing tissue. Higher magnetic susceptibility of lesions were indicative of myelin breakdown and iron accumulation. Several myelin imaging metrics were correlated with disability, disease severity and duration. Many studies showed a good correlation between myelin measured histologically and by MR myelin imaging techniques.

B1 inhomogeneity-corrected T1 mapping and quantitative magnetization transfer imaging via simultaneously estimating Bloch-Siegert shift and magnetization transfer effects

PURPOSE

To introduce a method of inducing Bloch-Siegert shift and magnetization Transfer Simultaneously (BTS) and demonstrate its utilization for measuring binary spin-bath model parameters free pool spin-lattice relaxation (T 1 F $$ {T}_1^{\mathrm{F}} $$ ), macromolecular fraction (f $$ f $$ ), magnetization exchange rate (k F $$ {k}_{\mathrm{F}} $$ ) and local transmit field (B 1 + $$ {B}_1^{+} $$ ).

THEORY AND METHODS

Bloch-Siegert shift and magnetization transfer is simultaneously induced through the application of off-resonance irradiation in between excitation and acquisition of an RF-spoiled gradient-echo scheme. Applying the binary spin-bath model, an analytical signal equation is derived and verified through Bloch simulations. Monte Carlo simulations were performed to analyze the method's performance. The estimation of the binary spin-bath parameters withB 1 + $$ {B}_1^{+} $$ compensation was further investigated through experiments, both ex vivo and in vivo.

RESULTS

Comparing BTS with existing methods, simulations showed that existing methods can significantly biasT 1 $$ {T}_1 $$ estimation when not accounting for transmitB 1 $$ {B}_1 $$ heterogeneity and MT effects that are present. Phantom experiments further showed that the degree of this bias increases with increasing macromolecular proton fraction. Multi-parameter fit results from an in vivo brain study generated values in agreement with previous literature. Based on these studies, we confirmed that BTS is a robust method for estimating the binary spin-bath parameters in macromolecule-rich environments, even in the presence ofB 1 + $$ {B}_1^{+} $$ inhomogeneity.

CONCLUSION

A method of estimating Bloch-Siegert shift and magnetization transfer effect has been developed and validated. Both simulations and experiments confirmed that BTS can estimate spin-bath parameters (T 1 F $$ {T}_1^{\mathrm{F}} $$ ,f $$ f $$ ,k F $$ {k}_{\mathrm{F}} $$ ) that are free fromB 1 + $$ {B}_1^{+} $$ bias.

Design and development of a novel flexible ultra-short echo time (FUSE) sequence

PURPOSE

To present the validation of a new Flexible Ultra-Short Echo time (FUSE) pulse sequence using a short-T2 phantom.

METHODS

FUSE was developed to include a range of RF excitation pulses, trajectories, dimensionalities, and long-T2 suppression techniques, enabling real-time interchangeability of acquisition parameters. Additionally, we developed an improved 3D deblurring algorithm to correct for off-resonance artifacts. Several experiments were conducted to validate the efficacy of FUSE, by comparing different approaches for off-resonance artifact correction, variations in RF pulse and trajectory combinations, and long-T2 suppression techniques. All scans were performed on a 3 T system using an in-house short-T2 phantom. The evaluation of results included qualitative comparisons and quantitative assessments of the SNR and contrast-to-noise ratio.

RESULTS

Using the capabilities of FUSE, we demonstrated that we could combine a shorter readout duration with our improved deblurring algorithm to effectively reduce off-resonance artifacts. Among the different RF and trajectory combinations, the spiral trajectory with the regular half-inc pulse achieves the highest SNRs. The dual-echo subtraction technique delivers better short-T2 contrast and superior suppression of water and agar signals, whereas the off-resonance saturation method successfully suppresses water and lipid signals simultaneously.

CONCLUSION

In this work, we have validated the use of our new FUSE sequence using a short T2 phantom, demonstrating that multiple UTE acquisitions can be achieved within a single sequence. This new sequence may be useful for acquiring improved UTE images and the development of UTE imaging protocols.

Optimization of acquisition parameters for cortical inhomogeneous magnetization transfer (ihMT) imaging using a rapid gradient echo readout

PURPOSE

Imaging biomarkers with increased myelin specificity are needed to better understand the complex progression of neurological disorders. Inhomogeneous magnetization transfer (ihMT) imaging is an emergent technique that has a high degree of specificity for myelin content but suffers from low signal to-noise ratio (SNR). This study used simulations to determine optimal sequence parameters for ihMT imaging for use in high-resolution cortical mapping.

