Department of Thoracic Surgical Oncology, Cancer Institute Hospital, The Japanese Foundation for Cancer Research, 3-10-6 Ariake, Koto-ku, Tokyo, 135-8550, Japan.
Gen Thorac Cardiovasc Surg. 2022 Mar;70(3):312-314. doi: 10.1007/s11748-021-01750-x. Epub 2021 Nov 23.
We developed a novel three-dimensional (3D) image simulation system, focused on pulmonary segmentectomy. The novel algorithms run by the software, which are independent of the differences in computed tomography (CT) values of vascular structures, enabled the creation of 3D images from unenhanced CT data with accuracy comparable to that from contrast-enhanced CT data. To evaluate the anatomical accuracy, we compared it between images created from unenhanced and contrast-enhanced CT in seven patients who underwent thoracoscopic segmentectomy. With regard to the automatic recognition of pulmonary vessels, the 3D image from unenhanced CT falsely recognized one or two points in two cases, whereas that from contrast-enhanced CT false recognitions in one case. Both 3D images had similar creation time and capability for identifying the intersegmental plain. The novel 3D image simulation for segmentectomy from unenhanced CT had sufficient anatomical accuracy for practical use but required attention due to inevitable minor false recognition.
我们开发了一种新型的三维（3D）图像模拟系统，专注于肺部节段切除术。该软件所运行的新型算法独立于血管结构的计算机断层扫描（CT）值差异，能够从非增强 CT 数据中创建出与增强 CT 数据相当的准确的 3D 图像。为了评估解剖学的准确性，我们将其与 7 例接受胸腔镜节段切除术的患者的非增强和增强 CT 图像进行了比较。关于肺血管的自动识别，非增强 CT 的三维图像在两例中错误识别了一个或两个点，而增强 CT 的三维图像在一例中错误识别了一个点。两种 3D 图像的创建时间和识别段间平面的能力相似。新型的非增强 CT 节段切除术 3D 图像具有足够的解剖学准确性，可用于实际应用，但由于不可避免的轻微错误识别，需要引起注意。
