Detecting Early-Stage Liver Fibrosis Using Macromolecular Proton Fraction Mapping Based on Spin-Lock MRI: Preliminary Observations.

BACKGROUND: Liver fibrosis is characterized by macromolecule depositions. Recently, a novel technology termed macromolecular proton fraction quantification based on spin-lock magnetic resonance imaging (MPF-SL) is reported to measure macromolecule levels. HYPOTHESIS: MPF-SL can detect early-stage liver fibrosis by measuring macromolecule levels in the liver. STUDY TYPE: Retrospective. SUBJECTS: Fifty-five participants, including 22 with no fibrosis (F0) and 33 with early-stage fibrosis (F1-2), were recruited. FIELD STRENGTH/SEQUENCE: 3 T; two-dimensional (2D) MPF-SL turbo spin-echo sequence, 2D spin-lock T1rho turbo spin-echo sequence, and multi-slice 2D gradient echo sequence. ASSESSMENT: Macromolecular proton fraction (MPF), T1rho, liver iron concentration (LIC), and fat fraction (FF) biomarkers were quantified within regions of interest. STATISTICAL TESTS: Group comparison of the biomarkers using Mann-Whitney U tests; correlation between the biomarkers assessed using Spearman's rank correlation coefficient and linear regression with goodness-of-fit; fibrosis stage differentiation using receiver operating characteristic curve (ROC) analysis. P-value < 0.05 was considered statistically significant. RESULTS: Average T1rho was 41.76 ± 2.94 msec for F0 and 41.15 ± 3.73 msec for F1-2 (P = 0.60). T1rho showed nonsignificant correlation with either liver fibrosis (ρ = -0.07; P = 0.61) or FF (ρ = -0.14; P = 0.35) but indicated a negative correlation with LIC (ρ = -0.66). MPF was 4.73 ± 0.45% and 5.65 ± 0.81% for F0 and F1-2 participants, respectively. MPF showed a positive correlation with liver fibrosis (ρ = 0.59), and no significant correlations with LIC (ρ = 0.02; P = 0.89) or FF (ρ = 0.05; P = 0.72). The area under the ROC curve was 0.85 (95% confidence interval [CI] 0.75-0.95) and 0.55 (95% CI 0.39-0.71; P = 0.55) for MPF and T1rho to discriminate between F0 and F1-2 fibrosis, respectively. DATA CONCLUSION: MPF-SL has the potential to diagnose early-stage liver fibrosis and does not appear to be confounded by either LIC or FF. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 3.

Characterization and correction of the effects of hepatic iron on T1ρ relaxation in the liver at 3.0T.

PURPOSE: Quantitative T1ρ imaging is an emerging technique to assess the biochemical properties of tissues. In this paper, we report our observation that liver iron content (LIC) affects T1ρ quantification of the liver at 3.0T field strength and develop a method to correct the effect of LIC. THEORY AND METHODS: On-resonance R1ρ (1/T1ρ ) is mainly affected by the intrinsic R2 (1/T2 ), which is influenced by LIC. As on-resonance R1ρ is closely related to the Carr-Purcell-Meiboom-Gill (CPMG) R2 , and because the calibration between CPMG R2 and LIC has been reported at 1.5T, a correction method was proposed to correct the R2 contribution to the R1ρ . The correction coefficient was obtained from the calibration results and related transformed factors. To compensate for the difference between CPMG R2 and R1ρ , a scaling factor was determined using the values of CPMG R2 and R1ρ , obtained simultaneously from a single breath-hold from volunteers. The livers of 110 subjects were scanned to validate the correction method. RESULTS: LIC was significantly correlated with R1ρ in the liver. However, when the proposed correction method was applied to R1ρ , LIC and the iron-corrected R1ρ were not significantly correlated. CONCLUSION: LIC can affect T1ρ in the liver. We developed an iron-correction method for the quantification of T1ρ in the liver at 3.0T.

Macromolecular proton fraction mapping based on spin-lock magnetic resonance imaging.

