B1-MRF: Large dynamic range MRF-based absolute B 1 + mapping in the human body at 7T.

PURPOSE: This study aims to map the transmit magnetic field ( B 1 + $$ {B}_1^{+} $$ ) in the human body at 7T using MR fingerprinting (MRF), with a focus on achieving high accuracy and precision across a large dynamic range, particularly at low flip angles (FAs). METHODS: A FLASH-based MRF sequence (B1-MRF) with high B 1 + $$ {B}_1^{+} $$ sensitivity was developed. Phantom and in vivo abdominal imaging were performed at 7T, and the results were compared with established reference methods, including a slow but precise preparation-based method (PEX), saturated TurboFLASH (satTFL), actual flip angle imaging (AFI) and Bloch-Siegert shift (BSS). RESULTS: The MRF signal curve was highly sensitive to B 1 + $$ {B}_1^{+} $$ , while T1 sensitivity was comparatively low. The phantom experiment showed good agreement of B 1 + $$ {B}_1^{+} $$ to PEX for a T1 range of 204-1691 ms evaluated at FAs from 0° to 70°. Compared to the references, a dynamic range increase larger than a factor of two was determined experimentally. In vivo liver scans showed a strong correlation between B1-MRF, satTFL, and RPE-AFI in a low body mass index (BMI) subject (18.1 kg/m2). However, in larger BMI subjects (≥25.5 kg/m2), inconsistencies were observed in low B 1 + $$ {B}_1^{+} $$ regions for satTFL and RPE-AFI, while B1-MRF still provided consistent results in these regions. CONCLUSION: B1-MRF provides accurate and precise B 1 + $$ {B}_1^{+} $$ maps over a wide range of FAs, surpassing the capabilities of existing methods in the FA range < 60°. Its enhanced sensitivity at low FAs is advantageous for various applications requiring precise B 1 + $$ {B}_1^{+} $$ estimates, potentially advancing the frontiers of ultra-high field (UHF) body imaging at 7T and beyond.

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 Cramer-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.

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 R 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's R 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 in R 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.

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.

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 non-linear 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.

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.

Unconstrained quantitative magnetization transfer imaging: disentangling T1 of the free and semi-solid spin pools.

Since the inception of magnetization transfer (MT) imaging, it has been widely assumed that Henkelman's two spin pools have similar longitudinal relaxation times, which motivated many researchers to constrain them to each other. However, several recent publications reported a T1s of the semi-solid spin pool that is much shorter than T1f of the free pool. While these studies tailored experiments for robust proofs-of-concept, we here aim to quantify the disentangled relaxation processes on a voxel-by-voxel basis in a clinical imaging setting, i.e., with an effective resolution of 1.24mm isotropic and full brain coverage in 12min. To this end, we optimized a hybrid-state pulse sequence for mapping the parameters of an unconstrained MT model. We scanned four people with relapsing-remitting multiple sclerosis (MS) and four healthy controls with this pulse sequence and estimated T1f≈1.84s and T1s≈0.34s in healthy white matter. Our results confirm the reports that T1s≪T1f and we argue that this finding identifies MT as an inherent driver of longitudinal relaxation in brain tissue. Moreover, we estimated a fractional size of the semi-solid spin pool of m0s≈0.212, which is larger than previously assumed. An analysis of T1f in normal-appearing white matter revealed statistically significant differences between individuals with MS and controls.

Minimization of eddy current artifacts in sequences with periodic dynamics.

PURPOSE: To minimize eddy current artifacts in periodic pulse sequences with balanced gradient moments as, for example, used for quantitative MRI. THEORY AND METHODS: Eddy current artifacts in balanced sequences result from large jumps in k-space. In quantitative MRI, one often samples some spin dynamics repeatedly while acquiring different parts of k-space. We swap individual k-space lines between different repetitions in order to minimize jumps in temporal succession without changing the overall trajectory. This reordering can be formulated as a traveling salesman problem and we tackle the discrete optimization with a simulated annealing algorithm. RESULTS: Compared to the default ordering, we observe a substantial reduction of artifacts in the reconstructed images and the derived quantitative parameter maps. Comparing two variants of our algorithm, one that resembles the pairing approach originally proposed by Bieri et al., and one that minimizes all k-space jumps equally, we observe slightly lower artifact levels in the latter. CONCLUSION: The proposed reordering scheme effectively reduces eddy current artifacts in sequences with balanced gradient moments. In contrast to previous approaches, we capitalize on the periodicity of the sampled signal dynamics, enabling both efficient k-space sampling and minimizing artifacts caused by eddy currents.