METHODS

MT-weighted cortical image intensity and ihMT SNR were simulated using modified Bloch equations for a range of sequence parameters. The acquisition time was limited to 4.5 min/volume. A custom MT-weighted RAGE sequence with center-out k-space encoding was used to enhance SNR at 3 T. Pulsed MT imaging was studied over a range of saturation parameters, and the impact of the turbo factor on the effective ihMT resolution was investigated. 1 mm isotropic ihMTsat maps were generated in 25 healthy adults.

RESULTS

Greater SNR was observed for larger number of bursts consisting of 6-8 saturation pulses each, combined with a high readout turbo factor. However, that protocol suffered from a point spread function that was more than twice the nominal resolution. For high-resolution cortical imaging, we selected a protocol with a higher effective resolution at the cost of a lower SNR. We present the first group-average ihMTsat whole-brain map at 1 mm isotropic resolution.

CONCLUSION

This study presents the impact of saturation and excitation parameters on ihMTsat SNR and resolution. We demonstrate the feasibility of high-resolution cortical myelin imaging using ihMTsat in less than 20 min.

Bloch simulator-driven deep recurrent neural network for magnetization transfer contrast MR fingerprinting and CEST imaging

PURPOSE

To develop a unified deep-learning framework by combining an ultrafast Bloch simulator and a semisolid macromolecular magnetization transfer contrast (MTC) MR fingerprinting (MRF) reconstruction for estimation of MTC effects.

METHODS

The Bloch simulator and MRF reconstruction architectures were designed with recurrent neural networks and convolutional neural networks, evaluated with numerical phantoms with known ground truths and cross-linked bovine serum albumin phantoms, and demonstrated in the brain of healthy volunteers at 3 T. In addition, the inherent magnetization-transfer ratio asymmetry effect was evaluated in MTC-MRF, CEST, and relayed nuclear Overhauser enhancement imaging. A test-retest study was performed to evaluate the repeatability of MTC parameters, CEST, and relayed nuclear Overhauser enhancement signals estimated by the unified deep-learning framework.

RESULTS

Compared with a conventional Bloch simulation, the deep Bloch simulator for generation of the MTC-MRF dictionary or a training data set reduced the computation time by 181-fold, without compromising MRF profile accuracy. The recurrent neural network-based MRF reconstruction outperformed existing methods in terms of reconstruction accuracy and noise robustness. Using the proposed MTC-MRF framework for tissue-parameter quantification, the test-retest study showed a high degree of repeatability in which the coefficients of variance were less than 7% for all tissue parameters.

CONCLUSION

Bloch simulator-driven, deep-learning MTC-MRF can provide robust and repeatable multiple-tissue parameter quantification in a clinically feasible scan time on a 3T scanner.

Detecting macromolecular differences of the CSF in low disability multiple sclerosis using quantitative MT MRI at 3T

Background

Imaging investigation of cerebrospinal fluid (CSF) in multiple sclerosis (MS) is understudied. Development of noninvasive methods to detect pathological CSF changes would have a profound effect on MS diagnosis and would offer insight into MS pathophysiology and mechanisms of neurological impairment.

Objective

We propose magnetization transfer (MT) MRI as a tool to detect macromolecular changes in spinal CSF.

Methods

MT and quantitative MT (qMT) data were acquired in the cervical region in 27 people with relapsing-remitting multiple sclerosis (pwRRMS) and 38 age and sex-matched healthy controls (HCs). MT ratio (MTR), the B1, B0, and R1 corrected qMT-derived pool size ratio (PSR) were quantified in the spinal cord and CSF of each group.

Results

Both CSF MTR and CSF qMT-derived PSR were significantly increased in pwRRMS compared to HC (p = 0.027 and p = 0.020, respectively). CSF PSR of pwRRMS was correlated to Expanded Disability Status Scale Scores (p = 0.045, R = 0.352).

Conclusion

Our findings demonstrate increased CSF macromolecular content in pwRRMS and link CSF macromolecular content with clinical impairment. This highlights the potential role of CSF in processing products of demyelination.

