Automated Scan Prescription for MR Imaging of Deformed and Normal Livers
Automated Scan Prescription for MR Imaging of Deformed and Normal Livers
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DOI：
10.2463/mrms.2012-0006
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发表时间：
2013-01-01
影响因子：
3
通讯作者：
Kabasawa, Hiroyuki
中科院分区：
文献类型：
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作者：
Goto, Takao;Kabasawa, Hiroyuki
目的：我们提出了一个自动化的扫描处方，以评估正常和变形的肝脏，并证明其在正常志愿者和模拟变形livers.Methods的疗效：我们的自动化扫描处方可用于识别肝脏的上，下边缘，使常用的轴向切片定位。通过模板匹配检测肝脏的上边缘，并最终通过将主动形状模型应用于矢状投影图像来识别。使用最大后验（MAP）概率估计来检测下边缘，该最大后验概率估计利用来自放置在肝脏中的感兴趣区域（ROT）的统计信息。这对肝脏形状没有限制，因此可以有效地评估肝脏畸形。经过机构审查和批准，我们在45名健康志愿者中测试了我们的方法。我们还使用临床信息来模拟变形的肝脏，并测试我们的方法与这些datasets offline.Results：我们可以检测到上边缘的误差范围为-3至6 mm，即使没有强度校正正常志愿者。正常志愿者的最大21 mm和7.84 mm标准差的下边缘的类似检测证实了我们的改良方法对于变形肝脏的上级功效优于使用我们以前的方法。临床使用需要大约10秒的计算时间的核心i5笔记本电脑2 GB memory.Conclusion：我们提出了一种方法，用于自动扫描处方的磁共振（MR）成像的肝脏，并证明了我们的算法的有效性，在一个实际的计算时间内评估变形的肝脏。通过应用MAP估计结合来自ROT的统计信息来检测各种形状的肝脏边缘，证明了该技术的潜在临床实用性。
Purpose: We propose an automated scan prescription to assess normal and deformed livers and demonstrate its efficacy in normal volunteers and in simulated deformed livers.Methods: Our automated scan prescription can be used to identify the upper and lower edges of the liver enables in commonly used axial slice positioning. The liver's upper edge is detected by template matching and finally identified by applying an active shape model to a sagittal projection image. The lower edge is detected using a maximum a posteriori (MAP) probability estimate that utilizes statistical information from a region of interest (ROT) placed in the liver. This places no restraints on liver shape and is therefore effective in assessing a deformed liver.Following institutional review and approval, we tested our method in 45 healthy volunteers. We also used clinical information to simulate deformed livers and tested our method with those datasets offline.Results: We could detect the upper edges within an error range of -3 to 6 mm, even without intensity correction for normal volunteers. Similar detection of the lower edges with maximum 21-mm and 7.84-mm standard deviation for normal volunteers confirmed the superior efficacy of our modified approach for deformed livers to that using our previous method. Clinical use required approximately 10 s' computational time on a Core i5 lap-top with 2-GB memory.Conclusion: We propose a method for automated scan prescription in magnetic resonance (MR) imaging of the liver and demonstrate the efficacy of our algorithm for evaluating deformed livers within a practical computation time. Detection of liver edges of various shapes by applying the MAP estimate combined with statistical information from the ROT demonstrated the potential clinical utility of this technique.