Bloch Simulations Research Articles

Rotary excitation of non-sinusoidal pulsed magnetic fields: Towards non-invasive direct detection of cardiac conduction.

There is a need for high resolution non-invasive imaging methods of physiologic magnetic fields. The purpose of this work is to develop a MRI detection approach for non-sinusoidal magnetic fields based on the rotary excitation (REX) mechanism which was previously successfully applied for the detection of oscillating magnetic fields in the sub-nT range. The new detection concept was examined by means of Bloch simulations, evaluating the interaction effect of spin-locked magnetization and low-frequency pulsed magnetic fields. The REX detection approach was validated under controlled conditions in phantom experiments at 3 T. Gaussian and sinc-shaped stimuli were investigated. In addition, the detection of artificial fields resembling a cardiac QRS complex, which is the most prominent peak visible on a magnetocardiogram, was tested. Bloch simulations demonstrated that the REX method has a high sensitivity to pulsed fields in the resonance case, which is met when the spin-lock frequency coincides with a non-zero Fourier component of the stimulus field. In the experiments, we found that magnetic stimuli of different durations and waveforms can be distinguished by their characteristic REX response spectrum. The detected REX amplitude was proportional to the stimulus peak amplitude (R2 > 0.98) and the lowest field detection was 1 nT. Furthermore, the detection of QRS-like fields with varying QRS durations yielded significant results in a phantom setup (p < 0.001). REX detection can be transferred to non-sinusoidal pulsed magnetic fields and could provide a non-invasive, quantitative tool for spatially resolved assessment of cardiac biomagnetism. Potential applications include the direct detection and characterization of cardiac conduction.

Highly efficient image navigator based 3D whole-heart cardiac MRA at 0.55T.

To develop and evaluate a highly efficient free-breathing and contrast-agent-free three-dimensional (3D) whole-heart Cardiac Magnetic Resonance Angiography (CMRA) sequence at 0.55T. Free-breathing whole-heart CMRA has been previously proposed at 1.5 and 3T. Direct application of this sequence to 0.55T is not possible due to changes in the magnetic properties of the tissues. To enable free-breathing CMRA at 0.55T, pulse sequence design and acquisition parameters of a previously proposed whole-heart CMRA framework are optimized via Bloch simulations. Image navigators (iNAVs) are used to enable nonrigid respiratory motion-correction and 100% respiratory scan efficiency. Patch-based low-rank denoising is employed to accelerate the scan and account for the reduced signal-to-noise ratio at 0.55T. The proposed approach was evaluated on 11 healthy subjects. Image quality was assessed by a clinical expert (1: poor to 5: excellent) for all intrapericardiac structures. Quantitative evaluation was performed by assessing the vessel sharpness of the proximal right coronary artery (RCA). Optimization resulted in an imaging flip angle of , fat saturation flip angle of , and six k-space lines for iNAV encoding. The relevant cardiac structures and main coronary arteries were visible in all subjects, with excellent image quality (mean ) and minimal artifacts (mean ), with RCA vessel sharpness ( ) comparable to previous studies at 1.5T. The proposed approach enables 3D whole-heart CMRA at 0.55T in a 6-min scan ( ), providing excellent image quality, minimal artifacts, and comparable vessel sharpness to previous 1.5T studies. Future work will include the evaluation of the proposed approach in patients with cardiovascular disease.

Physics-guided self-supervised learning: Demonstration for generalized RF pulse design.

