Data acquisition and reconstruction
All imaging data were acquired using a Symbia Intevo16 hybrid SPECT/CT system (Siemens Healthineers, Erlangen, Germany) comprising an integrated dual-head SPECT camera with a 16-slice helical CT scanner. We acquired SPECT images under the following parameters: ± 7.5% energy window at 140 keV with a lower scatter window of 15%, ⅜” crystal thickness, low-energy high-resolution collimator, 256 × 256 matrix with 2.4-mm pixels and a total of 120 projections of 15 s/view over 360° in a non-circular orbit continuous acquisition mode. Immediately following SPECT acquisition, CT images were acquired at 130 KV and 70 ref mA using adaptive dose modulation (CARE Dose 4D; Siemens Healthineers) with a 512 × 512 matrix, pitch 1.5, 0.8-s rotation and 2 × 1.5-mm collimation. The CT data were reconstructed at a 3.0-mm slice thickness using a B31s attenuation filter (Siemens Healthineers).
We reconstructed the SPECT images using the algorithms F3D, xQ and xB and a 6-mm 3D Gaussian filter with various combinations of one fixed subset and 1-96 iterations. The F3D is equipped with OSEM and depth-dependent 3D resolution recovery using the Gaussian point-spread functions, AC and SC. The xQ and xB are equipped with OSCGM and depth-dependent 3D resolution recovery using AC and SC. The xB algorithm divides CT pixels into six tissue classes with smooth boundaries based on CT values or “zones” of air and lung, adipose, soft tissue, soft bone, cortical bone, metal material, and updates. The xB iterative operation can be weighted according to the corresponding zone class in the divided pixel; however, the iterative operation for each zone class based on the CT data did not increase the original count .
Cross-calibration of SPECT imaging
Counts from reconstructed SPECT images derived from F3D and xSPECT were converted to activity concentrations based on a cross-calibration factor (CCF) obtained from the relationship between the reconstructed counts and activity concentrations as well as system planar sensitivity, for quantitative comparisons.
In SPECT images using F3D, a circular region of interest (ROI) to measure SPECT count density (counts/mL) was placed at the center of the cylindrical phantom on the central slice and at ±1 and ±2 slices from the center. The CCF was automatically calculated using GI-BONE software (Aze, Tokyo, Japan) as the ratio of the actual activity concentration (measured by the dose calibrator) in the phantom at the time of scanning to the measured SPECT counts density per scan duration . The dose calibrator used for cross-calibration was CRC-15R. (final calibration date by manufacturer: 4/19/2005) The dose calibrator was also confirmed and calibrated with a site-specific NIST-traceable 68Ge/68Ga source every 3 months [31, 32] (final calibration date in site: 12/18/2019). Therefore, we consider that the uncertainty of the measurement by the dose calibrator is small. The actual SUV was calculated as:
Reconstruction with xQ and xB precisely determines images in units of Bq/mL that are converted using system planar sensitivity with an NIST traceable 57Co source . The system planar sensitivity is a necessary parameter to allow for conversion between the count rate and units of absolute activity. This is defined as a measure of how many counts the gamma camera detects for every unit of activity in its field of view. Therefore, system planar sensitivity was measured with the traceable point source without scattering and attenuation to realize accurate and reproducible quantitation [28, 33]. This source is recommended for all Siemens® users to improve SPECT quantitation. We automatically converted the quantitative SPECT/CT data using MI Applications VB10 (Siemens Healthineers).
