Bloch Simulations Research Articles

Bloch–Siegert Effect for Surface Nuclear Magnetic Resonance Sounding Experiments in the Unsaturated Zone

The Bloch–Siegert effect is relevant for NMR experiments where components of the excitation pulse other than the circularly polarized component have an influence on the evolution of the magnetization of the spin system under consideration. For linearly polarized excitation fields this happens at amplitudes higher than roughly one tenth of the magnitude of the static magnetic field. Since surface nuclear magnetic resonance (SNMR) experiments, also called magnetic-resonance-soundings (MRS), are conducted in the relatively low local field of the earth, the Bloch–Siegert effect can quickly become relevant. This is especially the case for SNMR experiments in the unsaturated zone, where due to short relaxation times fast pulses of high intensity must be used. To describe the Bloch–Siegert effect, we use the average Hamiltonian approximation obtained by the Magnus expansion of up to fifth order, as well as the solution of the Bloch equations. The results of these approximations are tested against the Bloch simulations and it is shown that they are only valid for limited ranges of the excitation field amplitude. The influence of the Bloch–Siegert effect on sensitivity kernels is described and verified with experimental data obtained with a small scale SNMR sensor on water containers.

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

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 ( ), macromolecular fraction ( ), magnetization exchange rate ( ) and local transmit field ( ). 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 with compensation was further investigated through experiments, both ex vivo and in vivo. Comparing BTS with existing methods, simulations showed that existing methods can significantly bias estimation when not accounting for transmit 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 of inhomogeneity. 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 ( , , ) that are free from bias.

MR-zero meets RARE MRI: Joint optimization of refocusing flip angles and neural networks to minimize T2 -induced blurring in spin echo sequences.

An end-to-end differentiable 2D Bloch simulation is used to reduce T2 induced blurring in single-shot turbo spin echo sequences, also called rapid imaging with refocused echoes (RARE) sequences, by using a joint optimization of refocusing flip angles and a convolutional neural network. Simulation and optimization were performed in the MR-zero framework. Variable flip angle train and DenseNet parameters were optimized jointly using the instantaneous transverse magnetization, available in our simulation, at a certain echo time, which serves as ideal blurring-free target. Final optimized sequences were exported for in vivo measurements at a real system (3 T Siemens, PRISMA) using the Pulseq standard. The optimized RARE was able to successfully lower T2 -induced blurring for single-shot RARE sequences in proton density-weighted and T2 -weighted images. In addition to an increased sharpness, the neural network allowed correction of the contrast changes to match the theoretical transversal magnetization. The optimization found flip angle design strategies similar to existing literature, however, visual inspection of the images and evaluation of the respective point spread function demonstrated an improved performance. This work demonstrates that when variable flip angles and a convolutional neural network are optimized jointly in an end-to-end approach, sequences with more efficient minimization of T2 -induced blurring can be found. This allows faster single- or multi-shot RARE MRI with longer echo trains.

A deep learning approach for accurate and robust T1 estimation from cardiac magnetic resonance MOLLI sequences

