Chemical-shift encoding-based water-fat separation with multifrequency fat spectrum modeling in spin-lock MRI

Comparative analysis of hepatic fat quantification across 5 T, 3 T and 1.5 T: A study on consistency and feasibility

OBJECTIVES

Magnetic resonance imaging (MRI) is a critical noninvasive technique for evaluating liver steatosis, with efficient and precise fat quantification being essential for diagnosing liver diseases. This study leverages 5 T ultra-high-field MRI to demonstrate the clinical significance of liver fat quantification, and explores the consistency and accuracy of the Proton Density Fat Fraction (PDFF) in the liver across different magnetic field strengths and measurement methodologies.

METHODS

The study involved phantoms with lipid contents ranging from 0 % to 30 % and 35 participants (21 females, 14 males; average age 30.17 ± 13.98 years, body mass index 25.84 ± 4.76, waist-hip ratio 0.84 ± 0.09). PDFF measurements were conducted using chemical shift encoded (CSE) MRI at 5 T, 3 T, and 1.5 T, alongside magnetic resonance spectroscopy (MRS) at 5 T and 1.5 T for both liver and phantoms, analyzed using jMRUI software. The MRS-derived PDFF values served as the reference standard. Repeatability of 5 T MRI measurements was assessed through correlation analysis, while accuracy was evaluated using linear regression analysis against the reference standards.

RESULTS

The CSE-PDFF measurements at 5 T demonstrated strong consistency with those at 3 T and 1.5 T, showing high intraclass correlation coefficients (ICC) of 0.988 and 0.980, respectively (all p < 0.001). There was also significant consistency across ROIs within liver lobes, with ICC values ranging from 0.975 to 0.986 (all p < 0.001). MRS-PDFF measurements for both phantoms and liver at 5 T and 1.5 T exhibited substantial agreement, with ICC values of 0.996 and 0.980, respectively (all p < 0.001). Particularly, ICC values for ROIs in the liver ranged from 0.963 to 0.990 (all p < 0.001). Despite overall agreement, statistically significant differences were noted in specific ROIs within the liver lobes (p = 0.004 and 0.012). The CSE and MRS PDFF measurements at 5 T displayed strong consistency, with an ICC of 0.988 (p < 0.001), and significant agreement was also found between 5 T CSE and 1.5 T MRS PDFF measurements, with an ICC of 0.978 (p < 0.001). Agreement was significant within the ROIs of the liver lobes on the same platform at 5 T, with ICC values ranging from 0.986 to 0.991 (all p < 0.001).

CONCLUSION

PDFF measurements at 5 T MR imaging exhibited both accuracy and repeatability, indicating that 5 T imaging provides reliable quantification of liver fat content and shows substantial potential for clinical diagnostic applications.

Prediction of MRI R 2 * $$ {\mathrm{R}}_2^{\ast } $$ relaxometry in the presence of hepatic steatosis by Monte Carlo simulations

