Article Figures & Data
Figures
- FIGURE 1.
Whole-body phantom, shown on PET/CT scanner table (top), consists of brain compartment; thorax with liver, lungs, and rib cage/spine; and pelvis with bladder. It models patient of approximately 92 kg. Coronal CT (bottom left) and PET (bottom right) images show main phantom compartments and structures. Example lesions can also be seen in PET image in both lungs, mediastinum, and pelvis regions.
- FIGURE 2.
Example analysis results used for selecting number of iterations and filter strength for each case studied. Plot on left (A) shows ALROC vs. subiteration for MLEM and OSEM14 (where 1 subiteration represents 1 full pass through data; i.e., 1 iteration MLEM = 1 subiteration, and 1 iteration OSEM14 = 14 subiterations). Here, data are shown for filter that maximized ALROC at each subiteration. Analogous plot on right (B) shows ALROC vs. filter SD, where each datum is shown for number of iterations that maximized ALROC for that filter strength. These data represent portion of multidimensional sampling used to optimize number of iterations and filter strength for phantom data used in this work.
- FIGURE 3.
Example reconstructed images with TOF for each number of OSEM subsets, showing slice in mediastinum with 10-mm-diameter hot lesion in left lung. Each image is shown at approximately 56 MLEM-equivalent iterations. Increasing noise and subtle shape-related artifacts can be observed in images as number of subsets increases.
- FIGURE 4.
Example TOF images for MLEM and OSEM28 with optimal iteration and filter as determined by this study, demonstrating potential effects on lesion detectability. Focus in left lung is true 8-mm hot lesion, and foci in mediastinum are noise artifacts. Horizontal profiles showing relative intensity (arbitrary units) demonstrate that OSEM28 resulted in loss of contrast for lung lesion (right black arrow), coupled with increase in contrast for mediastinal noise blobs (left black arrow), as compared with MLEM. This example represents case in which observer identified correct (lung) lesion on MLEM image (i.e., true-positive reading) but misidentified mediastinal noise blob as lesion on OSEM28 image (i.e., false-positive).
- FIGURE 5.
Lesion-detection performance, as measured by PLOC (A) and ALROC (B), plotted as function of number of OSEM subsets for both TOF and non-TOF reconstructions. Performance declined overall as number of subsets increased, with marked drop in performance beyond approximately 28 subsets. For TOF reconstructions, performance drop at 42 subsets effectively canceled benefit of TOF.
- FIGURE 6.
Comparison of how lesion-detection performance, as quantified by ALROC, is affected by increasing number of OSEM subsets or decreasing scan time for TOF reconstructions. These data provide context for interpreting significance of changes in ALROC observed in this work. For example, use of approximately 32 subsets would result in same loss of performance as shortening scan time from 240 to 180 s per bed position.
Tables
Non-TOF TOF No. of subsets No. of iterations Filter SD (voxels) No. of iterations Filter SD (voxels) 1 120 1.3 72 1.2 2 58 1.3 36 1.2 3 37 1.4 23 1.2 4 24 1.2 21 1.2 6 19 1.4 7 0.9 7 18 1.4 13 1.3 12 10 1.3 5 1.2 14 10 1.5 6 1.1 21 7 1.2 3 0.9 28 7 1.4 3 1.2 42 4 1.2 2 0.8 84 5 1.3 1 0.8
