Magnetic resonance fingerprinting.
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ABSTRACT:
SUBMITTER: Ma D
PROVIDER: S-EPMC3602925 | biostudies-literature | 2013 Mar
REPOSITORIES: biostudies-literature
Publications
Magnetic resonance fingerprinting.
Ma Dan D Gulani Vikas V Seiberlich Nicole N Liu Kecheng K Sunshine Jeffrey L JL Duerk Jeffrey L JL Griswold Mark A MA
Nature 20130301 7440
Magnetic resonance is an exceptionally powerful and versatile measurement technique. The basic structure of a magnetic resonance experiment has remained largely unchanged for almost 50 years, being mainly restricted to the qualitative probing of only a limited set of the properties that can in principle be accessed by this technique. Here we introduce an approach to data acquisition, post-processing and visualization--which we term 'magnetic resonance fingerprinting' (MRF)--that permits the simu ...[more]
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Project description:PurposeTo study the effects of magnetization transfer (MT, in which a semi-solid spin pool interacts with the free pool), in the context of magnetic resonance fingerprinting (MRF).MethodsSimulations and phantom experiments were performed to study the impact of MT on the MRF signal and its potential influence on T1 and T2 estimation. Subsequently, an MRF sequence implementing off-resonance MT pulses and a dictionary with an MT dimension, generated by incorporating a two-pool model, were used to estimate the fractional pool size in addition to the B1+ , T1 , and T2 values. The proposed method was evaluated in the human brain.ResultsSimulations and phantom experiments showed that an MRF signal obtained from a cross-linked bovine serum sample is influenced by MT. Using a dictionary based on an MT model, a better match between simulations and acquired MR signals can be obtained (NRMSE 1.3% vs. 4.7%). Adding off-resonance MT pulses can improve the differentiation of MT from T1 and T2 . In vivo results showed that MT affects the MRF signals from white matter (fractional pool-size ~16%) and gray matter (fractional pool-size ~10%). Furthermore, longer T1 (~1060 ms vs. ~860 ms) and T2 values (~47 ms vs. ~35 ms) can be observed in white matter if MT is accounted for.ConclusionOur experiments demonstrated a potential influence of MT on the quantification of T1 and T2 with MRF. A model that encompasses MT effects can improve the accuracy of estimated relaxation parameters and allows quantification of the fractional pool size.
| S-EPMC7083689 | biostudies-literature
Project description:PurposeDevelop a method for rigid body motion-corrected magnetic resonance fingerprinting (MRF).MethodsMRF has shown some robustness to abrupt motion toward the end of the acquisition. Here, we study the effects of different types of rigid body motion during the acquisition on MRF and propose a novel approach to correct for this motion. The proposed method (MC-MRF) follows 4 steps: (1) sliding window reconstruction is performed to produce high-quality auxiliary dynamic images; (2) rotation and translation motion is estimated from the dynamic images by image registration; (3) estimated motion is used to correct acquired k-space data with corresponding rotations and phase shifts; and (4) motion-corrected data are reconstructed with low-rank inversion. MC-MRF was validated in a standard T1 /T2 phantom and 2D in vivo brain acquisitions in 7 healthy subjects. Additionally, the effect of through-plane motion in 2D MC-MRF was investigated.ResultsSimulation results show that motion in MRF can introduce artifacts in T1 and T2 maps, depending when it occurs. MC-MRF improved parametric map quality in all phantom and in vivo experiments with in-plane motion, comparable to the no-motion ground truth. Reduced parametric map quality, even after motion correction, was observed for acquisitions with through-plane motion, particularly for smaller structures in T2 maps.ConclusionHere, a novel method for motion correction in MRF (MC-MRF) is proposed, which improves parametric map quality and accuracy in comparison to no-motion correction approaches. Future work will include validation of 3D MC-MRF to enable also through-plane motion correction.
