Inter-subject variability (ISV).

Background
In magnetic resonance imaging (MRI) segmentation research, the choice of sequence influences the segmentation accuracy. This study introduces a method to compare sequences. By aligning sequences with specific segmentation objectives, we provide an example of a comparative analysis of various sequences for knee images.
Methods
Based on the profile information of virtual rays, we devised metrics to compute the edge sharpness and contrast. Edge analysis was performed in five edges (EBB: between cancellous and cortical bone, EBC: between cortical bone and cartilage, ECF: between cartilage and fat, ECM: between cartilage and meniscus, EBT: between cortical bone and tissue). Subsequently, profiles were extracted from the virtual ray that traversed the defined edge. Finally, edge characteristics were compared in each sequence using the computed metrics.
Results
In the case of sharpness, T1-weighted (T1) showed the highest at EBB, ECF, and EBT (all, p < .05). The fat-suppressed 3D spoiled gradient-echo (SPGR) was the highest at EBC, and proton density fat-saturated (PDFS) was the highest at ECM (all, p < .005). Depending on each sequence, the knee structures showed different edge characteristics. Also, it was confirmed that the edge properties of the structure depend on the adjacent materials.
Conclusions
The ultimate goal of this study is to present a methodology for selecting the most appropriate MRI sequence for segmentation, which can be applied to images of other parts in addition to the knee images used in the study. The method we present quantitatively evaluates the edge characteristics, and experimental results show that our method shows consistent results according to the edge. Our method will provide additional information for MRI sequence selection for segmentation.

背景
在磁共振成像（Magnetic Resonance Imaging, MRI）分割研究领域中，成像序列的选择会直接影响分割精度。本研究提出一种用于对比不同成像序列的方法。通过将序列与特定分割目标进行适配，我们以膝关节图像为例，开展了多种成像序列的对比分析研究。
方法
基于虚拟扫描线的剖面信息，我们设计了用于计算边缘锐度与对比度的量化指标。本次边缘分析涵盖5组关键边缘：EBB：松质骨与皮质骨之间的边界；EBC：皮质骨与软骨之间的边界；ECF：软骨与脂肪之间的边界；ECM：软骨与半月板之间的边界；EBT：皮质骨与软组织之间的边界。随后，从穿过上述定义边缘的虚拟扫描线中提取剖面数据。最终，利用计算得到的量化指标，对不同序列下的边缘特征进行对比分析。
结果
就边缘锐度而言，T1加权成像（T1-weighted, T1）在EBB、ECF和EBT三组边缘上均表现出最优锐度（所有组均满足p < 0.05）。脂肪抑制三维扰相梯度回波（fat-suppressed 3D spoiled gradient-echo, SPGR）在EBC边缘上表现最优，质子密度脂肪抑制序列（proton density fat-saturated, PDFS）则在ECM边缘上表现最优（所有组均满足p < 0.005）。不同成像序列下，膝关节各结构呈现出各异的边缘特征。此外，本研究证实组织结构的边缘特性与其相邻组织的属性密切相关。
结论
本研究的最终目标是提出一种可用于筛选最优分割用磁共振成像序列的方法，该方法除可应用于本研究使用的膝关节图像外，还可推广至其他部位的医学影像。我们提出的方法可对边缘特征进行量化评估，实验结果表明，该方法在不同边缘下均能获得一致的分析结果。本方法可为分割任务中的磁共振成像序列选择提供额外的参考依据。