We custom-designed a physical three-dimensional phantom to determine the bone SPECT-specific distribution of activity and the linear attenuation coefficient (Figure 1). This phantom can be used to generate SPECT images of bone metastasis with a realistic abdomen contour . The phantom contains a 99mTc solution to simulate soft tissue, the vertebral body, spinous and transverse process, and tumor region contained a bone-equivalent solution of K2HPO4 and 99mTc . The phantom experiments were conducted twice using different activity concentrations and sphere sizes as follows. Tumor, normal bone, and soft tissues in the phantom were immersed in a solution of 99mTc. In the first round of experiments, a body phantom with four 28-mm diameter spheres was set and acquired at tumor-to-normal bone ratios (TBR) of 1, 2, 4 and 10 at a normal bone activity level of 50 kBq/mL. This phantom contained 8 kBq/mL of a 99mTc solution as the background activity of the soft tissue. That is, the boundary and the background do not differ at TBR1, but the difference in the activity concentration increases as a function a higher TBR. We determined the activity concentrations of the simulated soft tissue, normal bone, and tumor at 8, 50 and 200 kBq/mL, (TBR4), respectively, in second round of experiments using a phantom with 13-, 17-, 22-, 28-mm diameter spheres.
The SPECT acquisition data in the first round of experiments were reconstructed using subset 1 and 1-96 iterations. We examined the effects of the reconstruction algorithms on various TBR in the 28-mm sphere and then determined the optimal reconstruction parameters based on the result of convergence characteristics. Phantom images containing simulated tumors of different sizes were continuously analyzed in terms of the spatial resolution of a 10-mm spinous process, the coefficient of variance (CV), the contrast-to-noise ratio (CNR) of the vertebral body and RC as quantitative parameters. We drew profile curves on the spinous process, measured the full width at half maximum (FWHM), and evaluated the CV at an 80% circular ROI (ROI80%) placed at the center of the vertebral body. The CV was calculated as SD divided by the mean, where SD is the standard deviation of the ROI in the radioactive section and the mean is the mean SPECT value (kBq/mL) in the ROI. The CNR and RC at each sphere were determined by setting circular ROI with diameters of 13-, 17-, 22-, 28-mm. The CNR at TBR 4 was calculated as (Hs-Hnb)/σnb, where, Hs and Hnb are the activity concentrations measured in the spheres and normal bone, respectively, and σnb is the voxel SD in the normal bone. The RC was defined as the ratio of the mean and true SPECT values (kBq/mL) for each sphere.
We analyzed data from 20 consecutive patients who had undergone bone SPECT/CT imaging for metastatic prostate or breast cancer (male, n = 13; female, n = 7; median age, 62 years; range, 40–83 years; average weight, 65.2 ± 13.4 kg; range, 51.8–78.6 kg). The optimal condition of the convergence characteristic in the phantom study was applied to the clinical reconstruction parameters in xQ and xB. Bone SPECT/CT imaging proceeded from the abdomen to the pelvis ~2.5–4 h after delivering an intravenous injection of 1003.4 ± 102.8 MBq 99mTc-methylene diphosphonate (99mTc-MDP; FUJIFILM Toyama Chemical, Tokyo, Japan) or hydroxymethylene diphosphonate (99mTc-HMDP; Nihon Medi-Physics, Tokyo, Japan). The average amount of injected 99mTc was 15.9 ± 2.8 (range, 13.1–18.7) MBq/kg. The Ethics Committee at the Cancer Institute Hospital of JFCR approved this clinical study (Approval no. 2015-1151). These clinical data were retrospectively analyzed herein, and the results did not influence any further therapeutic decision-making.
The noise characteristics and quantitative performance of the clinical SPECT image were analyzed at the level of the 4th vertebral body (L4) . We placed a ROI of 80% on the center of the axial slice in the vertebral body section, and another precisely on the corresponding vertebral body in the central slice guided by the CT boundaries of the fused SPECT/CT images (Figure 2). We normalized the SUVmax, SUVmean and SUVpeak by the weight of each patient. These data were analyzed using PETSTAT software (AdIn Research, Tokyo, Japan).
All SUV and CV indices in the xQ and xB groups were compared using Wilcoxon signed-ranks tests after evaluating the non-normal distribution using Kolmogorov-Smirnov tests. Values were considered statistically significant when P < 0.05. These data were statistically analyzed using SPSS Statistics software (IBM corp, Armonk, NY, USA).