Shrestha Utsav, Brasher Sarah, Abramson Zachary, Morin Cara E, Tipirneni-Sajja Aaryani
Department of Biomedical Engineering, The University of Memphis, Memphis, Tennessee, USA.
Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, Tennessee, USA.
Magn Reson Med. 2025 May;93(5):2176-2185. doi: 10.1002/mrm.30419. Epub 2025 Jan 2.
To investigate the impact of iron particle size on and fat fraction (FF) estimations for coexisting hepatic iron overload and steatosis condition using Monte Carlo simulations and phantoms.
Three iron particle sizes (0.38, 0.52, and 0.71 μm) were studied using simulations and phantoms. Virtual liver models mimicking in vivo spatial distribution of fat droplets and iron deposits were created, and MRI signals were synthesized using Monte Carlo simulations for FF 1%-30% and liver iron concentration (LIC) 1-20 mg/g. Seventy-five fat-iron phantoms with varying iron (0-8 μg/mL) and fat (0%-40%) concentrations and particle sizes were constructed. Three-way analysis of variance was used to assess the effect of iron particle size on and FF estimations.
In simulations, estimated and true FF were in excellent agreement (slope: 0.93-1.09) for liver iron concentration ≤ 13 mg/g. For both simulations and phantoms, FF estimation bias increased as iron concentration increased and particle size decreased, with 0.71μm iron particle having the lowest bias (≤ 20%), and 0.52 μm and 0.38 μm iron particles producing higher bias (≥ 20%) for higher iron concentrations and lower FFs. Additionally, increased linearly with increasing iron concentration (r ≥ 0.87) and decreasing particle size. Iron particle size significantly influenced the estimated versus true FF (simulations: p = 0.04; phantoms: p = 0.03) and -iron concentration (simulations: p < 0.001; phantoms: p < 0.01) relationships. Heatmap demonstrated broader region with higher FF estimation bias as iron particle size decreased, especially at higher iron concentration.
and FF estimations are affected by iron particle size, with smaller particles leading to higher values and increased FF estimation bias.
使用蒙特卡罗模拟和体模研究铁颗粒大小对共存的肝铁过载和脂肪变性状态下的[具体指标未给出]和脂肪分数(FF)估计的影响。
使用模拟和体模研究了三种铁颗粒大小(0.38、0.52和0.71μm)。创建了模拟体内脂肪滴和铁沉积空间分布的虚拟肝脏模型,并使用蒙特卡罗模拟合成了FF为1%-30%且肝脏铁浓度(LIC)为1-20mg/g时的MRI信号。构建了75个具有不同铁(0-8μg/mL)和脂肪(0%-40%)浓度及颗粒大小的脂肪-铁体模。采用三因素方差分析来评估铁颗粒大小对[具体指标未给出]和FF估计的影响。
在模拟中,对于肝脏铁浓度≤13mg/g,估计的FF与真实FF高度一致(斜率:0.93-1.09)。对于模拟和体模,FF估计偏差均随着铁浓度的增加和颗粒大小的减小而增加,0.71μm铁颗粒的偏差最低(≤20%),而0.52μm和0.38μm铁颗粒在较高铁浓度和较低FF时产生更高的偏差(≥20%)。此外,[具体指标未给出]随铁浓度的增加(r≥0.87)和颗粒大小的减小呈线性增加。铁颗粒大小显著影响估计的FF与真实FF的关系(模拟:p = 0.04;体模:p = 0.03)以及[具体指标未给出]-铁浓度的关系(模拟:p < 0.001;体模:p < 0.01)。热图显示随着铁颗粒大小的减小,FF估计偏差较高的区域更宽,尤其是在高铁浓度时。
[具体指标未给出]和FF估计受铁颗粒大小影响,颗粒越小,[具体指标未给出]值越高且FF估计偏差越大。