Optimization of a dedicated protocol using a small-voxel PSF reconstruction for head-and-neck (18)FDG PET/CT imaging in differentiated thyroid cancer

BACKGROUND: (18)FDG PET/CT is crucial before neck surgery for nodal recurrence localization in iodine-refractory differentiated or poorly differentiated thyroid cancer (DTC/PDTC). A dedicated head-and-neck (HN) acquisition performed with a thin matrix and point-spread-function (PSF) modelling in addition to the whole-body PET study has been shown to improve the detection of small cancer deposits. Different protocols have been reported with various acquisition times of HN PET/CT. We aimed to compare two reconstruction algorithms for disease detection and to determine the optimal acquisition time per bed position using the Siemens Biograph6 with extended field-of-view. METHODS: Twenty-one consecutive and unselected patients with DTC/PDTC underwent HN PET/CT acquisition using list-mode. PET data were reconstructed, mimicking five different acquisition times per bed position from 2 to 10 min. Each PET data set was reconstructed using 3D-ordered subset expectation maximisation (3D-OSEM) or iterative reconstruction with PSF modelling with no post filtering (PSF(allpass)). These reconstructions resulted in 210 anonymized datasets that were randomly reviewed to assess (18)FDG uptake in cervical lymph nodes or in the thyroid bed using a 5-point scale. Noise level, maximal standard uptake values (SUVmax), tumour/background ratios (TBRs) and dimensions of the corresponding lesion on the CT scan were recorded. In surgical patients, the largest tumoral size of each lymph node metastasis was measured by a pathologist. RESULTS: The 120 HN PET studies of the 12 patients with at least 1 (18)FDG focus scored malignant formed the study group. Noise level significantly decreased between 2 and 4 min for both 3D-OSEM and PSF(allpass) reconstructions (p < 0.01). TBRs were similar for all the acquisition times for both 3D-OSEM and PSF(allpass) reconstructions (p = 0.25 and 0.44, respectively). The detection rate of malignant foci significantly improved from 2 to 10 min for PSF(allpass) reconstruction (20/26 to 26/26; p = 0.01) but not for 3D-OSEM (15/26 to 19/26; p = 0.26). For each of the five acquisition times, PSF(allpass) detected more malignant foci than 3D-OSEM (p < 0.01). In the seven surgical patients, PSF(allpass) evidenced smaller malignant lymph nodes than 3D-OSEM at 8 and 10 min. At 10 min, the mean size of the lymph node metastases neither detected with PSF(allpass) nor 3D-OSEM was 3 ± 0.6 mm vs 5.8 ± 1.1 mm for those detected with PSF(allpass) only and 10.9 ± 3.3 for those detected with both reconstructions (p < 0.001). CONCLUSIONS: PSF(allpass) HN PET improves lesion detectability as compared with 3D-OSEM HN PET. PSF(allpass) with an acquisition time between 8 and 10 min provides the best performance for tumour detection.