Abstract Funding Acknowledgements Type of funding sources: None. Background T1 parametric maps of the myocardium are a recent development in cardiac Magnetic Resonance Imaging (MRI). They can be used to characterize the composition of the heart and identify alterations that can be indicative of a pathology. A common method of T1 estimation involves pixel-wise non-linear curve fitting from Modified Look-Locker Inversion Recovery (MOLLI) series [1]. Whilst relatively accurate, this method presents several drawbacks. Firstly, it assumes magnetization recovery to be a purely exponential process dependent on T1, thus neglecting other parameters such as T2 and B1 that can interfere in the process. Secondly, it does not consider that each acquisition during an inversion recovery experiment inverts the magnetization vector by a given angle and thus interferes with the recovery leading to the measurement of a shorter T1* rather than the actual T1 [2]. Additionally, since it considers each pixel individually, neglecting spatial information, it is sensitive to image quality [3]. Finally, since non-linear curve fitting is an iterative algorithm, it is computationally expensive and time-consuming. Methods To address these issues a convolutional neural network, DeepBLESS [4], and two spatially aware variants of it, were trained to predict T1, T2, and B1 with a dataset of MOLLI series simulated from experimental data. The simulation was performed using Bloch simulations and parameters extracted from a dataset composed of 27 single-slice MOLLI series acquired with the 5(3s)3 protocol using a b-SSFP readout at a main magnetic field strength of 3 Tesla. Thus, a dataset could be generated without relying on exponential fitting for ground truth generation. Results The exponential fitting of T1 was shown to consistently underestimate true T1 values by 138 +- 90 ms. In comparison, the spatially naïve DeepBLESS approach showed a mean absolute error of 14 +- 9 ms and the spatially aware DeepBLESS approach (Median-DeepBLESS) showed a mean absolute error of 13 +- 9 ms. As can be seen in Figure 1, Median-DeepBLESS also showed considerably more resilience to noise than exponential fitting and the spatially naïve version of DeepBLESS. Additionally, the deep learning approaches showcased promising results with regards to the estimation of T2 and B1 from MOLLI sequences (See Figure 2). Conclusions It was shown that the Deep Learning approaches eliminated the underestimation bias of exponential fitting with the spatially aware network Median-DeepBLESS being more robust to noise than both exponential fitting and the standard DeepBLESS network for myocardial T1 estimation. Relatively accurate estimation of T2 and B1 from MOLLI sequences was also accomplished through the Deep Learning approach, which may lead to simultaneous T1, T2, and B1 estimation in common clinical practice in the future.

Self-consistent simulations of intracavity terahertz comb difference frequency generation by mid-infrared quantum cascade lasers

Portable terahertz (THz) frequency comb sources are highly desired for applications in rotational molecular spectroscopy and sensing. To date, direct THz quantum cascade laser (QCL) frequency comb generation is not achievable at room temperature. However, THz comb generation based on intracavity difference frequency generation (DFG) in mid-infrared (mid-IR) QCLs is a promising alternative. Here, we present a numerical study of THz DFG-QCL comb formation in mid-IR QCLs based on a self-consistent multi-domain simulation approach. The dynamical simulations are performed using our open-source software tool mbsolve, which provides a flexible and efficient codebase for solving the generalized full-wave Maxwell–Bloch equations. Here, DFG in the active region of a dual-wavelength mid-IR QCL is considered for the generation of THz radiation. The mixing process and, thus, THz generation require a high second-order intersubband nonlinear susceptibility in the QCL active region and can be obtained by targeted quantum engineering. The associated nonlinear effects are included in the Hamiltonian of our Maxwell–Bloch simulation approach. All necessary input parameters for the description of the quantum system are determined self-consistently using our in-house ensemble Monte Carlo software tool for stationary carrier transport simulations. Notably, such simulations require a full-wave Maxwell–Bloch solver that does not employ the common rotating wave approximation, as a broadband optical field extending from the THz to the mid-IR region is investigated. Our modeling approach and the obtained simulation results for two THz DFG-QCL comb setups are validated against experimental data, showing reasonable agreement. Furthermore, we obtain a locked frequency modulated comb state for mid-IR and THz regimes.

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

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. 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. 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. 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.

Concurrent measurement of perfusion parameters related to small blood vessels and cerebrospinal fluid circulation in the human brain using dynamic dual-spin-echo perfusion MRI.