To introduce a new method for generalized RF pulse design using physics-guided self-supervised learning (GPS), which uses the Bloch equations as the guiding physics model. The GPS framework consists of a neural network module and a physics module, where the physics module is a Bloch simulator for MRI applications. For RF pulse design, the neural network module maps an input target profile to an RF pulse, which is subsequently loaded into the physics module. Through the supervision of the physics module, the neural network module designs an RF pulse corresponding to the target profile. GPS was applied to design 1D selective, -insensitive, saturation, and multidimensional RF pulses, each conventionally requiring dedicated design algorithms. We further demonstrate our method's flexibility and versatility by compensating for experimental and scanner imperfections through online adaptation. Both simulations and experiments show that GPS can design a variety of RF pulses with corresponding profiles that agree well with the target input. Despite these verifications, GPS-designed pulses have unique differences compared to conventional designs, such as achieving -insensitivity using different mechanisms and using non-sampled regions of the conventional design to lower its peak power. Experiments, both ex vivo and in vivo, further verify that it can also be used for online adaptation to correct system imperfections, such as / inhomogeneity. This work demonstrates the generalizability, versatility, and flexibility of the GPS method for designing RF pulses and showcases its utility in several applications.

Simultaneous whole-liver water and mapping with isotropic resolution during free-breathing.

To develop and validate a data acquisition scheme combined with a motion-resolved reconstruction and dictionary-matching-based parameter estimation to enable free-breathing isotropic resolution self-navigated whole-liver simultaneous water-specific ( ) and ( ) mapping for the characterization of diffuse and oncological liver diseases. The proposed data acquisition consists of a magnetization preparation pulse and a two-echo gradient echo readout with a radial stack-of-stars trajectory, repeated with different preparations to achieve different and contrasts in a fixed acquisition time of 6 min. Regularized reconstruction was performed using self-navigation to account for motion during the free-breathing acquisition, followed by water-fat separation. Bloch simulations of the sequence were applied to optimize the sequence timing for insensitivity at 3 T, to correct for relaxation-induced blurring, and to map and using a dictionary. The proposed method was validated on a water-fat phantom with varying relaxation properties and in 10 volunteers against imaging and spectroscopy reference values. The performance and robustness of the proposed method were evaluated in five patients with abdominal pathologies. Simulations demonstrate good insensitivity of the proposed method in measuring and values. The proposed method produces co-registered and maps with a good agreement with reference methods (phantom: ; ). The proposed and mapping exhibits good repeatability and can be robustly performed in patients with pathologies. The proposed method allows whole-liver and quantification with high accuracy at isotropic resolution in a fixed acquisition time during free-breathing.

Variable-density velocity-selective magnetization preparation for non-contrast-enhanced peripheral MR angiography.

Velocity-selective (VS) magnetization preparation has shown great promise for non-contrast-enhanced (NCE) magnetic resonance angiography (MRA) with the ability to generate positive angiographic contrast directly using a single 3D acquisition. However, existing VS-MRA methods have an issue of aliased saturation around a certain velocity, known as velocity field-of-view (vFOV), which can cause undesired signal loss in arteries. This study aimed to develop a new version of the VS preparation pulse sequence that overcomes the aliased saturation problem in conventional VS preparation. Utilizing the fact that an excitation profile is the Fourier transform of excitation k-space sampling, we sampled the k-space in a non-uniform fashion by scaling gradient pulses accordingly to have aliased excitation diffused over velocity. The variable density sampling function was numerically optimized to maximize the average of the velocity passband signal while minimizing its variance. The optimized variable density VS magnetization was validated through Bloch simulations and applied to peripheral NCE MRA in healthy subjects. The in-vivo experiments showed that the proposed variable density VS-MRA significantly lowered arterial signal loss observed in conventional VS-MRA, as evidenced by a higher arterial signal-to-noise ratio (58.50 ± 14.29 vs. 55.54 ± 12.32; p < 0.05) and improved artery-to-background contrast-to-noise ratio (22.75 ± 7.57 vs. 20.60 ± 6.51; p < 0.05).

Generalized inhomogeneity-resilient relaxation along a fictitious field (girRAFF) for improved robustness in rotating frame relaxometry at 3T.