To develop Monte Carlo simulations to predict the relationship ofR 2 * $$ {\mathrm{R}}_2^{\ast } $$ with liver fat content at 1.5 T and 3.0 T. For various fat fractions (FFs) from 1% to 25%, four types of virtual liver models were developed by incorporating the size and spatial distribution of fat droplets. Magnetic fields were then generated under different fat susceptibilities at 1.5 T and 3.0 T, and proton movement was simulated for phase accrual and MRI signal synthesis. The synthesized signal was fit to single-peak and multi-peak fat signal models forR 2 * $$ {\mathrm{R}}_2^{\ast } $$ and proton density fat fraction (PDFF) predictions. In addition, the relationships betweenR 2 * $$ {\mathrm{R}}_2^{\ast } $$ and PDFF predictions were compared with in vivo calibrations and Bland-Altman analysis was performed to quantitatively evaluate the effects of these components (type of virtual liver model, fat susceptibility, and fat signal model) onR 2 * $$ {\mathrm{R}}_2^{\ast } $$ predictions. A virtual liver model with realistic morphology of fat droplets was demonstrated, andR 2 * $$ {\mathrm{R}}_2^{\ast } $$ and PDFF values were predicted by Monte Carlo simulations at 1.5 T and 3.0 T.R 2 * $$ {\mathrm{R}}_2^{\ast } $$ predictions were linearly correlated with PDFF, while the slope was unaffected by the type of virtual liver model and increased as fat susceptibility increased. Compared with in vivo calibrations, the multi-peak fat signal model showed superior performance to the single-peak fat signal model, which yielded an underestimation of liver fat. TheR 2 * $$ {\mathrm{R}}_2^{\ast } $$ -PDFF relationships by simulations with fat susceptibility of 0.6 ppm and the multi-peak fat signal model wereR 2 * = 0.490 × PDFF + 28.0 $$ {\mathrm{R}}_2^{\ast }=0.490\times \mathrm{PDFF}+28.0 $$ (R 2 = 0.967 $$ {R}^2=0.967 $$ ,p < 0.01 $$ p<0.01 $$ ) at 1.5 T andR 2 * = 0.928 × PDFF + 39.4 $$ {\mathrm{R}}_2^{\ast }=0.928\times \mathrm{PDFF}+39.4 $$ (R 2 = 0.972 $$ {R}^2=0.972 $$ ,p < 0.01 $$ p<0.01 $$ ) at 3.0 T. Monte Carlo simulations provide a new means forR 2 * $$ {\mathrm{R}}_2^{\ast } $$ -PDFF prediction, which is primarily determined by fat susceptibility, fat signal model, and magnetic field strength. AccurateR 2 * $$ {\mathrm{R}}_2^{\ast } $$ -PDFF calibration has the potential to correct the effect of fat onR 2 * $$ {\mathrm{R}}_2^{\ast } $$ quantification, and may be helpful for accurateR 2 * $$ {\mathrm{R}}_2^{\ast } $$ measurements in liver iron overload. In this study, a Monte Carlo simulation of hepatic steatosis was developed to predict the relationship betweenR 2 * $$ {\mathrm{R}}_2^{\ast } $$ and PDFF. Furthermore, the effects of fat droplet morphology, fat susceptibility, fat signal model, and magnetic field strength were evaluated for theR 2 * $$ {\mathrm{R}}_2^{\ast } $$ -PDFF calibration. Our results suggest that Monte Carlo simulations provide a new means forR 2 * $$ {\mathrm{R}}_2^{\ast } $$ -PDFF prediction and this means can be easily generated for various regimes, such as simulations with higher fields and different echo times, as well as correction of magnetic susceptibility measurements for liver iron quantification.

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

T_1ρ 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 3 T. We aimed to design a robust spin-lock (SL) preparation module that can be used in myocardial T_1ρ quantification at 3 T. 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 (T_p ) of 8, 4, and 2 ms, 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 T_1ρ 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 T_p  = 8 ms (mean ∆Q = 126.15 vs. 174.42) and 75.6% for T_p  = 4 ms (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 T_1ρ quantification, the T_1ρ 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 T_1ρ quantification at 3 T.

New methods for robust continuous wave T1ρ relaxation preparation

Measurement of the longitudinal relaxation time in the rotating frame of reference (T_1ρ ) 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) T_1ρ contrast with improved robustness against field inhomogeneities and to compare the sensitivities of several existing and the novel T_1ρ contrast generation methods with the B₀ and B₁ field inhomogeneities. Four hard-pulse and four adiabatic CW-T_1ρ 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 T_1ρ as a robust method with minimal RF energy deposition. The double-refocus adiabatic B₁ insensitive rotation-4 CW-T_1ρ preparation offers further improved insensitivity to field variations, but because of the extra RF deposition, may be preferred for ex vivo applications.