Cramér-Rao Bound Optimized Subspace Reconstruction in Quantitative MRI.

We extend the traditional framework for estimating subspace bases 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. 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. Compared to the traditional SVD approach, the proposed method better preserves the CRB across all biophysical parameters with negligible 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, permitting the use of smaller basis sizes in subspace reconstruction and offering significant computational savings.

Cramér-Rao bound-informed training of neural networks for quantitative MRI.

PURPOSE: To improve the performance of neural networks for parameter estimation in quantitative MRI, in particular when the noise propagation varies throughout the space of biophysical parameters. THEORY AND METHODS: A theoretically well-founded loss function is proposed that normalizes the squared error of each estimate with respective Cramér-Rao bound (CRB)-a theoretical lower bound for the variance of an unbiased estimator. This avoids a dominance of hard-to-estimate parameters and areas in parameter space, which are often of little interest. The normalization with corresponding CRB balances the large errors of fundamentally more noisy estimates and the small errors of fundamentally less noisy estimates, allowing the network to better learn to estimate the latter. Further, proposed loss function provides an absolute evaluation metric for performance: A network has an average loss of 1 if it is a maximally efficient unbiased estimator, which can be considered the ideal performance. The performance gain with proposed loss function is demonstrated at the example of an eight-parameter magnetization transfer model that is fitted to phantom and in vivo data. RESULTS: Networks trained with proposed loss function perform close to optimal, that is, their loss converges to approximately 1, and their performance is superior to networks trained with the standard mean-squared error (MSE). The proposed loss function reduces the bias of the estimates compared to the MSE loss, and improves the match of the noise variance to the CRB. This performance gain translates to in vivo maps that align better with the literature. CONCLUSION: Normalizing the squared error with the CRB during the training of neural networks improves their performance in estimating biophysical parameters.

Generalized Bloch model: A theory for pulsed magnetization transfer.

PURPOSE: The paper introduces a classical model to describe the dynamics of large spin-1/2 ensembles associated with nuclei bound in large molecule structures, commonly referred to as the semi-solid spin pool, and their magnetization transfer (MT) to spins of nuclei in water. THEORY AND METHODS: Like quantum-mechanical descriptions of spin dynamics and like the original Bloch equations, but unlike existing MT models, the proposed model is based on the algebra of angular momentum in the sense that it explicitly models the rotations induced by radiofrequency (RF) pulses. It generalizes the original Bloch model to non-exponential decays, which are, for example, observed for semi-solid spin pools. The combination of rotations with non-exponential decays is facilitated by describing the latter as Green's functions, comprised in an integro-differential equation. RESULTS: Our model describes the data of an inversion-recovery magnetization-transfer experiment with varying durations of the inversion pulse substantially better than established models. We made this observation for all measured data, but in particular for pulse durations smaller than 300 µs. Furthermore, we provide a linear approximation of the generalized Bloch model that reduces the simulation time by approximately a factor 15,000, enabling simulation of the spin dynamics caused by a rectangular RF-pulse in roughly 2 µs. CONCLUSION: The proposed theory unifies the original Bloch model, Henkelman's steady-state theory for MT, and the commonly assumed rotation induced by hard pulses (i.e., strong and infinitesimally short applications of RF-fields) and describes experimental data better than previous models.

Phase-contrast acceleration mapping with synchronized encoding.

PURPOSE: To develop a phase-contrast (PC) -based method for direct and unbiased quantification of the acceleration vector field by synchronization of the spatial and acceleration encoding time points. The proposed method explicitly aims at in-vitro applications, requiring high measurement accuracy, as well as the validation of clinically relevant acceleration-encoded sequences. METHODS: A velocity-encoded sequence with synchronized encoding (SYNC SPI) was modified to allow direct acceleration mapping by replacing the bipolar encoding gradients with tripolar gradient waveforms. The proposed method was validated in two in-vitro flow cases: a rotation and a stenosis phantom. The thereby obtained velocity and acceleration vector fields were quantitatively compared to those acquired with conventional PC methods, as well as to theoretical data. RESULTS: The rotation phantom study revealed a systematic bias of the conventional PC acceleration mapping method that resulted in an average pixel-wise relative angle between the measured and theoretical vector field of (7.8 ± 3.2)°, which was reduced to (-0.4 ± 2.7)° for the proposed SYNC SPI method. Furthermore, flow features in the stenosis phantom were displaced by up to 10 mm in the conventional PC data compared with the acceleration-encoded SYNC SPI data. CONCLUSIONS: This work successfully demonstrates a highly accurate method for direct acceleration mapping. It thus complements the existing velocity-encoded SYNC SPI method to enable the direct and unbiased quantification of both the velocity and acceleration vector field for in vitro studies. Hence, this method can be used for the validation of conventional acceleration-encoded PC methods applicable in-vivo.