| S-EPMC6519164 | biostudies-literature
Project description:PURPOSE:Cardiac magnetic resonance fingerprinting (cMRF) has been recently introduced to simultaneously provide T1 , T2 , and M0 maps. Here, we develop a 3-point Dixon-cMRF approach to enable simultaneous water specific T1 , T2 , and M0 mapping of the heart and fat fraction (FF) estimation in a single breath-hold scan. METHODS:Dixon-cMRF is achieved by combining cMRF with several innovations that were previously introduced for other applications, including a 3-echo GRE acquisition with golden angle radial readout and a high-dimensional low-rank tensor constrained reconstruction to recover the highly undersampled time series images for each echo. Water-fat separation of the Dixon-cMRF time series is performed to allow for water- and fat-specific T1 , T2 , and M0 estimation, whereas FF estimation is extracted from the M0 maps. Dixon-cMRF was evaluated in a standardized T1 -T2 phantom, in a water-fat phantom, and in healthy subjects in comparison to current clinical standards: MOLLI, SASHA, T2 -GRASE, and 6-point Dixon proton density FF (PDFF) mapping. RESULTS:Dixon-cMRF water T1 and T2 maps showed good agreement with reference T1 and T2 mapping techniques (R2 > 0.99 and maximum normalized RMSE ~5%) in a standardized phantom. Good agreement was also observed between Dixon-cMRF FF and reference PDFF (R2 > 0.99) and between Dixon-cMRF water T1 and T2 and water selective T1 and T2 maps (R2 > 0.99) in a water-fat phantom. In vivo Dixon-cMRF water T1 values were in good agreement with MOLLI and water T2 values were slightly underestimated when compared to T2 -GRASE. Average myocardium septal T1 values were 1129 ± 38 ms, 1026 ± 28 ms, and 1045 ± 32 ms for SASHA, MOLLI, and the proposed water Dixon-cMRF. Average T2 values were 51.7 ± 2.2 ms and 42.8 ± 2.6 ms for T2 -GRASE and water Dixon-cMRF, respectively. Dixon-cMRF FF maps showed good agreement with in vivo PDFF measurements (R2 > 0.98) and average FF in the septum was measured at 1.3%. CONCLUSION:The proposed Dixon-cMRF allows to simultaneously quantify myocardial water T1 , water T2 , and FF in a single breath-hold scan, enabling multi-parametric T1 , T2 , and fat characterization. Moreover, reduced T1 and T2 quantification bias caused by water-fat partial volume was demonstrated in phantom experiments.
| S-EPMC7064906 | biostudies-literature
Project description:T1ρ relaxation imaging is a quantitative imaging technique that has been used to assess cartilage integrity, liver fibrosis, tumors, cardiac infarction, and Alzheimer's disease. T1 , T2 , and T1ρ relaxation time constants have each demonstrated different degrees of sensitivity to several markers of fibrosis and inflammation, allowing for a potential multi-parametric approach to tissue quantification. Traditional magnetic resonance fingerprinting (MRF) has been shown to provide quick, quantitative mapping of T1 and T2 relaxation time constants. In this study, T1ρ relaxation is added to the MRF framework using spin lock preparations. An MRF sequence involving an RF-spoiled sequence with TR , flip angle, T1ρ , and T2 preparation variation is described. The sequence is then calibrated against conventional T1 , T2 , and T1ρ relaxation mapping techniques in agar phantoms and the abdomens of four healthy volunteers. Strong intraclass correlation coefficients (ICC > 0.9) were found between conventional and MRF sequences in phantoms and also in healthy volunteers (ICC > 0.8). The highest ICC correlation values were seen in T1 , followed by T1ρ and then T2 . In this study, T1ρ relaxation has been incorporated into the MRF framework by using spin lock preparations, while still fitting for T1 and T2 relaxation time constants. The acquisition of these parameters within a single breath hold in the abdomen alleviates the issues of movement between breath holds in conventional techniques.
