Syngo via vb30

Image processing was performed by using a commercially available software application (Syngo. via VB30, MR Prostate, and MR Tissue4D; Siemens Healthineers, Shanghai, China). The TOFTS model was used to calculate quantitative pharmacokinetic model parameters, including the influx forward volume transfer constant (Ktrans, /min) and rate constant (Kep, /min).
Ktrans and Kep measurements were achieved by a circle tool to delineate the ROI on perfusion maps with the largest three layers of tumor lesions (carefully avoiding necrosis or cystic areas). In this study, two experienced radiologists (with 6 and 10 years of experience in rectal imaging) performed this task blind to the patient’s clinical and pathological information, but they were aware that the patients were rectal cancer patients. The radiologists reviewed the T₂WI and DWI images and determined the location of the tumor. The final Ktrans and Kep values corresponded to the mean values obtained by drawing three different levels of ROI (with areas no less than 1 cm²)^{29 (link)} and taking the average. The Ktrans and Kep values were averaged between the two radiologists for further analysis (Figs. 1, 2).Figure 1

(a–d) Showing the T2WI, ADC map, Ktrans, and Kep of GRASP, respectively.

Figure 2

(a–d) Showing the T2WI, ADC map, Ktrans, and Kep of TWIST, respectively.

DECT datasets were postprocessed in commercially available postprocessing software Syngo.ViaVB30 (Siemens Healthineers, Forchheim, Germany) using the “Gout” application class at factory default settings including a DECT ratio of 1.4, minimum HU at 150 HU and maximum at 500 HU. Transversal gout series (slice thickness 0.75 mm with 0.5-mm increments) where then exported into a custom MeVisLab-application (MevisLab vers.2.8.2, GmbH, Bremen, Germany).
All colour-coded DECT voxels in the scanned regions were automatically registered. Colour-coded DECT lesions were defined as a 3-dimensional cluster of colour-coded DECT voxels adjacent to each other.

Datasets were analyzed using dedicated semi-automatic software (SyngoViaVB30, Pulmo3D, Siemens Healthineers, Forchheim, Germany). Lung segmentation was automated and manually revised if necessary (Fig 1). Four quantitative parameters were acquired: total lung volume (volume), mean lung density (MLD), full-width-half-max (FWHM) and low attenuation volume (LAV). The LAV threshold for emphysema was set to -950 HU. This cut-off had been extensively evaluated in previous studies and strongly correlates with microscopic and gross emphysema [27 (link), 28 (link)]. FWHM marks the width at the half maximum of the voxel count to specific HU value curve representing the density distribution of the lung parenchyma. A graphical explanation of the latter can be found in the online supplement. (S1 Fig).

Image datasets with detectable breathing changes were quantitatively analyzed in a dual approach: (a) evaluations of the cardiovascular contrast dynamics and (b) of respiratory-induced translation of the liver. The quantitative assessments were performed using a commercially available postprocessing software application (syngo.via VB30; Siemens Healthineers). Evaluations were performed in consensus readings by two radiologists, C.G.G. and H.-C.B., both with 3 years of experience in image postprocessing, on de-identified image datasets and without annotations regarding the administered contrast agent.

Images obtained on the Vision at the four different reconstructed scan durations using three different reconstruction protocols were evaluated on image quality. Two nuclear medicine physicians (AHB and WN, with 20 and 5 years of experience in ⁸⁹Zr immunoPET image reading, respectively) independently assessed the images using a dedicated syngo.via VB30 (Siemens Healthineers) workstation. All images were scored based on a 5-point Likert scale regarding image noise, lesion margin demarcation and overall image quality (see Supplemental Fig. 1  for the used visual image assessment form).Fig. 1

Patient example ⁸⁹Zr immunoPET images obtained using the Vision PET/CT. Maximum intensity projection PET images acquired at day 4 p.i. of 37 MBq [⁸⁹Zr]mAb of a 79-year-old patient (weight 86 kg) with metastatic breast cancer acquired at 100%, 75%, 50% and 25% of the scan duration (from left to right, respectively) using the Clinical Vision, EARL2 Vision and EARL1 Vision reconstruction protocols (from top to bottom, respectively). Images were scaled at equal contrast intensities

C11 methionine PET images were loaded into SIEMENS SYNGO Via (VB30) workstation after correcting for partial volume effects (PVE) using Siemens E7 tools (Fig. 1A, B). The 3D ROI was drawn semi-automatically using an individually adapted isocontour of the tumor maximum using a standard ROI with a fixed diameter of 1.6 cm centered on the tumor maximum yielding a volume of 2 ml (Fig. 1C). Similar mirror ROI was placed in the contralateral brain parenchyma to calculate the background /normal brain parenchymal uptake (Fig. 1C). The values SUV_max and SUV_mean were obtained for both tumor and normal brain parenchyma and tabulated. Ratio TBR _max and TBR _mean (tumor to normal brain/background) were calculated for statistical analysis.
Fig. 1

LIST mode UTE MRAC sequence reconstructed PET images (A) and images reprocessed on E7 tools SIEMENS for correction of partial volume effects (PVE) (B). 3D ROI was drawn semi-automatically using an individually adapted isocontour of the tumor maximum using a standard ROI with a fixed diameter of 1.6 cm centered on the tumor maximum yielding a volume of 2 ml (C)