To develop Monte Carlo simulations to predict the relationship of R 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 for R 2 * $$ {\mathrm{R}}_2^{\ast } $$ and proton density fat fraction (PDFF) predictions. In addition, the relationships between R 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) on R 2 * $$ {\mathrm{R}}_2^{\ast } $$ predictions. A virtual liver model with realistic morphology of fat droplets was demonstrated, and R 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. The R 2 * $$ {\mathrm{R}}_2^{\ast } $$ -PDFF relationships by simulations with fat susceptibility of 0.6 ppm and the multi-peak fat signal model were R 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 and R 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 for R 2 * $$ {\mathrm{R}}_2^{\ast } $$ -PDFF prediction, which is primarily determined by fat susceptibility, fat signal model, and magnetic field strength. Accurate R 2 * $$ {\mathrm{R}}_2^{\ast } $$ -PDFF calibration has the potential to correct the effect of fat on R 2 * $$ {\mathrm{R}}_2^{\ast } $$ quantification, and may be helpful for accurate R 2 * $$ {\mathrm{R}}_2^{\ast } $$ measurements in liver iron overload.

To develop Monte Carlo simulations to predict the relationship of R-2(* ) 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 for R-2(* ) and proton density fat fraction (PDFF) predictions. In addition, the relationships between R-2(* ) 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) on R-2(* ) predictions. A virtual liver model with realistic morphology of fat droplets was demonstrated, and R-2(* ) and PDFF values were predicted by Monte Carlo simulations at 1.5 T and 3.0 T. R-2(* ) 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. The R-2(* ) -PDFF relationships by simulations with fat susceptibility of 0.6 ppm and the multi-peak fat signal model were R-2(* ) =0.490xPDFF+28.0 (R-2=0.967, p<0.01) at 1.5 T and R-2(* ) =0.928xPDFF+39.4 (R-2=0.972, p<0.01) at 3.0 T. Monte Carlo simulations provide a new means for R2*-PDFF prediction, which is primarily determined by fat susceptibility, fat signal model, and magnetic field strength. Accurate R-2(* ) -PDFF calibration has the potential to correct the effect of fat on R-2(* ) quantification, and may be helpful for accurate R-2(* ) measurements in liver iron overload. In this study, a Monte Carlo simulation of hepatic steatosis was developed to predict the relationship between R-2(* ) and PDFF. Furthermore, the effects of fat droplet morphology, fat susceptibility, fat signal model, and magnetic field strength were evaluated for the R-2(* ) -PDFF calibration. Our results suggest that Monte Carlo simulations provide a new means for R-2(* ) -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. Abbreviations

PURPOSE:To investigate the effect of particle size on liver R 2 * $$ {\mathrm{R}}_2^{\ast } $$ by Monte Carlo simulation and phantom studies at both 1.5 T and 3.0 T. METHODS:Two kinds of particles (i.e., iron sphere and fat droplet) with varying sizes were considered separately in simulation and phantom studies. MRI signals were synthesized and analyzed for predicting R 2 * $$ {\mathrm{R}}_2^{\ast } $$ , based on simulations by incorporating virtual liver model, particle distribution, magnetic field generation, and proton movement into phase accrual. In the phantom study, iron-water and fat-water phantoms were constructed, and each phantom contained 15 separate vials with combinations of five particle concentrations and three particle sizes. R 2 * $$ {\mathrm{R}}_2^{\ast } $$ measurements in the phantom were made at both 1.5 T and 3.0 T. Finally, differences in R 2 * $$ {\mathrm{R}}_2^{\ast } $$ predictions or measurements were evaluated across varying particle sizes. RESULTS:In the simulation study, strong linear and positively correlated relationships were observed between R 2 * $$ {\mathrm{R}}_2^{\ast } $$ predictions and particle concentrations across varying particle sizes and magnetic field strengths ( r ≥ 0.988 $$ r\ge 0.988 $$ ). The relationships were affected by iron sphere size ( p < 0.001 $$ p<0.001 $$ ), where smaller iron sphere size yielded higher predicted R 2 * $$ {\mathrm{R}}_2^{\ast } $$ , whereas fat droplet size had no effect on R 2 * $$ {\mathrm{R}}_2^{\ast } $$ predictions ( p ≥ 0.617 $$ p\ge 0.617 $$ ) for constant total fat concentration. Similarly, the phantom study showed that R 2 * $$ {\mathrm{R}}_2^{\ast } $$ measurements were relatively sensitive to iron sphere size ( p ≤ 0.004 $$ p\le 0.004 $$ ) unlike fat droplet size ( p ≥ 0.223 $$ p\ge 0.223 $$ ). CONCLUSION:Liver R 2 * $$ {\mathrm{R}}_2^{\ast } $$ is affected by iron sphere size, but is relatively unaffected by fat droplet size. These findings may lead to an improved understanding of the underlying mechanisms of R 2 * $$ {\mathrm{R}}_2^{\ast } $$ relaxometry in vivo, and enable improved quantitative MRI phantom design.