Accumulating evidence from recent studies has indicated the importance of studying the interaction between the microvascular and lymphatic systems in the brain. To date, most imaging methods can only measure blood or lymphatic vessels separately, such as dynamic susceptibility contrast (DSC) MRI for blood vessels and DSC MRI-in-the-cerebrospinal fluid (CSF) (cDSC MRI) for lymphatic vessels. An approach that can measure both blood and lymphatic vessels in a single scan offers advantages such as a halved scan time and contrast dosage. This study attempts to develop one such approach by optimizing a dual-echo turbo-spin-echo sequence, termed "dynamic dual-spin-echo perfusion (DDSEP) MRI". Bloch simulations were performed to optimize the dual-echo sequence for the measurement of gadolinium (Gd)-induced blood and CSF signal changes using a short and a long echo time, respectively. The proposed method furnishes a T1-dominant contrast in CSF and a T2-dominant contrast in blood. MRI experiments were performed in healthy subjects to evaluate the dual-echo approach by comparing it with existing separate methods. Based on simulations, the short and long echo time were chosen around the time when blood signals show maximum difference between post- and pre-Gd scans, and the time when blood signals are completely suppressed, respectively. The proposed method showed consistent results in human brains as previous studies using separate methods. Signal changes from small blood vessels occurred faster than from lymphatic vessels after intravenous Gd injection. In conclusion, Gd-induced signal changes in blood and CSF can be detected simultaneously in healthy subjects with the proposed sequence. The temporal difference in Gd-induced signal changes from small blood and lymphatic vessels after intravenous Gd injection was confirmed using the proposed approach in the same human subjects. Results from this proof-of-concept study will be used to further optimize DDSEP MRI in subsequent studies.

Robust cardiac mapping at 3T using adiabatic spin-lock preparations.

The aim of this study is to develop and optimize an adiabatic ( ) mapping method for robust quantification of spin-lock (SL) relaxation in the myocardium at 3T. Adiabatic SL (aSL) preparations were optimized for resilience against and inhomogeneities using Bloch simulations. Optimized -aSL, Bal-aSL and -aSL modules, each compensating for different inhomogeneities, were first validated in phantom and human calf. Myocardial mapping was performed using a single breath-hold cardiac-triggered bSSFP-based sequence. Then, optimized preparations were compared to each other and to conventional SL-prepared maps (RefSL) in phantoms to assess repeatability, and in 13 healthy subjects to investigate image quality, precision, reproducibility and intersubject variability. Finally, aSL and RefSL sequences were tested on six patients with known or suspected cardiovascular disease and compared with LGE, , and ECV mapping. The highest preparation efficiency was obtained in simulations for modules comprising 2 HS pulses of 30 ms each. In vivo maps yielded significantly higher quality than RefSL maps. Average myocardial values were 183.28 25.53 ms, compared with 38.21 14.37 ms RefSL-prepared . maps showed a significant improvement in precision (avg. 14.47 3.71% aSL, 37.61 19.42% RefSL, p < 0.01) and reproducibility (avg. 4.64 2.18% aSL, 47.39 12.06% RefSL, p < 0.0001), with decreased inter-subject variability (avg. 8.76 3.65% aSL, 51.90 15.27% RefSL, p < 0.0001). Among aSL preparations, -aSL achieved the better inter-subject variability. In patients, -aSL preparations showed the best artifact resilience among the adiabatic preparations. times show focal alteration colocalized with areas of hyper-enhancement in the LGE images. Adiabatic preparations enable robust in vivo quantification of myocardial SL relaxation times at 3T.

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

MRI's T2 relaxation time is a valuable biomarker for neuromuscular disorders and muscle dystrophies. One of the hallmarks of these pathologies is the infiltration of adipose tissue and a loss of muscle volume. This leads to a mixture of two signal components, from fat and from water, to appear in each imaged voxel, each having a specific T2 relaxation time. In this proof-of-concept work, we present a technique that can separate the signals from water and from fat within each voxel, measure their separate T2 values, and calculate their relative fractions. The echo modulation curve (EMC) algorithm is a dictionary-based technique that offers accurate and reproducible mapping of T2 relaxation times. We present an extension of the EMC algorithm for estimating subvoxel fat and water fractions, alongside the T2 and proton-density values of each component. To facilitate data processing, calf and thigh anatomy were automatically segmented using a fully convolutional neural network and FSLeyes software. The preprocessing included creating two signal dictionaries, for water and for fat, using Bloch simulations of the prospective protocol. Postprocessing included voxelwise fitting for two components, by matching the experimental decay curve to a linear combination of the two simulated dictionaries. Subvoxel fat and water fractions and relaxation times were generated and used to calculate a new quantitative biomarker, termed viable muscle index, and reflecting disease severity. This biomarker indicates the fraction of remaining muscle out of the entire muscle region. The results were compared with those using the conventional Dixon technique, showing high agreement (R = 0.98, p< 0.001). It was concluded that the new extension of the EMC algorithm can be used to quantify abnormal fat infiltration as well as identify early inflammatory processes corresponding to elevation in the T2 value of the water (muscle) component. This new ability may improve the diagnostic accuracy of neuromuscular diseases, help stratification of patients according to disease severity, and offer an efficient tool for tracking disease progression.