| S-EPMC8818303 | biostudies-literature
Project description:PurposeThe goal of this work is to demonstrate a method for the simultaneous acquisition of proton multiparametric maps (T1 , T2 , and proton density) and sodium density images in 1 single scan. We hope that the development of such capabilities will help to ease the implementation of sodium MRI in clinical trials and provide more opportunities for researchers to investigate metabolism through sodium MRI.MethodsWe developed a sequence based on magnetic resonance fingerprinting (MRF), which contains interleaved proton (1 H) and sodium (23 Na) excitations followed by a simultaneous center-out radial readout for both nuclei. The receive chain of a 7T scanner was modified to enable simultaneous acquisition of 1 H and 23 Na signal. The obtained signal-to-noise ratio (SNR) was evaluated, and the accuracy of both proton T1 , T2 , and B1+ and sodium density maps were verified in phantoms. Finally, the method was demonstrated in 2 healthy subjects.ResultsThe SNR obtained using the simultaneous measurement was almost identical to single-nucleus measurements (<1% change). Similarly, the proton T1 and T2 maps remained stable (normalized root mean square error in T1 ≈ 2.2%, in T2 ≈ 1.4%, and B1+ ≈ 5.4%), which indicates that the proposed sequence and hardware have no significant effects on the signal from either nucleus. In vivo measurements corroborated these results and demonstrated the feasibility of our method for in vivo application.ConclusionsWith the proposed approach, we were able to simultaneously acquire sodium density images in addition to proton T1 , T2 , and B1+ maps as well as proton density images.
| S-EPMC7047525 | biostudies-literature
Project description:PURPOSE:To explore the motion sensitivity of magnetic resonance fingerprinting (MRF), we performed experiments with different types of motion at various time intervals during multiple scans. Additionally, we investigated the possibility to correct the motion artifacts based on redundancy in MRF data. METHODS:A radial version of the FISP-MRF sequence was used to acquire one transverse slice through the brain. Three subjects were instructed to move in different patterns (in-plane rotation, through-plane wiggle, complex movements, adjust head position, and pretend itch) during different time intervals. The potential to correct motion artifacts in MRF by removing motion-corrupted data points from the fingerprints and dictionary was evaluated. RESULTS:Morphological structures were well preserved in multi-parametric maps despite subject motion. Although the bulk T1 values were not significantly affected by motion, fine structures were blurred when in-plane motion was present during the first part of the scan. On the other hand, T2 values showed a considerable deviation from the motion-free results, especially when through-plane motion was present in the middle of the scan (-44% on average). Explicitly removing the motion-corrupted data from the scan partially restored the T2 values (-10% on average). CONCLUSION:Our experimental results showed that different kinds of motion have distinct effects on the precision and effective resolution of the parametric maps measured with MRF. Although MRF-based acquisitions can be relatively robust to motion effects occurring at the beginning or end of the sequence, relying on redundancy in the data alone is not sufficient to assure the accuracy of the multi-parametric maps in all cases.
| S-EPMC6215476 | biostudies-literature
Project description:Magnetic resonance fingerprinting (MRF) was recently proposed as a novel strategy for MR data acquisition and analysis. A variant of MRF called vascular MRF (vMRF) followed, that extracted maps of three parameters of physiological importance: cerebral oxygen saturation (SatO2), mean vessel radius and cerebral blood volume (CBV). However, this estimation was based on idealized 2-dimensional simulations of vascular networks using random cylinders and the empirical Bloch equations convolved with a diffusion kernel. Here we focus on studying the vascular MR fingerprint using real mouse angiograms and physiological values as the substrate for the MR simulations. The MR signal is calculated ab initio with a Monte Carlo approximation, by tracking the accumulated phase from a large number of protons diffusing within the angiogram. We first study the identifiability of parameters in simulations, showing that parameters are fully estimable at realistically high signal-to-noise ratios (SNR) when the same angiogram is used for dictionary generation and parameter estimation, but that large biases in the estimates persist when the angiograms are different. Despite these biases, simulations show that differences in parameters remain estimable. We then applied this methodology to data acquired using the GESFIDE sequence with SPIONs injected into 9 young wild type and 9 old atherosclerotic mice. Both the pre injection signal and the ratio of post-to-pre injection signals were modeled, using 5-dimensional dictionaries. The vMRF methodology extracted significant differences in SatO2, mean vessel radius and CBV between the two groups, consistent across brain regions and dictionaries. Further validation work is essential before vMRF can gain wider application.