3D sodium (23 Na) magnetic resonance fingerprinting for time-efficient relaxometric mapping.

PURPOSE: To develop a framework for 3D sodium (23 Na) MR fingerprinting (MRF), based on irreducible spherical tensor operators with tailored flip angle (FA) pattern and time-efficient data acquisition for simultaneous quantification of T1 , T2l∗ , T2s∗ , and T2∗ in addition to ΔB0 . METHODS: 23 Na-MRF was implemented in a 3D sequence and irreducible spherical tensor operators were exploited in the simulations. Furthermore, the Cramér Rao lower bound was used to optimize the flip angle pattern. A combination of single and double echo readouts was implemented to increase the readout efficiency. A study was conducted to compare results in a multicompartment phantom acquired with MRF and reference methods. Finally, the relaxation times in the human brain were measured in four healthy volunteers. RESULTS: Phantom experiments revealed a mean difference of 1.0% between relaxation times acquired with MRF and results determined with the reference methods. Simultaneous quantification of the longitudinal and transverse relaxation times in the human brain was possible within 32 min using 3D 23 Na-MRF with a nominal resolution of (5 mm)3 . In vivo measurements in four volunteers yielded average relaxation times of: T1,brain = (35.0 ± 3.2) ms, T2l,brain∗ = (29.3 ± 3.8) ms and T2s,brain∗ = (5.5 ± 1.3) ms in brain tissue, whereas T1,CSF = (61.9 ± 2.8) ms and T2,CSF∗ = (46.3 ± 4.5) ms was found in cerebrospinal fluid. CONCLUSION: The feasibility of in vivo 3D relaxometric sodium mapping within roughly ½ h is demonstrated using MRF in the human brain, moving sodium relaxometric mapping toward clinically relevant measurement times.

Bayesian uncertainty quantification for magnetic resonance fingerprinting.

Magnetic Resonance Fingerprinting (MRF) is a promising technique for fast quantitative imaging of human tissue. In general, MRF is based on a sequence of highly undersampled MR images which are analyzed with a pre-computed dictionary. MRF provides valuable diagnostic parameters such as theT1andT2MR relaxation times. However, uncertainty characterization of dictionary-based MRF estimates forT1andT2has not been achieved so far, which makes it challenging to assess if observed differences in these estimates are significant and may indicate pathological changes of the underlying tissue. We propose a Bayesian approach for the uncertainty quantification of dictionary-based MRF which leads to probability distributions forT1andT2in every voxel. The distributions can be used to make probability statements about the relaxation times, and to assign uncertainties to their dictionary-based MRF estimates. All uncertainty calculations are based on the pre-computed dictionary and the observed sequence of undersampled MR images, and they can be calculated in short time. The approach is explored by analyzing MRF measurements of a phantom consisting of several tubes across which MR relaxation times are constant. The proposed uncertainty quantification is quantitatively consistent with the observed within-tube variability of estimated relaxation times. Furthermore, calculated uncertainties are shown to characterize well observed differences between the MRF estimates and the results obtained from high-accurate reference measurements. These findings indicate that a reliable uncertainty quantification is achieved. We also present results for simulated MRF data and an uncertainty quantification for anin vivoMRF measurement. MATLAB®source code implementing the proposed approach is made available.

The impact of 4D flow displacement artifacts on wall shear stress estimation.

PURPOSE: To investigate the amplitude and spatial distribution of errors in wall shear stress (WSS) values derived from 4D flow measurements caused by displacement artifacts intrinsic to the 4D flow acquisition. METHODS: Phase-contrast MRI velocimetry was performed in a model of a stenotic aorta using two different timing schemes, both of which are commonly applied in vivo but differ in their resulting displacement artifacts. Whereas one scheme is optimized to minimize the duration of the encoding gradients (herein called FAST), the other aims to specifically minimize displacement artifacts by synchronizing all three spatial-encoding time points (called ECHO). WSS estimates were calculated and compared to unbiased WSS values obtained by a 5-hour single-point imaging acquisition. In addition, MRI simulations based on computational fluid dynamics data were carried out to investigate the impact of gradient timings corresponding to different spatial resolutions. RESULTS: 4D flow displacement artifacts were found to have an impact on the quantified WSS peak values, spatial location, and overall WSS pattern. FAST leads to the underestimation of local WSS values in the phantom arch by up to 90%. Moreover, the corresponding WSS estimates depend on the image orientation. This effect was avoided using ECHO, which, however, results in biased WSS values within the stenosis, yielding an underestimation of peak WSS by up to 17%. Computational fluid dynamics-based simulation results show that the bias in WSS due to displacement artifacts increases with increasing spatial resolution, thus counteracting the resolution benefit for WSS due to reduced partial volume effects and segmentation errors. CONCLUSIONS: 4D flow displacement artifacts can significantly impact the WSS estimates and depend on the timing scheme as well as potentially the image orientation. Whereas FAST might allow correct WSS estimation for lower resolutions, ECHO is recommended especially when spatial resolutions of 1 mm and smaller are used. Users need to be aware of this nonnegligible effect, particularly when conducting inter-site studies or studies between vendors. The timing scheme should thus be explicitly mentioned in publications.

