在儿科动态MRI中胸腔腹部器官的自分化
medRxiv : the preprint server for health sciences
|May 20, 2024
概括
这项研究引入了使用动态MRI (dMRI) 的胸腔腹部器官的自分区系统. 该系统精确地划分器官,帮助手术规划诸如胸部缺陷综合征 (TIS) 等疾病.
科学领域:
- 医疗成像医学成像
- 计算解剖学的计算解剖学
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 对胸部-腹部器官运动的准确分析对于胸部缺陷综合征 (TIS),青少年异常结石症 (AIS) 和早期结石症 (EOS) 等疾病的手术规划至关重要.
- 动态磁共振成像 (dMRI) 是捕捉儿科患者器官运动的关键方式.
- 在dMRI中的挑战包括对比度差,非标准图像和组织之间的类似纹理.
研究的目的:
- 建议在儿科dMRI扫描中为肺部,脏,肝脏,脏和胸腹皮肤提供一个自分区系统.
- 为了解决从dMRI数据对器官进行细分所带来的技术挑战.
主要方法:
- 使用深度学习 (DL) 架构的两步自动细分方法:DL-R用于识别,DL-D用于划分.
- DL-D采用了一个编码器-解码器框架,具有多分辨率功能,以提高细分精度.
- 对189名近乎正常的受试者在不同呼吸阶段 (吸气和呼气) 的dMRI松收购进行了评估.
主要成果:
- 识别步骤 (DL-R) 实现了肺,脏和脏的平均位置误差 (LE) 约为1个voxel,肝脏和皮肤的平均位置误差为1.5个voxel.
- 划分步骤 (DL-D) 产生了高的子系数 (DC):肺部为0.92-0.94,脏为0.82,肝脏为0.90,脏为0.81,皮肤为0.93.
- 该系统在不同的测试场景中表现出稳健性,包括呼吸阶段的变化和地面真相可用性.
结论:
- 开发的自动细分系统有效地处理dMRI中的挑战,例如低分辨率和运动模糊.
- 这种系统显着有望改善胸腔腹部疾病的儿科患者的手术规划.
- 目前正在进行的工作包括对TIS患者的dMRI数据测试系统的有效性.
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