| S-EPMC5368007 | biostudies-literature
Project description:PURPOSE:To estimate multiple components within a single voxel in magnetic resonance fingerprinting when the number and types of tissues comprising the voxel are not known a priori. THEORY:Multiple tissue components within a single voxel are potentially separable with magnetic resonance fingerprinting as a result of differences in signal evolutions of each component. The Bayesian framework for inverse problems provides a natural and flexible setting for solving this problem when the tissue composition per voxel is unknown. Assuming that only a few entries from the dictionary contribute to a mixed signal, sparsity-promoting priors can be placed upon the solution. METHODS:An iterative algorithm is applied to compute the maximum a posteriori estimator of the posterior probability density to determine the magnetic resonance fingerprinting dictionary entries that contribute most significantly to mixed or pure voxels. RESULTS:Simulation results show that the algorithm is robust in finding the component tissues of mixed voxels. Preliminary in vivo data confirm this result, and show good agreement in voxels containing pure tissue. CONCLUSIONS:The Bayesian framework and algorithm shown provide accurate solutions for the partial-volume problem in magnetic resonance fingerprinting. The flexibility of the method will allow further study into different priors and hyperpriors that can be applied in the model. Magn Reson Med 80:159-170, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
| S-EPMC5876128 | biostudies-literature
Project description:This study introduces a technique for simultaneous multislice (SMS) cardiac magnetic resonance fingerprinting (cMRF), which improves the slice coverage when quantifying myocardial T1, T2 , and M0 . The single-slice cMRF pulse sequence was modified to use multiband (MB) RF pulses for SMS imaging. Different RF phase schedules were used to excite each slice, similar to POMP or CAIPIRINHA, which imparts tissues with a distinguishable and slice-specific magnetization evolution over time. Because of the high net acceleration factor (R = 48 in plane combined with the slice acceleration), images were first reconstructed with a low rank technique before matching data to a dictionary of signal timecourses generated by a Bloch equation simulation. The proposed method was tested in simulations with a numerical relaxation phantom. Phantom and in vivo cardiac scans of 10 healthy volunteers were also performed at 3 T. With single-slice acquisitions, the mean relaxation times obtained using the low rank cMRF reconstruction agree with reference values. The low rank method improves the precision in T1 and T2 for both single-slice and SMS cMRF, and it enables the acquisition of maps with fewer artifacts when using SMS cMRF at higher MB factors. With this technique, in vivo cardiac maps were acquired from three slices simultaneously during a breathhold lasting 16 heartbeats. SMS cMRF improves the efficiency and slice coverage of myocardial T1 and T2 mapping compared with both single-slice cMRF and conventional cardiac mapping sequences. Thus, this technique is a first step toward whole-heart simultaneous T1 and T2 quantification with cMRF.
| S-EPMC7755311 | biostudies-literature
Project description:Magnetic Resonance Fingerprinting (MRF) is an MRI-based method that can provide quantitative maps of multiple tissue properties simultaneously from a single rapid acquisition. Tissue property maps are generated by matching the complex signal evolutions collected at the scanner to a dictionary of signals derived using Bloch equation simulations. However, in some circumstances, the process of dictionary generation and signal matching can be time-consuming, reducing the utility of this technique. Recently, several groups have proposed using machine learning to accelerate the extraction of quantitative maps from MRF data. This article will provide an overview of current research that combines MRF and machine learning, as well as present original research demonstrating how machine learning can speed up dictionary generation for cardiac MRF.
| S-EPMC7595247 | biostudies-literature