Simultaneous comprehensive liver T1 , T2 , T 2 ∗ , T1ρ , and fat fraction characterization with MR fingerprinting

PURPOSE

To develop a novel simultaneous co-registered T₁ , T₂ , T 2 ∗ , T_1ρ , and fat fraction abdominal MR fingerprinting (MRF) approach for fully comprehensive liver-tissue characterization in a single breath-hold scan.

METHODS

A gradient-echo liver MRF sequence with low fixed flip angle, multi-echo radial readout, and varying magnetization preparation pulses for multiparametric encoding is performed at 1.5 T. The T 2 ∗ and fat fraction are estimated from a graph/cut water/fat separation method using a six-peak fat model. Water/fat singular images obtained are then matched to an MRF dictionary, estimating water-specific T₁ , T₂ , and T_1ρ . The proposed approach was tested in phantoms and 10 healthy subjects and compared against conventional sequences.

RESULTS

For the phantom studies, linear fits show excellent coefficients of determination (r² > 0.9) for every parametric map. For in vivo studies, the average values measured within regions of interest drawn on liver, spleen, muscle, and fat are statistically different from the reference scans (p < 0.05) for T₁ , T₂ , and T_1⍴ but not for T 2 ∗ and fat fraction, whereas correlation between MRF and reference scans is excellent for each parameter (r² > 0.92 for every parameter).

CONCLUSION

The proposed multi-echo inversion-recovery, T₂ , and T_1⍴ prepared liver MRF sequence presented in this work allows for quantitative T₁ , T₂ , T 2 ∗ , T_1⍴ , and fat fraction liver-tissue characterization in a single breath-hold scan of 18 seconds. The approach showed good agreement and correlation with respect to reference clinical maps.

Agent Repurposing for the Treatment of Advanced Stage Diffuse Large B-Cell Lymphoma Based on Gene Expression and Network Perturbation Analysis

Over 50% of diffuse large B-cell lymphoma (DLBCL) patients are diagnosed at an advanced stage. Although there are a few therapeutic strategies for DLBCL, most of them are more effective in limited-stage cancer patients. The prognosis of patients with advanced-stage DLBCL is usually poor with frequent recurrence and metastasis. In this study, we aimed to identify gene expression and network differences between limited- and advanced-stage DLBCL patients, with the goal of identifying potential agents that could be used to relieve the severity of DLBCL. Specifically, RNA sequencing data of DLBCL patients at different clinical stages were collected from the cancer genome atlas (TCGA). Differentially expressed genes were identified using DESeq2, and then, weighted gene correlation network analysis (WGCNA) and differential module analysis were performed to find variations between different stages. In addition, important genes were extracted by key driver analysis, and potential agents for DLBCL were identified according to gene-expression perturbations and the Crowd Extracted Expression of Differential Signatures (CREEDS) drug signature database. As a result, 20 up-regulated and 73 down-regulated genes were identified and 79 gene co-expression modules were found using WGCNA, among which, the thistle1 module was highly related to the clinical stage of DLBCL. KEGG pathway and GO enrichment analyses of genes in the thistle1 module indicated that DLBCL progression was mainly related to the NOD-like receptor signaling pathway, neutrophil activation, secretory granule membrane, and carboxylic acid binding. A total of 47 key drivers were identified through key driver analysis with 11 up-regulated key driver genes and 36 down-regulated key diver genes in advanced-stage DLBCL patients. Five genes (MMP1, RAB6C, ACCSL, RGS21 and MOCOS) appeared as hub genes, being closely related to the occurrence and development of DLBCL. Finally, both differentially expressed genes and key driver genes were subjected to CREEDS analysis, and 10 potential agents were predicted to have the potential for application in advanced-stage DLBCL patients. In conclusion, we propose a novel pipeline to utilize perturbed gene-expression signatures during DLBCL progression for identifying agents, and we successfully utilized this approach to generate a list of promising compounds.