基于多像素值提取优化的重映射方法构建肝脾破裂伤三维体绘制模型

Constructing a three-dimensional volume rendering model of ruptured liver and spleen by an optimized remapping method based on multi-pixel value extraction

  • 摘要:
      背景  CT数据的三维可视化是医学图像领域的热点。肝脾受创伤后解剖结构发生变化,构建相关的三维可视化模型难度较高,相关研究较少。
      目的  本研究设计了多像素值提取优化的重映射方法,用于解决原有算法难以准确构建肝、脾破裂伤可视化模型的问题。
      方法  收集解放军总医院第一医学中心2013年1月- 2018年11月肝、脾破裂伤患者腹部增强CT数据15例,依据伤型分级和治疗等因素筛选男性9例,女性1例。首先利用多像素值提取的方法对CT全部DICOM数据构建体绘制模型,然后进行空间划分,最后对体绘制模型进行DICOM数据重映射与多层插值并得到最终可视化模型。
      结果  本方法于2020年8 - 9月在解放军总医院第一医学中心肝胆胰外科学部应用。模型的构建所需时间为4.01~5.83 s,可准确描述CT数据信息,清晰显示肝脾实质裂伤、血肿及肝内血管损伤,辅助外科医生诊断病情并规划手术。
      结论  本研究构建的肝、脾破裂伤可视化模型可应用于术前讨论、术中指导和术后复查;模型数据在手术仿真和临床教学等方面也具有一定的应用价值。

     

    Abstract:
      Background  Three-dimensional (3D) visualization of computed tomography (CT) data is a hot topic in the field of medical imaging. The anatomical structure of the liver and spleen changes after trauma, so it is difficult to construct the relevant 3D visualization model, and there are few related studies.
      Objective  In this study, we designed an optimized remapping method based on multi-pixel value extraction to solve the problem that it is difficult to accurately construct the visualization model of hepatic and splenic rupture using the original algorithm.
      Methods  Abdominal contrast-enhanced CT data of 15 patients with hepatic and splenic rupture from January 2013 to November 2018 in the First Medical Center of Chinese PLA General Hospital were collected, and 9 males and 1 female were selected based on the factors such as injury classification and treatment status. We first constructed the volume rendering model for all CT DICOM data by multi-pixel value extraction, then divided the space, and finally performed DICOM data remapping and multi-layer interpolation for the volume rendering model to obtain the final visualization model.
      Results  The method was applied at the Faculty of Hepato-Pancreato-Biliary Surgery, the First Medical Center of Chinese PLA General Hospital, from August to September in 2020. It took 4.01-5.83 seconds to construct the model. This model was able to preserve CT data and clearly showed hepatic and splenic parenchymal laceration, hematoma, and intrahepatic vascular injury, which could assist surgeons in understanding the disease condition and planning the surgery.
      Conclusion  The constructed visualization model of hepatic and splenic rupture can be used in preoperative discussion, intraoperative guidance, and postoperative review, and the model data also have certain application value in surgical simulation and clinical teaching.

     

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