Longitudinal multiparametric MRI of traumatic spinal cord injury in animal models

Multi-parametric MRI (mpMRI) technology enables non-invasive and quantitative assessments of the structural, molecular, and functional characteristics of various neurological diseases. Despite the recognized importance of studying spinal cord pathology, mpMRI applications in spinal cord research have been somewhat limited, partly due to technical challenges associated with spine imaging. However, advances in imaging techniques and improved image quality now allow longitudinal investigations of a comprehensive range of spinal cord pathological features by exploiting different endogenous MRI contrasts. This review summarizes the use of mpMRI techniques including blood oxygenation level-dependent (BOLD) functional MRI (fMRI), diffusion tensor imaging (DTI), quantitative magnetization transfer (qMT), and chemical exchange saturation transfer (CEST) MRI in monitoring different aspects of spinal cord pathology. These aspects include cyst formation and axonal disruption, demyelination and remyelination, changes in the excitability of spinal grey matter and the integrity of intrinsic functional circuits, and non-specific molecular changes associated with secondary injury and neuroinflammation. These approaches are illustrated with reference to a nonhuman primate (NHP) model of traumatic cervical spinal cord injuries (SCI). We highlight the benefits of using NHP SCI models to guide future studies of human spinal cord pathology, and demonstrate how mpMRI can capture distinctive features of spinal cord pathology that were previously inaccessible. Furthermore, the development of mechanism-based MRI biomarkers from mpMRI studies can provide clinically useful imaging indices for understanding the mechanisms by which injured spinal cords progress and repair. These biomarkers can assist in the diagnosis, prognosis, and evaluation of therapies for SCI patients, potentially leading to improved outcomes.

Severity of polycystic kidney disease revealed by multiparametric MRI

PURPOSE

We aimed to compare multiple MRI parameters, including relaxation rates (R 1 $$ {R}_1 $$ ,R 2 $$ {R}_2 $$ , andR 1 ρ $$ {R}_{1\rho } $$ ), ADC from diffusion weighted imaging, pool size ratio (PSR) from quantitative magnetization transfer, and measures of exchange from spin-lock imaging (S ρ $$ {S}_{\rho } $$ ), for assessing and predicting the severity of polycystic kidney disease (PKD) over time.

METHODS

Pcy/Pcy mice with CD1 strain, a mouse model of autosomal dominant PKD, were imaged at 5, 9, and 26 wk of age using a 7T MRI system. Twelve-week normal CD1 mice were used as controls. Post-mortem paraffin tissue sections were stained using hematoxylin and eosin and picrosirius red to identify histological changes.

RESULTS

Histology detected segmental cyst formation in the early stage (week 5) and progression of PKD over time in Pcy kidneys. InT 2 $$ {T}_2 $$ -weighted images, small cysts appeared locally in cystic kidneys in week 5 and gradually extended to the whole cortex and outer stripe of outer medulla region from week 5 to week 26. Regional PSR,R 1 $$ {R}_1 $$ ,R 2 $$ {R}_2 $$ , andR 1 ρ $$ {R}_{1\rho } $$ decreased consistently over time compared to normal kidneys, with significant changes detected in week 5. Among all the MRI measures,R 2 $$ {R}_2 $$ andR 1 ρ $$ {R}_{1\rho } $$ allow highest detectability to PKD, while PSR andR 1 $$ {R}_1 $$ have highest correlation with pathological indices of PKD. Using optimum MRI parameters as regressors, multiple linear regression provides reliable prediction of PKD progression.

CONCLUSION

R 2 $$ {R}_2 $$ ,R 1 $$ {R}_1 $$ , and PSR are sensitive indicators of the presence of PKD. Multiparametric MRI allows a comprehensive analysis of renal changes caused by cyst formation and expansion.

Only-train-once MR fingerprinting for B0 and B1 inhomogeneity correction in quantitative magnetization-transfer contrast

PURPOSE

To develop a fast, deep-learning approach for quantitative magnetization-transfer contrast (MTC)-MR fingerprinting (MRF) that simultaneously estimates multiple tissue parameters and corrects the effects of B0 and B1 variations.

METHODS

An only-train-once recurrent neural network was designed to perform the fast tissue-parameter quantification for a large range of different MRF acquisition schedules. It enabled a dynamic scan-wise linear calibration of the scan parameters using the measured B0 and B1 maps, which allowed accurate, multiple-tissue parameter mapping. MRF images were acquired from 8 healthy volunteers at 3 T. Estimated parameter maps from the MRF images were used to synthesize the MTC reference signal (Zref ) through Bloch equations at multiple saturation power levels.