High-efficient Bloch simulation of magnetic resonance imaging sequences based on deep learning

Objective. Bloch simulation constitutes an essential part of magnetic resonance imaging (MRI) development. However, even with the graphics processing unit (GPU) acceleration, the heavy computational load remains a major challenge, especially in large-scale, high-accuracy simulation scenarios. This work aims to develop a deep learning-based simulator to accelerate Bloch simulation. Approach. The simulator model, called Simu-Net, is based on an end-to-end convolutional neural network and is trained with synthetic data generated by traditional Bloch simulation. It uses dynamic convolution to fuse spatial and physical information with different dimensions and introduces position encoding templates to achieve position-specific labeling and overcome the receptive field limitation of the convolutional network. Main results. Compared with mainstream GPU-based MRI simulation software, Simu-Net successfully accelerates simulations by hundreds of times in both traditional and advanced MRI pulse sequences. The accuracy and robustness of the proposed framework were verified qualitatively and quantitatively. Besides, the trained Simu-Net was applied to generate sufficient customized training samples for deep learning-based T 2 mapping and comparable results to conventional methods were obtained in the human brain. Significance. As a proof-of-concept work, Simu-Net shows the potential to apply deep learning for rapidly approximating the forward physical process of MRI and may increase the efficiency of Bloch simulation for optimization of MRI pulse sequences and deep learning-based methods.

Free-breathing simultaneous native myocardial T1, T2 and T1ρ mapping with Cartesian acquisition and dictionary matching

BackgroundT1, T2 and T1ρ are well-recognized parameters for quantitative cardiac MRI. Simultaneous estimation of these parameters allows for comprehensive myocardial tissue characterization, such as myocardial fibrosis and edema. However, conventional techniques either quantify the parameters individually with separate breath-hold acquisitions, which may result in unregistered parameter maps, or estimate multiple parameters in a prolonged breath-hold acquisition, which may be intolerable to patients. We propose a free-breathing multi-parametric mapping (FB-MultiMap) technique that provides co-registered myocardial T1, T2 and T1ρ maps in a single efficient acquisition. MethodsThe proposed FB-MultiMap performs electrocardiogram-triggered single-shot Cartesian acquisition over 16 consecutive cardiac cycles, where inversion, T2 and T1ρ preparations are introduced for varying contrasts. A diaphragmatic navigator was used for prospective through-plane motion correction and the in-plane motion was corrected retrospectively with a group-wise image registration method. Quantitative mapping was conducted through dictionary matching of the motion corrected images, where the subject-specific dictionary was created using Bloch simulations for a range of T1, T2 and T1ρ values, as well as B1 factors to account for B1 inhomogeneities. The FB-MultiMap was optimized and validated in numerical simulations, phantom experiments, and in vivo imaging of 15 healthy subjects and six patients with suspected cardiac diseases. ResultsThe phantom T1, T2 and T1ρ values estimated with FB-MultiMap agreed well with reference measurements with no dependency on heart rate. In healthy subjects, FB-MultiMap T1 was higher than MOLLI T1 mapping (1218 ± 50 ms vs. 1166 ± 38 ms, p < 0.001). The myocardial T2 and T1ρ estimated with FB-MultiMap were lower compared to the mapping with T2- or T1ρ-prepared 2D balanced steady-state free precession (T2: 41.2 ± 2.8 ms vs. 42.5 ± 3.1 ms, p = 0.06; T1ρ: 45.3 ± 4.4 ms vs. 50.2 ± 4.0, p < 0.001). The pathological changes in myocardial parameters measured with FB-MultiMap were consistent with conventional techniques in all patients. ConclusionThe proposed free-breathing multi-parametric mapping technique provides co-registered myocardial T1, T2 and T1ρ maps in 16 heartbeats, achieving similar mapping quality to conventional breath-hold mapping methods.