To optimize Relaxation along a Fictitious Field (RAFF) pulses for rotating frame relaxometry with improved robustness in the presence of and field inhomogeneities. The resilience of RAFF pulses against and inhomogeneities was studied using Bloch simulations. A parameterized extension of the RAFF formulation was introduced and used to derive a generalized inhomogeneity-resilient RAFF (girRAFF) pulse. RAFF and girRAFF preparation efficiency, defined as the ratio of the longitudinal magnetization before and after the preparation ( ), were simulated and validated in phantom experiments. and parametric maps were acquired at 3T in phantom, the calf muscle, and the knee cartilage of healthy subjects. The relaxation time maps were analyzed for resilience against artificially induced field inhomogeneities and assessed in terms of in vivo reproducibility. Optimized girRAFF preparations yielded improved preparation efficiency (0.95/0.91 simulations/phantom) with respect to RAFF (0.36/0.67 simulations/phantom). preparations showed in phantom/calf 6.0/4.8 times higher resilience to inhomogeneities than RAFF, and a 4.7/5.3 improved resilience to inhomogeneities. In the knee cartilage, (53 14 ms) was higher than (42 11 ms). Moreover, girRAFF preparations yielded 7.6/4.9 times improved reproducibility across / inhomogeneity conditions, 1.9 times better reproducibility across subjects and 1.2 times across slices compared with RAFF. Dixon-based fat suppression led to a further 15-fold improvement in the robustness of girRAFF to inhomogeneities. RAFF pulses display residual sensitivity to off-resonance and pronounced sensitivity to inhomogeneities. Optimized girRAFF pulses provide increased robustness and may be an appealing alternative for applications where resilience against field inhomogeneities is required.

Preparation-based B mapping in the heart using Bloch-Siegert shifts.

To develop and evaluate a robust cardiac mapping sequence at 3 T, using Bloch-Siegert shift (BSS)-based preparations. A longitudinal magnetization preparation module was designed to encode . After magnetization tip-down, off-resonant Fermi pulses, placed symmetrically around two refocusing pulses, induced BSS, followed by tipping back of the magnetization. Bloch simulations were used to optimize refocusing pulse parameters and to assess the mapping sensitivity. Relaxation-induced error was simulated for various / times. The effective mapping range was determined in phantom experiments, and maps were compared to the conventional BSS method and subadiabatic hyperbolic-secant 8 (HS8) pulse-sensitized method. Cardiac maps were acquired in healthy subjects, and evaluated for repeatability and imaging plane intersection consistency. The technique was modified for three-dimensional (3D) acquisition of the whole heart in a single breath-hold, and compared to two-dimensional (2D) acquisition. Simulations indicate that the proposed preparation can be tailored to achieve high mapping sensitivity across various ranges, with maximum sensitivity at the upper range. / -induced bias did not exceed 5.2 . Experimentally reproduced sensitization closely matched simulations for (mean difference 0.031 0.022, compared to 0.018 0.025 in the HS8-sensitized method), and showed 20-fold reduction in the standard deviation of repeated scans, compared with conventional BSS mapping, and an equivalent 2-fold reduction compared with HS8-sensitization. Robust cardiac map quality was obtained, with an average test-retest variability of 0.027 0.043 relative to normalized magnitude, and plane intersection bias of 0.052 0.031. 3D acquisitions showed good agreement with 2D scans (mean absolute deviation 0.055 0.061). BSS-based preparations enable robust and tailorable 2D/3D cardiac mapping at 3 T in a single breath-hold.

Quantification of the rotating frame relaxation time T2ρ: Comparison of balanced spin-lock and continuous-wave Malcolm-Levitt preparations.

