Workstation

Raw CTA data were analyzed using a GE workstation. The original data were reconstructed using the Volume Rendering program. The nonvenous structures surrounding the VG were removed using the cut tool. The internal diameter of the vessels and the spatial distance between them were measured using the Measure distance tool. The Two Click AVA tool was used to measure the length of the vessels. The angle between different vessels was measured with the angle tool in Photoshop (2020, US. Adobe Systems Incorporated). Each parameter was measured three times, and the average value was used in analysis.

The raw CTA data were postprocessed using a GE Workstation. The raw CTA data were initially reconstructed using volume rendering procedure. Structures that interfered with the measurement were removed using the cutting tool. The vessel diameter was obtained using the distance-measuring tool. The curved length of a vessel was measured using the two-click AVA tool. The angle between the vessels was measured by the degree tool. Each parameter was measured 3 times, and the average value was used for analysis.

All patients underwent contrast-enhanced CT before biopsy and/or surgery to evaluate suspected mediastinal tumors. Chest CT examinations were performed with either 320-row detector CT (Toshiba Aquilion ONE, Otawara-shi, Japan), 64-row detector CT (Toshiba Aquilion 64, Otawara-shi, Japan), or 16-row detector CT (Siemens Somatom Sensation 16, Forcheim, Germany) scanners. The acquisition parameters were 0.5 mm, 0.5 mm, and 0.625 mm detector collimation; 120 kVp tube voltage; 0.5 s gantry rotation time; 1 mm, 1 mm, and 1.5 mm reconstructed section thickness; and 0.8 mm, 0.8 mm, and 1 mm reconstruction intervals. All examinations were performed after injecting 60–100 ml (1–1.5 ml/kg) of nonionic intravenous contrast agent (350/100 Omnipaque, GE healthcare, Oslo, Norway), at a rate of 2.5 ml/s via the antecubital vein. The area from the thoracic inlet caudally to include the adrenal glands was scanned. All images were reviewed by a senior radiologist (Ç.U) with more than 10 years of experience in thoracic imaging. She was blinded to the histopathological data to avoid bias. Multiplanar reformatted images were analyzed on a workstation (GE Healthcare, Waukesha, WI, USA) (Figs. 1 and 2).
Fig. 1

The lung extraction and 3D representation of tumor with lung structures

Fig. 2

3D representation of regional segmentation of bronchus, artery, and vessels together with tumor volume

For the selected CT data (original CT data), a paired dataset was obtained with a MAR algorithm applied (MAR-CT data) via the CT console of the workstation (GE Healthcare, Milwaukee, WI, USA). In both the original and the MAR-applied CT data, jaw bone segmentation was performed based on the threshold of 160 to 3071 Hounsfield units (HU). The segmented jaw bone was converted into an 3D model in STL format. The segmentation and conversion processes were performed on the AW Server (GE Healthcare, Milwaukee, WI, USA). The 3D digital model from the original CT data was referred to as the original model and that from the MAR-CT data was referred to as the MAR-applied model.