由于不健康的饮食和缺乏运动,脂肪含量异常已逐渐成为危害人体健康的重要因素之一.在组织器官周围过度聚集的脂肪会破坏人体内信息传导系统,使得组织器官向身体传递错误信号,导致内分泌系统紊乱,这通常与脂肪肝、2型糖尿病、高血压和骨质疏松等慢性疾病密切相关.因此,脂肪精准量化对上述疾病的预防、病情评估和诊断治疗有着极其重要的临床意义.活检是目前脂肪量化的金标准,但其具有一定的有创性和抽样误差.近年来,磁共振成像技术越来越广泛地应用于脂肪量化研究.本文就多种磁共振成像技术,包括磁共振波谱、脂肪抑制成像、水脂分离和质子密度脂肪分数,量化人体不同部位(肝脏、胰腺、椎体骨髓和肌肉)脂肪含量的研究进展作一综述,旨在为脂肪量化提供更准确的生物标志物,以达到临床精准诊治的目的.

BACKGROUND AND OBJECTIVE:To model hepatic steatosis in adult humans with non-alcoholic fatty liver disease based on stereology and spatial distribution of fat droplets from liver biopsy specimens. METHODS:Histological analysis was performed on 30 adult human liver biopsy specimens with varying degrees of steatosis. Morphological features of fat droplets were characterized by gamma distribution function (GDF) in both two-dimensional (2D) and three-dimensional (3D) spaces from three aspects: 1) size distribution indicating non-uniformity of fat droplets in radius; 2) nearest neighbor distance distribution indicating heterogeneous accumulation (i.e., clustering) of fat droplets; 3) regional anisotropy indicating inter-regional variability in fat fraction (FF). To generalize the morphological description of hepatic steatosis to different FFs, correlation analysis was performed among the estimated GDF parameters and FFs for all specimens. Finally, Monte Carlo modeling of hepatic steatosis was developed to simulate fat droplet distribution in tissue. RESULTS:Morphological features, including size and nearest neighbor distance in 2D and 3D spaces as well as regional anisotropy, statistically captured the distribution of fat droplets by the GDF fit (R2 > 0.54). The estimated GDF parameters (i.e., scale and shape parameters) and FFs were well correlated, with R2 > 0.55. In addition, simulated 3D liver morphological models demonstrated similar sections to real histological samples both visually and quantitatively. CONCLUSIONS:The morphology of hepatic steatosis is well characterized by stereology and spatial distribution of fat droplets. Simulated models demonstrate similar appearances to real histological samples. Furthermore, the model may help understand MRI signal behavior in the presence of liver steatosis.

The R2' relaxation rate is expected to become a more specific biomarker in hepatic iron overload because it is not affected by pathological factors. However, calibrating the relationship between R2' relaxation rate and hepatic iron concentration (HIC) at different field strengths is time-consuming. In this work, we develop Monte Carlo simulations to investigate relationship between R2' relaxation rate and HIC in hepatic iron overload at both 1.5T and 3.0T. Results show strong linear relationships between R2' predictions and HIC. The Monte Carlo simulations may greatly shorten the clinical calibration time of MRI relaxation rates and HIC.

Motivation: Liver fat (hepatic steatosis) can confound R2*-based iron quantification in chemical shift encoded MRI, while the size of fat droplets may also affect liver R2*. However, it is infeasible to experimentally investigate the influence of fat droplet size in vivo on liver R2* due to tissue complexity. Goal(s): To investigate the influence of fat droplet size on liver R2* at both 1.5T and 3.0T. Approach: Monte Carlo simulation and phantom studies. Results: Liver R2* demonstrates a positive linear relationship with proton density fat fraction and remains relatively unaffected by fat droplet size. Impact: These findings may benefit phantom design and understanding of the underlying mechanisms of R2* characteristics in the presence of hepatic steatosis.