Free-breathing and instantaneous abdominal T2 mapping via single-shot multiple overlapping-echo acquisition and deep learning reconstruction.

To develop a real-time abdominal T2 mapping method without requiring breath-holding or respiratory-gating. The single-shot multiple overlapping-echo detachment (MOLED) pulse sequence was employed to achieve free-breathing T2 mapping of the abdomen. Deep learning was used to untangle the non-linear relationship between the MOLED signal and T2 mapping. A synthetic data generation flow based on Bloch simulation, modality synthesis, and randomization was proposed to overcome the inadequacy of real-world training set. The results from simulation and in vivo experiments demonstrated that our method could deliver high-quality T2 mapping. The average NMSE and R2 values of linear regression in the digital phantom experiments were 0.0178 and 0.9751. Pearson's correlation coefficient between our predicted T2 and reference T2 in the phantom experiments was 0.9996. In the measurements for the patients, real-time capture of the T2 value changes of various abdominal organs before and after contrast agent injection was realized. A total of 33 focal liver lesions were detected in the group, and the mean and standard deviation of T2 values were 141.1 ± 50.0ms for benign and 63.3 ± 16.0ms for malignant lesions. The coefficients of variance in a test-retest experiment were 2.9%, 1.2%, 0.9%, 3.1%, and 1.8% for the liver, kidney, gallbladder, spleen, and skeletal muscle, respectively. Free-breathing abdominal T2 mapping is achieved in about 100ms on a clinical MRI scanner. The work paved the way for the development of real-time dynamic T2 mapping in the abdomen. • MOLED achieves free-breathing abdominal T2 mapping in about 100ms, enabling real-time capture of T2 value changes due to CA injection in abdominal organs. • Synthetic data generation flow mitigates the issue of lack of sizable abdominal training datasets.

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

PurposeMRI's T2 relaxation time is one of the key contrast mechanisms for clinical diagnosis and prognosis of pathologies. Mapping this relaxation time, however, involves extensive scan times, which are needed to collect quantitative data, thereby impeding its integration into clinical routine. This study employs a low-rank plus sparse (L + S) signal decomposition approach in order to reconstruct accurate T2-maps from highly undersampled multi-echo spin-echo (MESE) MRI data. MethodsTwo new algorithms are presented: the first uses standard L + S approach, where both L and S are iteratively updated. The second technique, dubbed SPArse and fixed RanK (SPARK), uses a fixed-rank L, under the assumption that most MESE information is found in the L component and that this rank can be pre-calculated. The utility of these new techniques is demonstrated on in vivo brain and calf data at x2 to x6 acceleration factors. ResultsAccelerated T2 maps showed improved accuracy compared to fully sampled ground truth maps, when using L + S and SPARK techniques vis-à-vis standard GRAPPA acceleration. ConclusionSPARK provides accurate T2 maps with increased robustness to the selection of reconstruction parameters making it suitable to a wide range of applications and facilitating the use of quantitative T2 information in clinical settings.

Three-dimensional Multi-parameter Mapping of Relaxation Times and Susceptibility Using Partially RF-spoiled Gradient Echo.

