Impact of PET/CT system, reconstruction protocol, data analysis method, and repositioning on PET/CT precision: An experimental evaluation using an oncology and brain phantom

Abstract

Purpose: In longitudinal oncological and brain PET/CT studies, it is important to understand the repeatability of quantitative PET metrics in order to assess change in tracer uptake. The present studies were performed in order to assess precision as function of PET/CT system, reconstruction protocol, analysis method, scan duration (or image noise), and repositioning in the field of view.

Methods: Multiple (repeated) scans have been performed using a NEMA image quality (IQ) phantom and a 3D Hoffman brain phantom filled with ¹⁸ F solutions on two systems. Studies were performed with and without randomly (< 2 cm) repositioning the phantom and all scans (12 replicates for IQ phantom and 10 replicates for Hoffman brain phantom) were performed at equal count statistics. For the NEMA IQ phantom, we studied the recovery coefficients (RC) of the maximum (SUV_max ), peak (SUV_peak ), and mean (SUV_mean ) uptake in each sphere as a function of experimental conditions (noise level, reconstruction settings, and phantom repositioning). For the 3D Hoffman phantom, the mean activity concentration was determined within several volumes of interest and activity recovery and its precision was studied as function of experimental conditions.

Results: The impact of phantom repositioning on RC precision was mainly seen on the Philips Ingenuity PET/CT, especially in the case of smaller spheres (< 17 mm diameter, P < 0.05). This effect was much smaller for the Siemens Biograph system. When exploring SUV_max , SUV_peak , or SUV_mean of the spheres in the NEMA IQ phantom, it was observed that precision depended on phantom repositioning, reconstruction algorithm, and scan duration, with SUV_max being most and SUV_peak least sensitive to phantom repositioning. For the brain phantom, regional averaged SUVs were only minimally affected by phantom repositioning (< 2 cm).

Conclusion: The precision of quantitative PET metrics depends on the combination of reconstruction protocol, data analysis methods and scan duration (scan statistics). Moreover, precision was also affected by phantom repositioning but its impact depended on the data analysis method in combination with the reconstructed voxel size (tissue fraction effect). This study suggests that for oncological PET studies the use of SUV_peak may be preferred over SUV_max because SUV_peak is less sensitive to patient repositioning/tumor sampling.

Keywords: 3D Hoffman brain phantom; IQ NEMA phantom; PET/CT; phantom repositioning; repeatability; reproducibility.

Figure 1

Illustration of VOIs in gray matter for both brain hemispheres in (A) and VOIs in white matter in (B). These VOIs were used to assess RC for different brain structures and regions as function of experimental condition. [Color figure can be viewed at wileyonlinelibrary.com]

Figure 2

RC of NEMA IQ phantom data as a function of sphere diameter. Data were acquired on the Philips Ingenuity system and based on images with a 4 × 4 × 4 mm³ voxel size and 5‐min starting frame duration using TOF on the left column and TOF + PSF on the right column. Figures (A and B) represent RC (%) for max, (C and D), peak, and (E and F) mean SUVs. Dotted lines correspond to the true RC based on the true activity within the phantom spheres. Boxes represent standard deviation (SD), whiskers show ranges, and solid line depicts median of the data.

Figure 3

RC of NEMA IQ phantom data as a function of sphere diameter. Data were acquired on the Siemens Biograph system and based on images with a 3.1819 × 3.1819 × 2 mm voxel size and 5‐min starting frame duration using TOF on the left column and TOF + PSF on the right column. Figures (A and B) represent RC (%) for max, (C and D), peak, and (E and F) mean SUVs. Dotted lines correspond to the true RC based on the true activity within the phantom spheres. Boxes represent standard deviation (SD), whiskers show ranges, and solid line depicts median of the data.

Figure 4

RC of NEMA IQ phantom data as a function of sphere diameter. Data were acquired on the Philips Ingenuity system and based on images with a 2 × 2 × 2 mm³ voxel size and 5‐min starting frame duration using TOF on the left column and TOF + PSF on the right column. Figures (A and B) represent RC (%) for max, (C and D), peak, and (E and F) mean SUVs. Dotted lines correspond to the true RC based on the true activity within the phantom spheres. Boxes represent standard deviation (SD), whiskers show ranges, and solid line depicts median of the data.

Figure 5

RC of NEMA IQ phantom data as a function of sphere diameter. Data were acquired on the Siemens Biograph system and based on images with a 2 × 2 × 2 mm³ voxel size and 5‐min starting frame duration using TOF on the left column and TOF + PSF on the right column. Figures (A and B) represent RC (%) for max, (C and D), peak, and (E and F) mean SUVs. Dotted lines correspond to the true RC based on the true activity within the phantom spheres. Boxes represent standard deviation (SD), whiskers show ranges, and solid line depicts median of the data.

Figure 6

RC (%) of Hoffman phantom data in different gray matter regions. Data were acquired on the Philips Ingenuity system and reconstructed using TOF (A) and TOF + PSF (B). RC for 5‐min frame duration are shown. Boxes represent standard deviation (SD), whiskers show ranges, and solid line depicts median of the data.

Figure 7

RC (%) of Hoffman phantom for different white matter regions. Data were acquired on the Philips Ingenuity system and reconstructed using TOF are shown (A) and with TOF + PSF in (B). Data for 5‐min frame durations are shown. Boxes represent standard deviation (SD), whiskers show ranges, and solid line depicts median of the data depicts median of the data.