PURPOSE: In MRI, the macromolecular proton fraction (MPF) is a key parameter of magnetization transfer (MT). It represents the relative amount of immobile protons associated with semi-solid macromolecules involved in MT with free water protons. We aim to quantify MPF based on spin-lock MRI and explore its advantages over the existing MPF-mapping methods. METHODS: In the proposed method, termed MPF quantification based on spin-lock (MPF-SL), off-resonance spin-lock is used to sensitively measure the MT effect. MPF-SL is designed to measure a relaxation rate (Rmpfsl ) that is specific to the MT effect by removing the R1ρ relaxation due to the mobile water and chemical exchange pools. A theory is derived to quantify MPF from the measured Rmpfsl . No prior knowledge of tissue relaxation parameters, including T1 or T2 , is needed to quantify MPF using MPF-SL. The proposed approach is validated with Bloch-McConnell simulations, phantom, and in vivo liver studies at 3.0T. RESULTS: Both Bloch-McConnell simulations and phantom experiments show that MPF-SL is insensitive to variations of the mobile water pool and the chemical exchange pool. MPF-SL is specific to the MT effect and can measure MPF reliably. In vivo liver studies show that MPF-SL can be used to detect collagen deposition in patients with liver fibrosis. CONCLUSION: A novel MPF imaging method based on spin-lock MRI is proposed. The confounding factors are removed, and the measurement is specific to the MT effect. It holds promise for MPF-sensitive diagnostic imaging in clinical settings.

Probing chemical exchange using quantitative spin-lock R1ρ asymmetry imaging with adiabatic RF pulses.

PURPOSE: CEST is commonly used to probe the effects of chemical exchange. Although R1ρ asymmetry quantification has also been described as a promising option for detecting the effects of chemical exchanges, the existing acquisition approaches are highly susceptible to B1 RF and B0 field inhomogeneities. To address this problem, we report a new R1ρ asymmetry imaging approach, AC-iTIP, which is based on the previously reported techniques of irradiation with toggling inversion preparation (iTIP) and adiabatic continuous wave constant amplitude spin-lock RF pulses (ACCSL). We also derived the optimal spin-lock RF pulse B1 amplitude that yielded the greatest R1ρ asymmetry. METHODS: Bloch-McConnell simulations were used to verify the analytical formula derived for the optimal spin-lock RF pulse B1 amplitude. The performance of the AC-iTIP approach was compared to that of the iTIP approach based on hard RF pulses and the R1ρ -spectrum acquired using adiabatic RF pulses with the conventional fitting method. Comparisons were performed using Bloch-McConnell simulations, phantom, and in vivo experiments at 3.0T. RESULTS: The analytical prediction of the optimal B1 was validated. Compared to the other 2 approaches, the AC-iTIP approach was more robust under the influences of B1 RF and B0 field inhomogeneities. A linear relationship was observed between the measured R1ρ asymmetry and the metabolite concentration. CONCLUSION: The AC-iTIP approach could probe the chemical exchange effect more robustly than the existing R1ρ asymmetry acquisition approaches. Therefore, AC-iTIP is a promising technique for metabolite imaging based on the chemical exchange effect.

On-resonance and off-resonance continuous wave constant amplitude spin-lock and T1ρ quantification in the presence of B1 and B0 inhomogeneities.

Spin-lock MRI is a valuable diagnostic imaging technology, as it can be used to probe the macromolecule environment of tissues. Quantitative T1ρ imaging is one application of spin-lock MRI that is reported to be promising for a number of clinical applications. Spin-lock is often performed with a continuous RF wave at a constant RF amplitude either on resonance or off resonance. However, both on- and off-resonance spin-lock approaches are susceptible to B1 and B0 inhomogeneities, which results in image artifacts and quantification errors. In this work, we report a continuous wave constant amplitude spin-lock approach that can achieve negligible image artifacts in the presence of B1 and B0 inhomogeneities for both on- and off-resonance spin-lock. Under the adiabatic condition, by setting the maximum B1 amplitude of the adiabatic pulses equal to the B1 amplitude of spin-lock RF pulse, the spins are ensured to align along the effective field throughout the spin-lock process. We show that this results in simultaneous compensation of B1 and B0 inhomogeneities for both on- and off-resonance spin-lock. The relaxation effect during the entire adiabatic half passage (AHP) and reverse AHP, and the stationary solution of the Bloch-McConnell equation present at off-resonance frequency offset, are considered in the revised relaxation model. We demonstrate that these factors create a direct current component to the conventional relaxation model. In contrast to the previously reported dual-acquisition method, the revised relaxation model just requires one acquisition to perform quantification. The simulation, phantom, and in vivo experiments demonstrate that the proposed approach achieves superior image quality compared with the existing methods, and the revised relaxation model can perform T1ρ quantification with one acquisition instead of two.