For the quantification of rotating frame relaxation times, the T2ρ relaxation pathway plays an essential role. Nevertheless, T2ρ imaging has been studied only to a small extent compared with T1ρ, and preparation techniques for T2ρ have so far been adapted from T1ρ methods. In this work, two different preparation concepts are compared specifically for the use of T2ρ mapping. The first approach involves transferring the balanced spin-locking (B-SL) concept of T1ρ imaging. The second and newly proposed approach is a continuous-wave Malcolm-Levitt (CW-MLEV) pulse train with zero echo times and was motivated from T2 preparation strategies. The modules are tested in Bloch simulations for their intrinsic sensitivity to field inhomogeneities and validated in phantom experiments. In addition, myocardial T2ρ mapping was performed in mice as an exemplary application. Our results demonstrate that the CW-MLEV approach provides superior robustness and thus suggest that established methods of T1ρ imaging are not best suited for T2ρ experiments. In the presence of field inhomogeneities, the simulations indicated an increased banding compensation by a factor of 4.1 compared with B-SL. Quantification of left ventricular T2ρ time in mice yielded more consistent results, and values in the range of 59.2-61.1 ms (R2= 0.986-0.992) were observed at 7 T.

Arterial Input Function (AIF) Correction Using AIF Plus Tissue Inputs with a Bi-LSTM Network.

Background: The arterial input function (AIF) is vital for myocardial blood flow quantification in cardiac MRI to indicate the input time-concentration curve of a contrast agent. Inaccurate AIFs can significantly affect perfusion quantification. Purpose: When only saturated and biased AIFs are measured, this work investigates multiple ways of leveraging tissue curve information, including using AIF + tissue curves as inputs and optimizing the loss function for deep neural network training. Methods: Simulated data were generated using a 12-parameter AIF mathematical model for the AIF. Tissue curves were created from true AIFs combined with compartment-model parameters from a random distribution. Using Bloch simulations, a dictionary was constructed for a saturation-recovery 3D radial stack-of-stars sequence, accounting for deviations such as flip angle, T2* effects, and residual longitudinal magnetization after the saturation. A preliminary simulation study established the optimal tissue curve number using a bidirectional long short-term memory (Bi-LSTM) network with just AIF loss. Further optimization of the loss function involves comparing just AIF loss, AIF with compartment-model-based parameter loss, and AIF with compartment-model tissue loss. The optimized network was examined with both simulation and hybrid data, which included in vivo 3D stack-of-star datasets for testing. The AIF peak value accuracy and ktrans results were assessed. Results: Increasing the number of tissue curves can be beneficial when added tissue curves can provide extra information. Using just the AIF loss outperforms the other two proposed losses, including adding either a compartment-model-based tissue loss or a compartment-model parameter loss to the AIF loss. With the simulated data, the Bi-LSTM network reduced the AIF peak error from -23.6 ± 24.4% of the AIF using the dictionary method to 0.2 ± 7.2% (AIF input only) and 0.3 ± 2.5% (AIF + ten tissue curve inputs) of the network AIF. The corresponding ktrans error was reduced from -13.5 ± 8.8% to -0.6 ± 6.6% and 0.3 ± 2.1%. With the hybrid data (simulated data for training; in vivo data for testing), the AIF peak error was 15.0 ± 5.3% and the corresponding ktrans error was 20.7 ± 11.6% for the AIF using the dictionary method. The hybrid data revealed that using the AIF + tissue inputs reduced errors, with peak error (1.3 ± 11.1%) and ktrans error (-2.4 ± 6.7%). Conclusions: Integrating tissue curves with AIF curves into network inputs improves the precision of AI-driven AIF corrections. This result was seen both with simulated data and with applying the network trained only on simulated data to a limited in vivo test dataset.

Unravelling the mechanisms of CE-SSFP in imaging myocardium at risk: The effect of relaxation times on myocardial contrast