RESULTS

The B0 and B1 errors in MR fingerprints, if not corrected, would impair the tissue quantification and subsequently corrupt the synthesized MTC reference images. Bloch equation-based numerical phantom studies and synthetic MRI analysis demonstrated that the proposed approach could correctly estimate water and semisolid macromolecule parameters, even with severe B0 and B1 inhomogeneities.

CONCLUSION

The only-train-once deep-learning framework can improve the reconstruction accuracy of brain-tissue parameter maps and be further combined with any conventional MRF or CEST-MRF method.

MR fingerprinting for semisolid magnetization transfer and chemical exchange saturation transfer quantification

Chemical exchange saturation transfer (CEST) MRI has positioned itself as a promising contrast mechanism, capable of providing molecular information at sufficient resolution and amplified sensitivity. However, it has not yet become a routinely employed clinical technique, due to a variety of confounding factors affecting its contrast-weighted image interpretation and the inherently long scan time. CEST MR fingerprinting (MRF) is a novel approach for addressing these challenges, allowing simultaneous quantitation of several proton exchange parameters using rapid acquisition schemes. Recently, a number of deep-learning algorithms have been developed to further boost the performance and speed of CEST and semi-solid macromolecule magnetization transfer (MT) MRF. This review article describes the fundamental theory behind semisolid MT/CEST-MRF and its main applications. It then details supervised and unsupervised learning approaches for MRF image reconstruction and describes artificial intelligence (AI)-based pipelines for protocol optimization. Finally, practical considerations are discussed, and future perspectives are given, accompanied by basic demonstration code and data.

The relayed nuclear Overhauser effect in magnetization transfer and chemical exchange saturation transfer MRI

Magnetic resonance (MR) is a powerful technique for noninvasively probing molecular species in vivo but suffers from low signal sensitivity. Saturation transfer (ST) MRI approaches, including chemical exchange saturation transfer (CEST) and conventional magnetization transfer contrast (MTC), allow imaging of low-concentration molecular components with enhanced sensitivity using indirect detection via the abundant water proton pool. Several recent studies have shown the utility of chemical exchange relayed nuclear Overhauser effect (rNOE) contrast originating from nonexchangeable carbon-bound protons in mobile macromolecules in solution. In this review, we describe the mechanisms leading to the occurrence of rNOE-based signals in the water saturation spectrum (Z-spectrum), including those from mobile and immobile molecular sources and from molecular binding. While it is becoming clear that MTC is mainly an rNOE-based signal, we continue to use the classical MTC nomenclature to separate it from the rNOE signals of mobile macromolecules, which we will refer to as rNOEs. Some emerging applications of the use of rNOEs for probing macromolecular solution components such as proteins and carbohydrates in vivo or studying the binding of small substrates are discussed.

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

PURPOSE

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.

METHODS

The proposed framework consists of SPGR sequences prepared with simultaneous dual-offset frequency-saturation pulses to cancel out dipolar order and associated relaxation (T_1D ) 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], T_2,f , T_2,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 .

RESULTS

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.

CONCLUSION

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.

Feasibility of in vivo multi-parametric quantitative magnetic resonance imaging of the healthy sciatic nerve with a unified signal readout protocol

Magnetic resonance neurography (MRN) has been used successfully over the years to investigate the peripheral nervous system (PNS) because it allows early detection and precise localisation of neural tissue damage. However, studies demonstrating the feasibility of combining MRN with multi-parametric quantitative magnetic resonance imaging (qMRI) methods, which provide more specific information related to nerve tissue composition and microstructural organisation, can be invaluable. The translation of emerging qMRI methods previously validated in the central nervous system to the PNS offers real potential to characterise in patients in vivo the underlying pathophysiological mechanisms involved in a plethora of conditions of the PNS. The aim of this study was to assess the feasibility of combining MRN with qMRI to measure diffusion, magnetisation transfer and relaxation properties of the healthy sciatic nerve in vivo using a unified signal readout protocol. The reproducibility of the multi-parametric qMRI protocol as well as normative qMRI measures in the healthy sciatic nerve are reported. The findings presented herein pave the way to the practical implementation of joint MRN-qMRI in future studies of pathological conditions affecting the PNS.