Scan protocol and image description.

BackgroundIn 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.MethodsBased 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.ResultsIn the case of sharpness, T1-weighted (T1) showed the highest at EBB, ECF, and EBT (all, p BC, and proton density fat-saturated (PDFS) was the highest at ECM (all, p ConclusionsThe 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：磁共振成像（magnetic resonance imaging, MRI）分割研究中，成像序列的选择会显著影响分割精度。本研究提出一种可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相匹配，我们以膝关节图像为例，开展了多种序列的对比分析研究。 方法：本研究基于虚拟射线剖面信息，设计了可用于计算边缘锐度与对比度的量化指标。针对五类解剖边缘开展边缘分析：骨松质与骨皮质之间（EBB）、骨皮质与软骨之间（EBC）、软骨与脂肪之间（ECF）、软骨与半月板之间（ECM）以及骨皮质与软组织之间（EBT）。随后，从穿过目标边缘的虚拟射线中提取剖面数据，最终通过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：在锐度指标上，T1加权成像（T1-weighted, T1）在EBB、ECF与EBT边缘处表现最优（全部，p BC），质子密度脂肪抑制序列（proton density fat-saturated, PDFS）在ECM边缘处表现最优（全部，p ）。 结论：本研究的最终目标是提出一套可用于筛选最优分割用MRI序列的方法框架，该框架不仅可应用于本研究中的膝关节图像，还可拓展至其他部位的医学影像。本方法可对边缘特征进行量化评估，实验结果表明，该方法在不同边缘上均可获得一致的分析结果。本研究提出的方法可为分割任务中的MRI序列选择提供额外的量化参考依据。