Impact of Time-of-Flight on PET Tumor Detection

Whole-body phantom (top) consisted of anthropomorphic thorax phantom with lungs and liver, 3D brain phantom, and pelvis compartment with bladder. Inset (top) shows shell-less ⁶⁸Ge lesion embedded in black open cell foam. Custom modifications to the soft-tissue and lung compartments with open-cell foam and nylon bead bags provided inhomogeneous density and structures as shown by CT (bottom). Such heterogeneities more accurately mimic patient structures, providing a more realistic and challenging lesion-detection task than encountered for phantoms with uniform background compartments.

Example reconstructed images for (top-to-bottom) LOR, PSF, TOF, and PSF+TOF. Each image slice shown contains exactly 1 lesion, where relatively obvious cases are shown for purposes of this example. Number of iterations and post-reconstruction filter strength used for each algorithm are given in Table 1. Note that while images show broad similarities, differences in lesion depiction and noise texture exist between algorithms. The effect of these differences upon the task of identifying and localizing focal lesions has been evaluated in this study.

Results of CNPW model observer analysis, plotting area under the LROC curve versus filter strength for each algorithm. Separate lines are shown on each plot for iterations 1–10. These data were used to select optimal reconstruction parameters for images read by human observers. The algorithms including PSF model (B, D) required little or no filter for maximal performance, whereas other algorithms (A, C) required moderate filtering for peak performance.

Final results of model observer study (A) and human observer study (B), providing comparison and ranking of 4 algorithms studied. Model observer results plot area under LROC curve versus number of iterations, where optimal filter for each iteration and algorithm was applied (as determined from results shown in Fig. 4). Note that reconstructions with TOF provided markedly higher performance at earliest iterations, showing that the most important image features for lesion detection are recovered more quickly when using TOF. Human observer LROC curves demonstrate distinctions in performance for 4 algorithms, and confirm model observer results. Quantitative results for these curves are provided in Table 2.

Coronal (top) and sagittal (bottom) sections through ¹⁸F-FDG PET/CT scan of a patient with history of colon cancer. The 71-y-old male patient had BMI of 33.5 and was injected with 396 MBq (10.7 mCi) of ¹⁸F-FDG. After 90-min uptake period, patient underwent CT scan (130 kV; 180 mAs) with both intravenous and oral contrast, followed by PET scan acquired at 6 bed positions for 3 min per bed. PET data were reconstructed into 168 × 168 matrix with (A) baseline algorithm and PSF (4 iterations, 14 subsets, no smoothing), and (B) baseline algorithm with PSF and TOF (2 iterations, 14 subsets, no smoothing) and time resolution kernel of 590 ps.