PurposeThe aim of this study was to investigate the contrast mechanisms of Contrast-enhanced steady-state free-precession (CE-SSFP) through the utilization of Bloch simulations in an experimental porcine model and in patients with acute myocardial infarction. MethodsSix pigs and ten patients with myocardial infarction underwent CMR and tissue characterization at 1.5 T whereas a Bloch simulation framework was utilized to simulate the CE-SSFP signal formation and compare it against the actual CE-SSFP signal acquired from the experimental porcine model and the patient population. The relaxation times of remote, salvaged, and infarcted myocardium were calculated after the injection of gadolinium, at the time of CE-SSFP acquisition. Simulations were performed using the same CE-SSFP pulse sequence as used on the scanner on a set of spins with the calculated relaxation times from the CMR scans. ResultsThe normalized signal intensities of salvaged and infarcted myocardium obtained with simulations were lower than the corresponding normalized signal intensities obtained in vivo in pigs (p < 0.05, 134% vs 153%) and in patients (p < 0.05, 126% vs 145%). The results from simulations showed a linear relationship to the results obtained in the experimental porcine model (r2 = 0.61) and in patients (r2 = 0.69). ConclusionThe T1 and T2 values of remote, salvaged, and infarcted myocardium only partly explain the signal intensities in CE-SSFP images. Bloch simulations suggest that there may be more elements that contribute to the CE-SSFP contrast. Integration of other aspects of the MR experiment into the simulation model could further help to fully unravel the mechanisms of CE-SSFP.

Direct angiographic comparison of different velocity-selective saturation, inversion, and DANTE labeling modules on cerebral arteries.

This study evaluated the velocity-selective (VS) MRA with different VS labeling modules, including double refocused hyperbolic tangent, eight-segment B1-insensitive rotation, delay alternating with nutation for tailored excitation, Fourier transform-based VS saturation, and Fourier transform-based inversion. These five VS labeling modules were evaluated first through Bloch simulations, and then using VSMRA directly on various cerebral arteries of healthy subjects. The relative signal ratios from arterial ROIs and surrounding tissues as well as relative arteria-tissue contrast ratios of different methods were compared. Double refocused hyperbolic tangent and eight-segment B1-insensitive rotation showed very similar labeling effects. Delay alternating with nutation for tailored excitation yielded high arterial signal but with residual tissue signal due to the spatial banding effect. Fourier transform-based VS saturation with half the time of other techniques serves as an efficient nonsubtractive VSMRA method, but the remaining tissue signal still obscured some small distal arteries that were delineated by other subtraction-based VSMRA, allowing more complete cancelation of static tissue. Fourier transform-based inversion produced the highest arterial signal in VSMRA with minimal tissue background. This is the first study that angiographically compared five different VS labeling modules. Their labeling characteristics on arteries and tissue and implications for VSMRA and VS arterial spin labeling are discussed.

Recurrent neural network-based simultaneous cardiac T1, T2, and T1ρ mapping.

The purpose of the current study was to explore the feasibility of training a deep neural network to accelerate the process of generating T1, T2, and T1ρ maps for a recently proposed free-breathing cardiac multiparametric mapping technique, where a recurrent neural network (RNN) was utilized to exploit the temporal correlation among the multicontrast images. The RNN-based model was developed for rapid and accurate T1, T2, and T1ρ estimation. Bloch simulation was performed to simulate a dataset of more than 10 million signals and time correspondences with different noise levels for network training. The proposed RNN-based method was compared with a dictionary-matching method and a conventional mapping method to evaluate the model's effectiveness in phantom and in vivo studies at 3 T, respectively. In phantom studies, the RNN-based method and the dictionary-matching method achieved similar accuracy and precision in T1, T2, and T1ρ estimations. In in vivo studies, the estimated T1, T2, and T1ρ values obtained by the two methods achieved similar accuracy and precision for 10 healthy volunteers (T1: 1228.70 ± 53.80 vs. 1228.34 ± 52.91 ms, p > 0.1; T2: 40.70 ± 2.89 vs. 41.19 ± 2.91 ms, p > 0.1; T1ρ: 45.09 ± 4.47 vs. 45.23 ± 4.65 ms, p > 0.1). The RNN-based method can generate cardiac multiparameter quantitative maps simultaneously in just 2 s, achieving 60-fold acceleration compared with the dictionary-matching method. The RNN-accelerated method offers an almost instantaneous approach for reconstructing accurate T1, T2, and T1ρ maps, being much more efficient than the dictionary-matching method for the free-breathing multiparametric cardiac mapping technique, which may pave the way for inline mapping in clinical applications.

