Three-dimensional image simulation for lung segmentectomy from unenhanced computed tomography data

Abstract

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.