Clinical feasibility and impact of data-driven respiratory motion compensation studied in 200 whole-body 18F-FDG PET/CT scans

Abstract

Background: This study examines the clinical feasibility and impact of implementing a fully automated whole-body PET protocol with data-driven respiratory gating in patients with a broad range of oncological and non-oncological pathologies 592 FDG PET/CT patients were prospectively included. 200 patients with lesions in the torso were selected for further analysis, and ungated (UG), belt gated (BG) and data-driven gating (DDG) images were reconstructed. All images were reconstructed using the same data and without prolonged acquisition time for gated images. Images were quantitatively analysed for lesion uptake and metabolic volume, complemented by a qualitative analysis of visual lesion detection. In addition, the impact of gating on treatment response evaluation was evaluated in 23 patients with malignant lymphoma.

Results: Placement of the belt needed for BG was associated with problems in 27% of the BG scans, whereas no issues were reported using DDG imaging. For lesion quantification, DDG and BG images had significantly greater SUV values and smaller volumes than UG. The physicians reported notable image blurring in 44% of the UG images that was problematic for clinical evaluation in 4.5% of cases.

Conclusion: Respiratory motion compensation using DDG is readily integrated into clinical routine and produce images with more accurate and significantly greater SUV values and smaller metabolic volumes. In our broad cohort of patients, the physicians overwhelmingly preferred gated over ungated images, with a slight preference for DDG images. However, even in patients with malignant disease in the torso, no additional diagnostic information was obtained by the gated images that could not be derived from the ungated images.

Keywords: Data driven gating; FDG; Gating; PET.

Fig. 1

Example of VOI delimitations used for the Deauville scoring. This patient with a T-cell lymphoma displayed multiple new lesions in the thorax area. Deauville score = 5

Fig. 2

Bland–Altman plots of the “most blurry” lesion (N = 200), representing the differences between SUV_max values (A–C), and differences between metabolic volumes (D–F). Note: For the smallest lesions, very high volume differences (in %) can be caused by the inclusion/exclusion of few voxels

Fig. 3

Bland–Altman plots for the fixed liver VOI, representing the differences between SUV_max values. N = 199 (one patient excluded due to artefact in liver region, see Fig. 4)

Fig. 4

This 48-year-old man was scanned as part of lymphoma evaluation. The belt-based gating (BG) reconstruction introduced a “band” artefact (arrows) over the liver region, which hindered clinical evaluation

Fig. 5

A 79-year-old male patient with lung cancer displaying a classic motion artefact. The ungated image (UG) appears to have a double contouring of the primary tumour (arrows), whereas the two motion compensated images (BG, DDG) clearly reveal a single lesion with sharper contours, smaller metabolic volume and increase in SUV values

Fig. 6

Changes in target-to-background ratios (TBR’s) and corresponding Deauville scores using the 3 different gating reconstructions. Deauville scores are reported as a range on a continuous TBR scale as SUV_max in the lesion divided by SUV_max in reference tissue (liver and/or mediastinum)