MR parameter mapping is a technique that obtains distributions of parameters such as relaxation time and proton density (PD) and is starting to be used for disease quantification in clinical diagnoses. Quantitative susceptibility mapping is also promising for the early diagnosis of brain disorders such as degenerative neurological disorders. Therefore, we developed an MR quantitative parameter mapping (QPM) method to map four tissue-related parameters (T1, T2*, PD, and susceptibility) and B1 simultaneously by using a 3D partially RF-spoiled gradient echo (pRSGE). We verified the accuracy and repeatability of QPM in phantom and volunteer experiments. Tissue-related parameters are estimated by varying four scan parameters of the 3D pRSGE: flip angle, RF-pulse phase increment, TR and TE, performing multiple image scans, and finding a least-squares fit for an intensity function (which expresses the relationship between the scan parameters and intensity values). The intensity function is analytically complex, but by using a Bloch simulation to create it numerically, the least-squares fitting can be used to estimate the quantitative values. This has the advantage of shortening the image-reconstruction processing time needed to estimate the quantitative values than with methods using pattern matching. A 1.1-mm isotropic resolution scan covering the whole brain was completed with a scan time of approximately 12 minutes, and the reconstruction time using a GPU was approximately 1 minute. The phantom experiments confirmed that both the accuracy and repeatability of the quantitative values were high. The volunteer scans also confirmed that the accuracy of the quantitative values was comparable to that of conventional methods. The proposed QPM method can map T1, T2*, PD, susceptibility, and B1 simultaneously within a scan time that can be applied to human subjects.

MOdel-Based SyntheTic Data-Driven Learning (MOST-DL): Application in Single-Shot T2 Mapping With Severe Head Motion Using Overlapping-Echo Acquisition.

Use of synthetic data has provided a potential solution for addressing unavailable or insufficient training samples in deep learning-based magnetic resonance imaging (MRI). However, the challenge brought by domain gap between synthetic and real data is usually encountered, especially under complex experimental conditions. In this study, by combining Bloch simulation and general MRI models, we propose a framework for addressing the lack of training data in supervised learning scenarios, termed MOST-DL. A challenging application is demonstrated to verify the proposed framework and achieve motion-robust [Formula: see text] mapping using single-shot overlapping-echo acquisition. We decompose the process into two main steps: (1) calibrationless parallel reconstruction for ultra-fast pulse sequence and (2) intra-shot motion correction for [Formula: see text] mapping. To bridge the domain gap, realistic textures from a public database and various imperfection simulations were explored. The neural network was first trained with pure synthetic data and then evaluated with in vivo human brain. Both simulation and in vivo experiments show that the MOST-DL method significantly reduces ghosting and motion artifacts in [Formula: see text] maps in the presence of unpredictable subject movement and has the potential to be applied to motion-prone patients in the clinic. Our code is available at https://github.com/qinqinyang/MOST-DL.

New methods for robust continuous wave T1ρ relaxation preparation.

Measurement of the longitudinal relaxation time in the rotating frame of reference (T1ρ ) is sensitive to the fidelity of the main imaging magnetic field (B0 ) and that of the RF pulse (B1 ). The purpose of this study was to introduce methods for producing continuous wave (CW) T1ρ contrast with improved robustness against field inhomogeneities and to compare the sensitivities of several existing and the novel T1ρ contrast generation methods with the B0 and B1 field inhomogeneities. Four hard-pulse and four adiabatic CW-T1ρ magnetization preparations were investigated. Bloch simulations and experimental measurements at different spin-lock amplitudes under ideal and non-ideal conditions, as well as theoretical analysis of the hard-pulse preparations, were conducted to assess the sensitivity of the methods to field inhomogeneities, at low (ω1 << ΔB0 ) and high (ω1 >> ΔB0 ) spin-locking field strengths. In simulations, previously reported single-refocus and new triple-refocus hard-pulse and double-refocus adiabatic preparation schemes were found to be the most robust. The mean normalized absolute deviation between the experimentally measured relaxation times under ideal and non-ideal conditions was found to be smallest for the refocused preparation schemes and broadly in agreement with the sensitivities observed in simulations. Experimentally, all refocused preparations performed better than those that were non-refocused. The findings promote the use of the previously reported hard-pulse single-refocus ΔB0 and B1 insensitive T1ρ as a robust method with minimal RF energy deposition. The double-refocus adiabatic B1 insensitive rotation-4 CW-T1ρ preparation offers further improved insensitivity to field variations, but because of the extra RF deposition, may be preferred for ex vivo applications.