GPU-accelerated Bloch simulations and MR-STAT reconstructions using the Julia programming language.

MR-STAT is a relatively new multiparametric quantitative MRI technique in which quantitative paramater maps are obtained by solving a large-scale nonlinear optimization problem. Managing reconstruction times is one of the main challenges of MR-STAT. In this work we leverage GPU hardware to reduce MR-STAT reconstruction times. A highly optimized, GPU-compatible Bloch simulation toolbox is developed as part of this work that can be utilized for other quantitative MRI techniques as well. The Julia programming language was used to develop a flexible yet highly performant and GPU-compatible Bloch simulation toolbox called BlochSimulators.jl. The runtime performance of the toolbox is benchmarked against other Bloch simulation toolboxes. Furthermore, a (partially matrix-free) modification of a previously presented (matrix-free) MR-STAT reconstruction algorithm is proposed and implemented using the Julia language on GPU hardware. The proposed algorithm is combined with BlochSimulators.jl and the resulting MR-STAT reconstruction times on GPU hardware are compared to previously presented MR-STAT reconstruction times. The BlochSimulators.jl package demonstrates superior runtime performance on both CPU and GPU hardware when compared to other existing Bloch simulation toolboxes. The GPU-accelerated partially matrix-free MR-STAT reconstruction algorithm, which relies on BlochSimulators.jl, allows for reconstructions of 68 seconds per two-dimensional (2D slice). By combining the proposed Bloch simulation toolbox and the partially matrix-free reconstruction algorithm, 2D MR-STAT reconstructions can be performed in the order of one minute on a modern GPU card. The Bloch simulation toolbox can be utilized for other quantitative MRI techniques as well, for example for online dictionary generation for MR Fingerprinting.

Unsupervised deep learning with convolutional neural networks for static parallel transmit design: A retrospective study.

To mitigate inhomogeneity at 7T for multi-channel transmit arrays using unsupervised deep learning with convolutional neural networks (CNNs). Deep learning parallel transmit (pTx) pulse design has received attention, but such methods have relied on supervised training and did not use CNNs for multi-channel maps. In this work, we introduce an alternative approach that facilitates the use of CNNs with multi-channel maps while performing unsupervised training. The multi-channel maps are concatenated along the spatial dimension to enable shift-equivariant processing amenable to CNNs. Training is performed in an unsupervised manner using a physics-driven loss function that minimizes the discrepancy of the Bloch simulation with the target magnetization, which eliminates the calculation of reference transmit RF weights. The training database comprises 3824 2D sagittal, multi-channel maps of the healthy human brain from 143 subjects. data were acquired at 7T using an 8Tx/32Rx head coil. The proposed method is compared to the unregularized magnitude least-squares (MLS) solution for the target magnetization in static pTx design. The proposed method outperformed the unregularized MLS solution for RMS error and coefficient-of-variation and had comparable energy consumption. Additionally, the proposed method did not show local phase singularities leading to distinct holes in the resulting magnetization unlike the unregularized MLS solution. Proposed unsupervised deep learning with CNNs performs better than unregularized MLS in static pTx for speed and robustness.

CMRsim-A python package for cardiovascular MR simulations incorporating complex motion and flow.

