Pmod 4

Image processing of the PET/CT scans was performed by an experienced nuclear physician using PMOD 4.0 software (PMOD Technologies LLC, Zurich, Switzerland) (10 (link)). The 97 frames were merged into a single dynamic sequence to quantify tracer dynamics using co-registered dynamic PET and CT images. To account for the partial volume effect, this study involved delineating volumes of interest (VOIs), which were found to be 1–3 mm smaller in all dimensions than the actual region of interest observed in the images. The boundary definition of this VOI should be based on either CT or PET imaging, and we prioritized the smaller area as the more accurate representation of the region of interest. In this study, all organs were rendered to scale as previously specified, except for the skeletal bone, which was represented by the proximal femur. Time-activity curves (TACs) were then automatically generated using the kinetic modeling module of the PMOD software while taking into account the radioactive decay. The generated TACs were used to observe changes in uptake by the source organs.

The researchers
were not blinded during the experiments, but data analysis was done
using automated procedures and was thus operator-independent. Data
analysis was performed using PMOD 4.0 software (PMOD Technologies,
Zürich, Switzerland). The averaged PET images acquired between
40 and 60 min after tracer injection were aligned to a tracer-specific
reference template for [¹¹C]raclopride. The same transformation
matrix was subsequently applied to dynamic PET frames in order to
automatically co-register them to the reference template. A volume
of interest (VOI) atlas containing the striatum and cerebellum was
then placed on each co-registered PET image. Individual time–activity
curves (TACs, in kBq/mL) were generated for each VOI from the dynamic
data.

Static imaging of sacrificed animals was performed on a VECTor4 small-animal SPECT/PET/CT/OI scanner from MILabs (Utrecht, Netherlands) directly after blood collection with an acquisition time of 45 min using an HE-GP-RM collimator and a step-wise multiplanar bed movement via MILabs acquisition software (v11.00 and v12.26). Imaging data were reconstructed using MILabs-Reconstruction software (v12.00) and image analysis was performed with PMOD4.0 (PMOD technologies LLC, Zurich, Switzerland). Animals were subjected to biodistribution studies after imaging.

For the image evaluation subgroup (N = 200 patients), an experienced nuclear medicine physician (AHD) visually identified the “most blurry” lesion on the UG images, i.e., a representative lesion clearly affected by motion-induced blurring, and delimited this lesion in a volume of interest (VOI). This VOI was then adjusted to the contours of the lesion with the isocontour tool in PMOD 4.0 (PMOD Technologies Ltd) using the three most commonly used delimitation thresholds: SUV > 2.5, SUV > 41% SUV_max, and SUV > 50% SUV_max. In addition, a reference area in the liver was delimited by drawing a spherical VOI with a radius = 20 mm (volume = 33.5 mL) in an area of hepatic tissue devoid of pathological findings. This method was applied for the 3 gating reconstructions (UG, BG and DDG), and measurements of SUV (mean, max, SD) and metabolic volume were registered.

¹⁸F-FDG/microPET imaging provides a non-invasive way to detect tumor progression and analyze the metabolic function of cancers. MicroPET studies were performed using the R4 system (R4; Concorde Microsystems). Briefly, mice were injected with 19.6–20.1 MBq ¹⁸F-FDG intravenously. Thirty minutes later, mice were anesthetized using 2.5% isoflurane, and imaging data were collected for 30 min with the microPET scanner. Regions of interest (ROIs) were drawn over the tumor area using the standardized uptake value (SUV) and calculated by PMOD 4.0 (PMOD Technologies LLC).

PET/MR image analysis was performed in PMOD 4.0 (PMOD Technologies Ltd., Zurich, Switzerland). Dynamic PET images were interframe motion-corrected using a rigid registration approach with the average of 11 first frames as the reference frame and co-registered with the respective T1-weighted MR images in the PNEURO tool. Time-activity curves from the cortical gray matter (brain and cerebellum), white matter, caudate nucleus, putamen, pallidum, thalamus, whole cerebellum, and brainstem VOIs were extracted using N30R83 Hammers-Atlas [28 (link),29 (link)].

SPECT/CT imaging was performed on a VECTor⁴ small-animal SPECT/PET/OI/CT scanner (MILabs BV, Utrecht, The Netherlands). Static images were acquired with 45-min acquisition time using the HE-GP-RM collimator and a stepwise multi-planar bed movement. All images were reconstructed using the MILabs Rec software (version 10.02) and a pixel-based Similarity-Regulated Ordered Subsets Expectation Maximization (SROSEM) algorithm with a window-based scatter correction (20% below and 20% above the photopeak, respectively; voxel size CT: 80 µm, voxel size SPECT: 0.8 mm, 1.6 mm (FWHM) Gaussian blurring post-processing filter, with calibration factor in kBq/mL and decay correction, no attenuation correction). Image analysis was carried out using PMOD 4.0 (PMOD TECHNOLOGIES LLC, Fällanden, Switzerland).