What is the optimal schedule for multiparametric MRS? A magnetic resonance fingerprinting perspective.
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ABSTRACT:
SUBMITTER: Kulpanovich A
PROVIDER: S-EPMC9244865 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description: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 simultaneous non-invasive quantification of multiple important properties of a material or tissue. MRF thus provides an alternative way to quantitatively detect and analyse complex changes that can represent physical alterations of a substance or early indicators of disease. MRF can also be used to identify the presence of a specific target material or tissue, which will increase the sensitivity, specificity and speed of a magnetic resonance study, and potentially lead to new diagnostic testing methodologies. When paired with an appropriate pattern-recognition algorithm, MRF inherently suppresses measurement errors and can thus improve measurement accuracy.
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Project description:Magnetic resonance (MR) fingerprinting is a new quantitative imaging paradigm, which simultaneously acquires multiple MR tissue parameter maps in a single experiment. In this paper, we present an estimation-theoretic framework to perform experiment design for MR fingerprinting. Specifically, we describe a discrete-time dynamic system to model spin dynamics, and derive an estimation-theoretic bound, i.e., the Cramér-Rao bound, to characterize the signal-to-noise ratio (SNR) efficiency of an MR fingerprinting experiment. We then formulate an optimal experiment design problem, which determines a sequence of acquisition parameters to encode MR tissue parameters with the maximal SNR efficiency, while respecting the physical constraints and other constraints from the image decoding/reconstruction process. We evaluate the performance of the proposed approach with numerical simulations, phantom experiments, and in vivo experiments. We demonstrate that the optimized experiments substantially reduce data acquisition time and/or improve parameter estimation. For example, the optimized experiments achieve about a factor of two improvement in the accuracy of T2 maps, while keeping similar or slightly better accuracy of T1 maps. Finally, as a remarkable observation, we find that the sequence of optimized acquisition parameters appears to be highly structured rather than randomly/pseudo-randomly varying as is prescribed in the conventional MR fingerprinting experiments.
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2018-12-01 | GSE101908 | GEO
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.
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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.
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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.
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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.
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Project description:PurposeThe clinical utility of multiparametric magnetic resonance imaging (mpMRI) for the detection and localization of prostate cancer (PCa) has been evaluated and validated. However, the implementation of mpMRI into the clinical practice remains some burden of cost and availability for patients and society. We aimed to predict the results of prostate mpMRI using the clinical parameters and multivariable model to reduce unnecessary mpMRI scans.MethodsWe retrospectively identified 784 men who underwent mpMRI scans and subsequent prostate biopsy between 2016 and 2020 according to the inclusion criterion. The cohort was split into a training cohort of 548 (70%) patients and a validation cohort of 236 (30%) patients. Clinical parameters including age, prostate-specific antigen (PSA) derivates, and prostate volume (PV) were assessed as the predictors of mpMRI results. The mpMRI results were divided into groups according to the reports: "negative", "equivocal", and "suspicious" for the presence of PCa.ResultsUnivariate analysis showed that the total PSA (tPSA), free PSA (fPSA), PV, and PSA density (PSAD) were significant predictors for suspicious mpMRI (P < 0.05). The PSAD (AUC = 0.77) and tPSA (AUC = 0.74) outperformed fPSA (AUC = 0.68) and PV (AUC = 0.62) in the prediction of the mpMRI results. The multivariate model (AUC = 0.80) had a similar diagnostic accuracy with PSAD (P = 0.108), while higher than tPSA (P = 0.024) in predicting the mpMRI results. The multivariate model illustrated a better calibration and substantial improvement in the decision curve analysis (DCA) at a threshold above 20%. Using the PSAD with a 0.13 ng/ml2 cut-off could spare the number of mpMRI scans by 20%, keeping a 90% sensitivity in the prediction of suspicious MRI-PCa and missing three (3/73, 4%) clinically significant PCa cases. At the same sensitivity level, the multivariate model with a 32% cut-off could spare the number of mpMRI scans by 27%, missing only one (1/73, 1%) clinically significant PCa case.ConclusionOur multivariate model could reduce the number of unnecessary mpMRI scans without comprising the diagnostic ability of clinically significant PCa. Further prospective validation is required.
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Project description:Background:Current magnetic resonance imaging (MRI) of pancreatic disease is qualitative in nature. Quantitative imaging offers several advantages, including increased reproducibility and sensitivity to detect mild or diffuse disease. The role of multiparametric mapping MRI in characterizing various tissue types in pancreatic disease such as chronic pancreatitis (CP) and pancreatic ductal adenocarcinoma (PDAC) has rarely been evaluated. Purpose:To evaluate the feasibility of multiparametric mapping [T1, T2, and apparent diffusion coefficient (ADC)] in defining tissue characteristics that occur in CP and PDAC to improve disease diagnosis. Materials and Methods:Pancreatic MRI was performed in 17 patients with PDAC undergoing therapy, 7 patients with CP, and 29 healthy volunteers with no pancreatic disease. T1 modified Look-Locker Inversion Recovery (T1 MOLLI), T2-prepared gradient-echo, and multi-slice single-shot echo-planar diffusion weighted imaging (SS-EPI DWI) sequences were used for data acquisition. Regions of interest (ROIs) of pancreas in PDAC, CP, and control subjects were outlined by an experienced radiologist. One-way analysis of variance (ANOVA) was used to compare the difference between groups and regions of the pancreas, and Tukey tests were used for multiple comparison testing within groups. Receiver operator characteristic (ROC) curves were analyzed, and the areas under the curves (AUCs) were calculated using single parameter and combined parameters, respectively. Results:T1, T2, and ADC values of the entire pancreas among PDAC, CP, and control subjects; and between upstream and downstream portions of the pancreas in PDAC patients were all significantly different (p < 0.05). The AUC values were 0.90 for T1, 0.55 for T2, and 0.71 for ADC for independent prediction of PDAC. By combining T1, T2, and ADC, the AUC value was 0.94 (sensitivity 91.54%, specificity 85.81%, 95% CI: 0.92-0.96), which yielded higher accuracy than any one parameter only (p < 0.001). Conclusion:Multiparametric mapping MRI is feasible for the evaluation of the differences between PDAC, CP, and normal pancreas tissues. The combination of multiple parameters of T1, T2, and ADC provides a higher accuracy than any single parameter alone in tissue characterization of the pancreas.
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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