To present an open-source MR simulation framework that facilitates the incorporation of complex motion and flow for studying cardiovascular MR (CMR) acquisition and reconstruction. CMRsim is a Python package that allows simulation of CMR images using dynamic digital phantoms with complex motion as input. Two simulation paradigms are available, namely, numerical and analytical solutions to the Bloch equations, using a common motion representation. Competitive simulation speeds are achieved using TensorFlow for GPU acceleration. To demonstrate the capability of the package, one introductory and two advanced CMR simulation experiments are presented. The latter showcase phase-contrast imaging of turbulent flow downstream of a stenotic section and cardiac diffusion tensor imaging on a contracting left ventricle. Additionally, extensive documentation and example resources are provided. The Bloch simulation with turbulent flow using approximately 1.5 million particles and a sequence duration of 710 ms for each of the seven different velocity encodings took a total of 29 min on a NVIDIA Titan RTX GPU. The results show characteristic phase contrast and magnitude modulation present in real data. The analytical simulation of cardiac diffusion tensor imaging with bulk-motion phase sensitivity took approximately 10 s per diffusion-weighted image, including preparation and loading steps. The results exhibit the expected alteration of diffusion metrics due to strain. CMRsim is the first simulation framework that allows one to feasibly incorporate complex motion, including turbulent flow, to systematically study advanced CMR acquisition and reconstruction approaches. The open-source package features modularity and transparency, facilitating maintainability and extensibility in support of reproducible research.

Mitigating slice cross-talk in multi-slice multi-echo spin echo T2 mapping.

To investigate whether a T2 inter-slice variation could occur when a multi-slice multi-echo spin echo (MESE) sequence is used for image acquisition and to propose an enhanced method for reconstructing T2 maps that can effectively address and correct these variations. Bloch simulations were performed accounting for the direct saturation effect to evaluate magnetization changes in multi-slice 2D MESE sequence. Experimental phantom scans were performed to validate these simulations. An improved version of the dictionary-based reconstruction approach was proposed, enabling the creation of a multi-slice dictionary of echo modulation curves (EMC). The corresponding method has been assayed considering inter-slice T2 variation with phantoms and in lower leg. Experimental and numerical study illustrate that direct saturation leads to a bias of EMCs. This bias during the T2 maps reconstructions using original single-slice EMC-dictionary method led to inter-slice T2 variation of 2.03% in average coefficient of variation (CV) in agarose phantoms, and up to 2.8% in vivo (for TR = 2 s, slice gap = 0%). A reduction of CV was observed when increasing the gap up to 100% (0.36% in phantoms, and up to 1.5% in vivo) or increasing TR up to 4 s (0.76% in phantoms, and up to 1.9% in vivo). Matching the multi-slice experimental data with multi-slice dictionaries provided a reduced CV of 0.54% in phantoms and up to 2.3% in vivo. T2 values quantified from multi-slice MESE images using single-slice dictionaries are biased. A dedicated multi-slice EMC method providing the appropriate dictionaries can reduce the inter-slice T2 variation.

Field camera input to virtual phantom (ViP) scanner acquisitions for quality assurance of derived MRI quantities: first implementation and proof-of-principle.

Quality assurance (QA) of measurements derived from MRI can require complicated test phantoms. This work introduces a new QA concept using gradient and transmit RF recordings by a limited field camera (FC) to govern the previous Virtual Phantom (ViP) method. The purpose is to describe the first technical implementation of combined FC+ViP, and illustrate its performance in examples, including quantitative first-pass myocardial perfusion. The new QA concept starts with a synthetic test object (STO) representing some arbitrary test input. Using recordings of the unmodified standard sequence by a gradient and RF waveform camera (FC), ViP calculates by Bloch simulation the continuous RF signal emitted by the STO during this sequence (hence FC+ViP). During nominally identical repetition of the sequence acquisition, ViP transmits the RF signal for scanner reception, reconstruction and any further parametric derivations by the unmodified standard scanner image reconstruction and analysis software. The scanner outputs were compared against the input STOs. First proof-of-principle was discussed and supported by correlation between scanner outputs and the input STO. The work makes no claim that its examples are valid QA methods. It concludes by proposing a new industrial standard for QA without the FC.

Velocity selective spin labeling using parallel transmission.