Uncertainty-weighted Multi-tasking for T1ρ and T2 Mapping in the Liver with Self-supervised Learning.

Multi-parametric mapping of MRI relaxations in liver has the potential of revealing pathological information of the liver. A self-supervised learning based multi-parametric mapping method is proposed to map T1ρ and T2 simultaneously, by utilising the relaxation constraint in the learning process. Data noise of different mapping tasks is utilised to make the model uncertainty-aware, which adaptively weight different mapping tasks during learning. The method was examined on a dataset of 51 patients with non-alcoholic fatter liver disease. Results showed that the proposed method can produce comparable parametric maps to the traditional multi-contrast pixel wise fitting method, with a reduced number of images and less computation time. The uncertainty weighting also improves the model performance. It has the potential of accelerating MRI quantitative imaging.

Uncertainty-aware self-supervised neural network for liverT1ρmapping with relaxation constraint.

Objective.T1ρmapping is a promising quantitative MRI technique for the non-invasive assessment of tissue properties. Learning-based approaches can mapT1ρfrom a reduced number ofT1ρweighted images but requires significant amounts of high-quality training data. Moreover, existing methods do not provide the confidence level of theT1ρestimation. We aim to develop a learning-based liverT1ρmapping approach that can mapT1ρwith a reduced number of images and provide uncertainty estimation.Approach. We proposed a self-supervised neural network that learns aT1ρmapping using the relaxation constraint in the learning process. Epistemic uncertainty and aleatoric uncertainty are modelled for theT1ρquantification network to provide a Bayesian confidence estimation of theT1ρmapping. The uncertainty estimation can also regularize the model to prevent it from learning imperfect data. Main results. We conducted experiments onT1ρdata collected from 52 patients with non-alcoholic fatty liver disease. The results showed that when only collecting twoT1ρ-weighted images, our method outperformed the existing methods forT1ρquantification of the liver. Our uncertainty estimation can further regularize the model to improve the performance of the model and it is consistent with the confidence level of liverT1ρvalues.Significance. Our method demonstrates the potential for accelerating theT1ρmapping of the liver by using a reduced number of images. It simultaneously provides uncertainty ofT1ρquantification which is desirable in clinical applications.

Test-retest repeatability of T1rho (T1ρ) MR imaging in the head and neck.

PURPOSE: T1rho imaging is a new quantitative MRI sequence for head and neck cancer and the repeatability for this region is unknown. This study aimed to evaluate the repeatability of quantitative T1rho imaging in the head and neck. MATERIALS AND METHODS: T1rho imaging of the head and neck was prospectively performed in 15 healthy participants on three occasions. Scan 1 and 2 were performed with a time interval of 30 minutes (intra-session) and scan 3 was performed 14 days later (inter-session). T1rho values for normal tissues (parotid glands, palatine tonsils, pterygoid muscles, and tongue) were obtained on each scan. Intra-class coefficients (ICCs), within-subject coefficient of variances (wCoVs), and repeatability coefficient (RCs) of the intra-session scan (scan 1 vs 2) and inter-session scan (scan 1 vs 3) for the normal tissues were calculated. RESULTS: The ICCs of T1rho values for normal tissues were almost perfect (0.83-0.97) for intra-session scans and were substantial (0.71-0.80) for inter-session scans. The wCoVs showed a small range (2.46%-3.30%) for intra-session scans, and slightly greater range (3.27%-6.51%) for inter-session scan. The greatest and lowest wCoVs of T1rho were found in the parotid gland and muscles, respectively. The T1rho RCs varied for all tissues between intra- and inter- sessions, and the greatest RC of 10.07 msec was observed for parotid gland on inter-session scan. CONCLUSION: T1rho imaging is a repeatable quantitative MRI sequence in the head and neck but variances of T1rho values among tissues should be take into account during analysis.