Validating PET segmentation of thoracic lesions—is 4D PET necessary?

Respiratory-induced motions are prone to degrade the positron emission tomography (PET) signal with the consequent loss of image information and unreliable segmentations. This phantom study aims to assess the discrepancies relative to stationary PET segmentations, of widely used semiautomatic PET segmentation methods on heterogeneous target lesions influenced by motion during image acquisition. Three target lesions included dual F-18 Fluoro-deoxy-glucose (FDG) tracer concentrations as high-and low tracer activities relative to the background. