Introduction

MRI T2* relaxometry protocols are often used for Liver Iron Quantification in patients with hemochromatosis. Several methods exist to semi-automatically segment parenchyma and exclude vessels for this calculation.

Purpose

To determine if inclusion of multiple echoes inputs to Convolutional Neural Networks (CNN) improves automated liver and vessel segmentation in MRI T2* relaxometry protocols and to determine if the resultant segmentations agree with manual segmentations for liver iron quantification analysis.

Methods

Multi echo Gradient Recalled Echo (GRE) MRI sequence for T2* relaxometry was performed for 79 exams on 31 patients with hemochromatosis for iron quantification analysis. 275 axial liver slices were manually segmented as ground truth masks. A batch normalized U-Net with variable input width to incorporate multiple echoes is used for segmentation, using DICE as the accuracy metric. ANOVA is used to evaluate significance of channel width changes in segmentation accuracy. Linear regression is used to model the relationship of channel width on segmentation accuracy. Liver segmentations are applied to relaxometry data to calculate liver T2* yielding liver iron concentration(LIC) derived from literature based calibration curves. Manual and CNN based LIC values are compared with Pearson correlation. Bland altman plots are used to visualize differences between manual and CNN based LIC values.

Results

Performance metrics are tested on 55 hold out slices. Linear regression indicates that there is a monotonic increase of DICE with increasing channel depth (p = 0.001) with a slope of 3.61e-3. ANOVA indicates a significant increase segmentation accuracy over single channel starting at 3 channels. Incorporation of all channels results in an average DICE of 0.86, an average increase of 0.07 over single channel. The calculated LIC from CNN segmented livers agrees well with manual segmentation (R = 0.998, slope = 0.914, p«0.001), with an average absolute difference 0.27 ± 0.99 mg Fe/g or 1.34 ± 4.3%.

Conclusion

More input echoes yields higher model accuracy until the noise floor. Echos beyond the first three echo times in GRE based T2* relaxometry do not contribute significant information for segmentation of liver for LIC calculation. Deep learning models with three channel width allow for generalization of model to protocols of more than three echoes, effectively a universal requirement for relaxometry. Deep learning segmentations achieve a good accuracy compared with manual segmentations with minimal preprocessing. Liver iron values calculated from hand segmented liver and Neural network segmented liver were not statistically different from each other.

中文翻译：

---

通道宽度优化的神经网络，用于肝铁定量中的肝脏和血管分割。

介绍

MRI T2 *弛豫测定法常用于血色素沉着病患者的肝铁定量。存在几种半自动分割实质并排除血管进行此计算的方法。

目的

为了确定是否将多个回波包含到卷积神经网络（CNN）中，可以改善MRI T2 *弛张测量法协议中的自动肝和血管分割，并确定所得分割是否与手动分割相符，以进行肝铁定量分析。

方法

对31名血色素沉着病患者进行了79次检查，以进行T2 *弛豫法的多回波梯度回波（GRE）MRI序列，以进行铁定量分析。手动将275个轴向肝切片切成地面真面膜。使用DICE作为精度指标，使用具有可变输入宽度并合并多个回波的批归一化U-Net进行分割。方差分析用于评估分割精度中通道宽度变化的重要性。线性回归用于对通道宽度与分段精度之间的关系进行建模。将肝脏分割应用于张弛测量数据以计算肝脏T2 *，从而从基于文献的校准曲线中得出肝脏铁浓度（LIC）。将基于手动和CNN的LIC值与Pearson相关性进行比较。

结果

性能指标在55个保持片上进行了测试。线性回归表明，随着通道深度（p = 0.001）的增加，DICE呈单调增加，斜率为3.61e-3。方差分析表明，从3个通道开始，与单个通道相比，分段精度显着提高。合并所有通道会导致平均DICE为0.86，比单个通道平均增加0.07。从CNN分割的肝脏计算出的LIC与手动分割非常吻合（R = 0.998，斜率= 0.914，p <0.001），平均绝对差为0.27±0.99 mg Fe / g或1.34±4.3％。

结论

输入噪声越多，产生的本底噪声越高，模型的精度越高。在基于GRE的T2 *弛豫法中，前三个回波时间之外的回波不会为LIC计算中的肝分割提供重要信息。具有三个通道宽度的深度学习模型允许将模型推广到三个以上回波的协议，这实际上是弛张测量的通用要求。与人工分割相比，深度学习分割具有最低的预处理精度。从手分割肝脏和神经网络分割肝脏计算出的肝铁值在统计学上没有差异。