Development of a rotation phantom for phase contrast MRI sequence validation and quality control.

PURPOSE: To develop a reliable, consistent, and reproducible reference phantom for error quantification of phase-contrast MRI so it can be used for validation and quality control. METHODS: An air-driven rotation phantom consisting of a steadily rotating cylinder surrounded by a static ring both filled with agarose gel was developed. Rotational speed was measured and controlled in real time using an optical counter and a closed-loop controller. Consistency of the phantom was assessed by recording variations in rotational speed. The phantom was imaged with 2D phase-contrast MRI, and the velocity at each point was compared with analytically predicted velocity. Additionally, to examine reproducibility, the phantom was run with the same rotational speed on 2 different days and imaged using the same phase-contrast MRI protocol. RESULTS: The rotation phantom provided consistent rotational speed with 2 revolutions per minute SD from the set value for 20 min. Comparison between predicted and measured velocities demonstrated excellent agreement (intraclass correlation coefficient of 0.99). The RMS error in velocity components were less than 1% of maximum value. The scan-rescan experiment showed that the phantom can reproduce the same velocity distributions (intraclass correlation coefficient of 0.99) using the same rotational speed and MRI settings. CONCLUSION: The developed rotation phantom provided well-defined and reproducible linear velocity distributions, which can be used for systematic and quantitative error analysis of phase-contrast MRI for a range of known velocities.

Sodium relaxometry using 23 Na MR fingerprinting: A proof of concept.

PURPOSE: To evaluate the feasibility of 23 Na MR fingerprinting (MRF) for simultaneous quantification of T1 , T2l∗ , T2s∗ , T2∗ in addition to ΔB0 . METHODS: A framework for sodium relaxometry using MRF at 7T was developed, allowing simultaneous measurement of relaxation times and inhomogeneities in the static field. The technique distinguishes between bi- and monoexponential transverse relaxation and was validated in simulations with respect to the ground truth. In phantom measurements, a resolution of 2 × 2 × 12 mm3 was achieved within 1 h acquisition time, and the resulting parameter maps were compared to results from reference methods. Relaxation times in five healthy volunteers were measured with a resolution of 4 × 4 × 12 mm3 . RESULTS: Phantom experiments revealed an agreement between the relaxation times obtained via 23 Na-MRF and the reference methods. In white matter, a longitudinal relaxation constant of T1 = 38.9 ± 4.8 ms was found, while values of T2l∗ = 29.2 ± 4.9 ms and T2s∗ = 4.7 ± 1.2 ms were found for the long and short component of the transverse relaxation. In cerebrospinal fluid, T1 was 67.7 ± 6.3 ms and T2∗ = 41.5 ± 3.4 ms. CONCLUSION: This work demonstrates the feasibility of 23 Na-MRF for relaxometry in sodium MRI in both phantom and in vivo studies. Simultaneous quantification of T1 , T2l∗ , T2s∗ , T2∗ and ΔB0 was possible within a 1 h measurement time.

Velocity encoding and velocity compensation for multi-spoke RF excitation.