Ultra-high field (UHF) provides improved SNR which greatly benefits SNR starved imaging techniques such as perfusion imaging. However, transmit field (B1 + ) inhomogeneities commonly observed at UHF hinders the excitation uniformity. Here we show how replacing standard excitation pulses with parallel transmit pulses can improve efficiency of velocity selective labeling. The standard tip-down and tip-up excitation pulses found in a velocity selective preparation module were replaced with tailored non-selective kT -points pulse solutions. Bloch simulations and experimental validation on a custom-built flow phantom and in vivo was performed to evaluate different pulse configurations in circularly polarized mode (CP-mode) and parallel transmit (pTx) mode. Tailored pTx pulses significantly improved velocity selective labeling fidelity and signal uniformity. The transverse magnetization normalized RMS error was reduced from 0.489 to 0.047 when compared to standard rectangular pulses played in CP-mode. Simulations showed that manipulation of time symmetry in the tailored pTx pulses is vital in minimizing residual magnetization. In addition, in vivo experiments achieved a 44% lower RF power output and a shorter pulse duration when compared to using adiabatic pulses in CP-mode. Using tailored pTx pulses for excitation within a velocity selective labeling preparation mitigated transmit field artifacts and improved SNR and contrast fidelity. The improvement in labeling efficiency highlights the potential of using pTx to improve robustness and accessibility of flow-based sequences such as velocity selective spin labeling at ultra-high field.

Stochastic-offset-enhanced restricted slice excitation and 180° refocusing designs with spatially non-linear ΔB0 shim array fields.

Developing a general framework with a novel stochastic offset strategy for the design of optimized RF pulses and time-varying spatially non-linear ΔB0 shim array fields for restricted slice excitation and refocusing with refined magnetization profiles within the intervals of the fixed voxels. Our framework uses the decomposition property of the Bloch equations to enable joint design of RF-pulses and shim array fields for restricted slice excitation and refocusing with auto-differentiation optimization. Bloch simulations are performed independently on orthogonal basis vectors, Mx, My, and Mz, which enables designs for arbitrary initial magnetizations. Requirements for refocusing pulse designs are derived from the extended phase graph formalism obviating time-consuming sub-voxel isochromatic simulations to model the effects of crusher gradients. To refine resultant slice-profiles because of voxelwise optimization functions, we propose an algorithm that stochastically offsets spatial points at which loss is computed during optimization. We first applied our proposed design framework to standard slice-selective excitation and refocusing pulses in the absence of non-linear ΔB0 shim array fields and compared them against pulses designed with Shinnar-Le Roux algorithm. Next, we demonstrated our technique in a simulated setup of fetal brain imaging in pregnancy for restricted-slice excitation and refocusing of the fetal brain. Our proposed framework for optimizing RF pulse and time-varying spatially non-linear ΔB0 shim array fields achieve high fidelity restricted-slice excitation and refocusing for fetal MRI, which could enable zoomed fast-spin-echo-MRI and other applications.

Alternating Look-Locker for quantitative T1 , T1ρ and B1 3D MRI mapping.

To develop a new MRI method, entitled alternating Look-Locker (aLL), for quantitative , , and 3D mapping. A Look-Locker scheme that alternates magnetization from +Z and -Z axes of the laboratory frame is utilized in combination with a 3D Multi-Band Sweep Imaging with Fourier Transformation (MB-SWIFT) readout. The analytical solution describing the spin evolution during aLL, as well as the correction required for segmented acquisition were derived. The simultaneous and mapping are demonstrated on an agar/saline phantom and on an in-vivo rat head. relaxation was achieved by cyclically applying magnetization preparation (MP) modules consisting of two adiabatic pulses. values in the rat brain in-vivo and in a gadobenate dimeglumine (Gd-DTPA) phantom were compared to those obtained with a previously introduced steady-state (SS) method. The accuracy and precision of the analytical solution was tested by Bloch simulations. With the application of MP modules, the aLL method provides simultaneous and maps. Conversely, without it, the method can be used for simultaneous and mapping. values were similar with both aLL and SS techniques. However, the aLL method resulted in more robust quantitative mapping compared to the SS method. Unlike the SS method, the aLL method does not require additional scans for generating maps. The proposed method offers a new flexible tool for quantitative mapping of , , and . The aLL method can also be used with readout schemes different from MB-SWIFT.