A self-compensated spin-locking scheme for quantitative R1ρ dispersion MR imaging in ordered tissues

PurposeTo propose a self-compensated spin-locking (SL) method for quantitative R1ρ dispersion imaging in ordered tissues. MethodsTwo pairs of antiphase rotary-echo SL pulses were proposed in a new scheme with each pairs sandwiching one refocusing RF pulse. This proposed SL method was evaluated by Bloch simulations and experimental studies relative to three prior schemes. Quantitative R1ρR dispersion imaging studies with constant SL duration (TSL = 40 ms) were carried out on an agarose (1–4% w/v) phantom and one in vivo human knee at 3 T, using six SL RF strengths ranging from 50 to 1000 Hz. The performances of these SL schemes were characterized with an average coefficient of variation (CV) of the signal intensities in agarose gels and the sum of squared errors (SSE) for quantifying in vivo R1ρ dispersion of the femoral and tibial cartilage. ResultsThe simulations demonstrate that the proposed SL scheme was less prone to B0 and B1 field inhomogeneities. This theoretical prediction was supported by fewer image banding artifacts and less signal fluctuation signified by a reduced CV (%) on the phantom without R1ρ dispersion (i.e., 4.04 ± 1.36 vs. 18.87 ± 4.46 or 6.66 ± 2.92 or 5.71 ± 2.05 for others), and further by mostly decreased SSE (*10–3) for characterizing R1ρ dispersion of the femoral (i.e., 0.3 vs. 1.2 or 0.4 or 0.1) and tibial (i.e., 0.4 vs. 7.2 or 3.2 or 2.8) cartilage. ConclusionThe proposed SL scheme is less sensitive to B0 and B1 field artifacts for a wide range of SL RF strengths and thus more suitable for quantitative R1ρ dispersion imaging in ordered tissues.

A robust adiabatic constant amplitude spin-lock preparation module for myocardial T1ρ quantification at 3T.

T1ρ quantification has the potential to assess myocardial fibrosis without contrast agent. However, its preparation spin-lock pulse is sensitive to B1 and B0 inhomogeneities, resulting in severe banding artifacts in the heart region, especially at high magnetic field such as 3T. We aimed to design a robust spin-lock (SL) preparation module that can be used in myocardial T1ρ quantification at 3T. We used the tan/tanh pulse to tip up and tip down the magnetization in the spin-lock preparation module (tan/tanh-SL). Bloch simulation was used to optimize pulse shape parameters of the tan/tanh with a pulse duration (Tp ) of 8, 4, and 2ms, respectively. The designed tan/tanh-SL modules were implemented on a 3-T MR scanner. They were evaluated in phantom studies under three different cases of B0 and B1 inhomogeneities, and tested in cardiac T1ρ quantification of healthy volunteers. The performance of the tan/tanh-SL was compared with the composite SL preparation pulses and the commonly used hyperbolic secant pulse for spin-lock (HS-SL). Feasible pulse shape parameters were obtained using Bloch simulation. Compared with HS-SL, the quantification error of tan/tanh-SL was reduced by 27.7% for Tp = 8ms (mean ∆Q = 126.15 vs. 174.42) and 75.6% for Tp = 4ms (mean ∆Q = 136.65 vs. 559.53). In the phantom study, tan/tanh-SL was less sensitive to B1 and B0 inhomogeneity compared with composite SL pulses and HS-SL. In cardiac T1ρ quantification, the T1ρ maps using tan/tanh-SL showed fewer banding artifacts than using composite SL pulses and HS-SL. The proposed tan/tanh-SL preparation module greatly improves the robustness to B0 and B1 field inhomogeneities and can be used in cardiac T1ρ quantification at 3T.

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

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

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

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