PURPOSE: To investigate velocity encoded and velocity compensated variants of multi-spoke RF pulses that can be used for flip-angle homogenization at ultra-high fields (UHF). Attention is paid to the velocity encoding for each individual spoke pulse and to displacement artifacts that arise in Fourier transform imaging in the presence of flow. THEORY AND METHODS: A gradient waveform design for multi-spoke excitation providing an algorithm for minimal TE was proposed that allows two different encodings. Such schemes were compared to an encoding approach that applies an established scheme to multi-spoke excitations. The impact on image quality and quantitative velocity maps was evaluated in phantoms using single- and two-spoke excitations. Additional validation measurements were obtained in-vivo at 7 T. RESULTS: Phantom experiments showed that keeping the first gradient moment constant for all k-space lines eliminates any displacements in phase-encoding and slice-selection direction for all spoke pulses but leads to artifacts for non-zero velocity components along readout direction. Introducing variable but well-defined first gradient moments in the phase-encoding direction creates displacements along the velocity vector and thus minimizes velocity-induced geometrical distortions. Phase-resolved mean volume flow in the ascending and descending aorta obtained from two-spoke excitation showed excellent agreement with single-spoke excitation over the cardiac cycle (mean difference 0.8 ± 16.2 ml/s). CONCLUSIONS: The use of single- and multi-spoke RF pulses for flow quantification at 7 T with controlled displacement artifacts has been successfully demonstrated. The presented techniques form the basis for correct velocity quantification and compensation not only for conventional but also for multi-spoke RF pulses allowing in-plane B1+ homogenization using parallel transmission at UHF.

Mapping the human brainstem: Brain nuclei and fiber tracts at 3 T and 7 T.

Structural high-resolution imaging of the brainstem can be of high importance in clinical practice. However, ultra-high field magnetic resonance imaging (MRI) is still restricted in use due to limited availability. Therefore, quantitative MRI techniques (quantitative susceptibility mapping [QSM], relaxation measurements [ R2* , R1 ], diffusion tensor imaging [DTI]) and T2 - and proton density (PD)-weighted imaging in the human brainstem at 3 T and 7 T are compared. Five healthy volunteers (mean age: 21.5 ± 1.9 years) were measured at 3 T and 7 T using multi-echo gradient echo sequences for susceptibility mapping and R2* relaxometry, magnetization-prepared 2 rapid acquisition gradient echo sequences for R1 relaxometry, turbo-spin echo sequences for PD- and T2 -weighted imaging and readout-segmented echo planar sequences for DTI. Susceptibility maps were computed using Laplacian-based phase unwrapping, V-SHARP for background field removal and the streaking artifact reduction for QSM algorithm for dipole inversion. Contrast-to-noise ratios (CNRs) were determined at 3 T and 7 T in ten volumes of interest (VOIs). Data acquired at 7 T showed higher CNR. However, in four VOIs, lower CNR was observed for R2* at 7 T. QSM was shown to be the contrast with which the highest number of structures could be identified. The depiction of very fine tracts such as the medial longitudinal fasciculus throughout the brainstem was only possible in susceptibility maps acquired at 7 T. DTI effectively showed the main tracts (crus cerebri, transverse pontine fibers, corticospinal tract, middle and superior cerebellar peduncle, pontocerebellar tract, and pyramid) at both field strengths. Assessing the brainstem with quantitative MRI methods such as QSM, R2* , as well as PD- and T2 -weighted imaging with great detail, is also possible at 3 T, especially when using susceptibility mapping calculated from a gradient echo sequence with a wide range of echo times from 10.5 to 52.5 ms. However, tracing smallest structures strongly benefits from imaging at ultra-high field.

4D flow imaging with 2D-selective excitation.

PURPOSE: 4D flow MRI permits to quantify non-invasively time-dependent velocity vector fields, but it demands long acquisition times. 2D-selective excitation allows to accelerate the acquisition by reducing the FOV in both phase encoding directions. In this study, we investigate 2D-selective excitation with reduced FOV imaging for fast 4D flow imaging while obtaining correct velocity quantification. METHODS: Two different 2D-selective excitation pulses were designed using spiral k-space trajectories. Further, their isophase time point was analyzed using simulations that considered both stationary and moving spins. On this basis, the 2D-selective RF pulses were implemented into a 4D flow sequence. A flow phantom study and seven 4D flow in vivo measurements were performed to assess the accuracy of velocity quantification by comparing the proposed technique to non-selective and conventional 1D slab-selective excitation. RESULTS: The isophase time point for spiral 2D-selective RF pulses was found to be located at the end of excitation for both stationary and moving spins. Based on that, 2D-selective excitation with reduced FOV allowed us to successfully quantify velocities both in a flow phantom and in vivo. In a flow phantom, the velocity difference Δv¯=0.8±5.3cm/s between the smaller reduced FOV and the reference scan was similar to the inter-scan variability of Δv¯=-1.0±2.3cm/s . In vivo, the differences in flow (P = 0.995) and flow volume (P = 0.469) between the larger reduced FOV and the reference scan were non-significant. By reducing the FOV by two-thirds, acquisition time was halved. CONCLUSION: A reduced field-of-excitation allows to limit the FOV and therefore shorten 4D flow acquisition times while preserving successful velocity quantification.