Clinical assessment of HD-PET reconstruction in oncology PET imaging

Objectives Iterative PET image reconstruction modeling the camera response offers the advantage of improving the spatial resolution while maintaining image noise. As such algorithms are becoming more widely used, in this study we evaluate the clinical applicability of such algorithms for lesion detectability and SUV measurements

Methods PET/CT studies in 10 oncologic subjects consisting of 6 females and 4 males with an average age of 57.2 years (18F-FDG in 9 and 64Cu-ATSM in 1) previously performed on a Biograph-40 PET/CT scanner were retrieved and reconstructed with 3D-OSEM (2 iterations/ 8 subsets - 5 mm Gaussian post-reconstruction filter and using HD PET reconstruction ( 2 iterations, 21 subsets, 2 mm Gaussian post-reconstruction filter). The images were evaluated by two blinded experienced nuclear medicine physicians and image quality was evaluated for the confidence in detecting lesions and the lesion’s maximum standardized uptake value (SUVmax) value was measured. In addition, the average SUV (SUVaverg) values of the liver and blood pool were measured and recorded in both sets of images.

Results For the oncology patients, a total of 24 tumor foci were identified and those were better identified in 67% of the cases (scores of 60% and 73%, respectively for each reader) with HD PET. Peak tumor SUV values were in average 29% higher with HD PET reconstruction relative to the non-HD reconstruction. Liver SUVaverage values were not significantly different between HD and non-HD (HD = 2.45 ± 0.41 and non-HD = 2.29 ± 0.25, p < 0.01) while the blood pool SUVavergshowed a small significant difference (HD = 2.03 ± 0.34 vs non-HD=1.92 ± 0.19, p = 0.19), with consistent measurements from both readers.

Conclusions HD PET image reconstruction showed better lesion detectability and increased SUVmax in the majority of the lesions studied which leads to improved diagnostic accuracy and greater confidence in lesion detectability.

Research Support Department of Radiology, Washington University and Siemens Medical Solutions USA, Inc.