This retrospective observation study was approved by the institutional review board (approval no. 017–0454). The requirement of written informed consent from each patient was waived because of the study’s retrospective nature. We confirmed that all methods were carried out in line with the relevant guidelines and regulations. A total of 30 PET-CT scans (sequential examinations for each scanner) were investigated in this study. No more than one scan was included for each patient. All the images were acquired between April 2019 and November 2019. Images were evaluated visually, and included to the study population if there were any pathological FDG uptakes in visual analysis until the number of scans reached 10 for each scanner. When all the FDG accumulation mass were considered physiological, the case was excluded. Note that not only uptake due to pathological malignancy but also malignancy-suspected and inflammatory uptakes were included in the analysis. In cases in which more than 5 uptakes were found, a maximum of 5 uptakes were recorded for a patient. An experienced nuclear medicine physician visually evaluated all the images.
PET-CT image acquisition and reconstruction
In this study, we investigated images acquired with 3 different PET-CT scanners made by 2 different manufacturers.
Scanner 1 was a Biograph 64 True Point PET-CT (Asahi-Siemens Medical Technologies, Tokyo). The transaxial and axial fields of view were 68.4 cm and 21.6 cm, respectively. Images were reconstructed using the OSEM algorithm with point spread function correction. Time-of-flight of photons was not measurable with the scanner. The reconstructed images had a matrix size of 168 × 168 and a voxel size of 4.1 × 4.1 × 2.0 mm.
Scanner 2 was a GEMINI TF64 PET-CT (Philips Japan, Tokyo). The transaxial and axial fields of view were 57.6 cm and 18.0 cm, respectively. Images were reconstructed using the OSEM algorithm reinforced with the time-of-flight algorithm. Point spread function correction was not applied. The reconstructed images had a matrix size of 144 × 144 and a voxel size of 4.0 × 4.0 × 4.0 mm.
Scanner 3 was a Vereos PET-CT (Philips Japan, Tokyo), which was the newest scanner of the three and equipped with digital photon counting detectors (19). The transaxial and axial fields of view were 67.6 cm and 16.4 cm, respectively (19). Images were reconstructed using the OSEM algorithm. Both the time-of-flight algorithm and point spread function correction were applied. The reconstructed images had a matrix size of 256 × 256 and a voxel size of 2.0 × 2.0 × 2.0 mm.
The number of voxels in the z-direction (i.e., cranio-caudal direction) ranged from 234 to 553, resulting in the final number of voxels ranging from 4.85 × 106 to 4.41 × 107. CT images were used for attenuation correction for all the scanners and for visual assessment, but were not analyzed quantitatively in the current study. All patients fasted for ≥ 6 hours before the injection of FDG (approx. 4 MBq/kg), and the emission scanning was initiated basically around 60 min post-injection. One scan was acquired 95 min post-injection due to mechanical troubles. Fasting blood sugar was confirmed to be smaller than 200 mg/dl in each study.
Commercially available DICOM viewers / PET viewers do not display SUVmax to 4 decimal places (DP) or higher. In order to obtain the ground truth of SUVmax, we modified Metavol software package, which we previously developed for PET-CT volumetric analysis (20). We used Windows 10, Microsoft Visual Studio Community 2019 Version 16.4.0, C# 8.0 language, .NET Core 3.1, and fo-dicom 4.0.3 for modifying Metavol. For instance, in the case that the true SUVmax is 3.14159, the modified Metavol will display it as it is, whereas XTREK VIEW software (J-MAC SYSTEM, Sapporo, Japan) will display it as 3.142. A nuclear medicine physician measured SUVmax by placing a spherical volume of interest (VOI) whose diameter can be changed by the operator.
After the VOI definition, SUVmax was calculated based on the newest QIBA publication (21). Briefly, in Biograph64 and Vereos, the radioactivity concentration c (Bq/ml) was calculated as follows:
c = ρ・s + i .
Here, ρ represents the raw pixel value that was stored with DICOM tag of (7FE0,0010) with each voxel expressed in a 16-bit integer. s represents the rescale slope, which is stored as a float value at (0028,1053). i represents the rescale intercept, which is stored as a float value at (0028,1052). Next, decay-corrected injection dosage Dc was calculated as follows:
Dc = D0・(1/2)(Ta − Ti)/h .
Here, D0 represents the radionuclide total dose (i.e., injected dosage of FDG) (Bq) stored as a float value at (0018,1074). Ta represents acquisition time stored at (0008,0032). Ti represents the radiopharmaceutical start time (i.e., injection time) stored at (0018,1072). Both times are stored in a “hhmmss” form string, and thus conversion to second is needed. h represents the radionuclide half-life (second) stored as a float value at (0018,1075).
Finally, SUV was calculated as follows:
SUV = c・w / Dc .
Here, w represents the patient’s weight (g), which is stored in kilograms at (0010,1030) and thus must be multiplied by 1000.
The SUV calculation was much simpler in GEMINI TF64, as follows:
SUV = (ρ・s + i) ・p .
Here, p represents the Philips Factor (float) stored as a float value at (7053, 1000). The values of s and i were 1 and 0, respectively, for all the GEMINI TF64 examinations investigated in the current study.
We implemented a function that searches voxels satisfying the given SUV range and illustrate the locations in the whole body image (Figs. 2–4). The SUV range was determined as follows. When “3” was provided by the operator, the range was considered to be 2.5 ≤ SUV < 3.5. When “3.1” was provided, the voxels satisfying 3.05 ≤ SUV < 3.15 were picked out, and so forth. Thus, the more precise the provided value of SUVmax (i.e., more digits) was, the narrower the range of SUV applied to extract voxels was. We compared the results from integer precision to 4th DP precision. Note that we do not show the results of 5th DP precision because there were no cases in which 5th DP precision improved the identification rate compared to 4th DP precision.
We performed experiments in different settings. First, the voxels within the range were extracted simply. Then, local maximum restriction was added to discard the voxel that was adjacent to the higher-value voxel, because such a voxel cannot have SUVmax. For local maximum restriction, milder restriction and stricter restriction were tested. Milder restriction was a condition under which the voxel must be highest in the 3 × 3 × 3 cube. Stricter restriction was a condition under which the voxel must be highest in the 5 × 5 × 5 cube.
Here, we defined that “identical detection” was achieved when only 1 voxel satisfied the criterion.
The relationship between SUVmax vs. the number of voxels detected (N) was estimated using Pearson’s correlation coefficient of the log of SUVmax vs. the log of N. The effects of the precision of SUVmax, i.e., the number of digits after the decimal point, and local maximum restriction on the rate of identical detection were tested using a chi-square test. P values less than 0.05 were considered statistically significant.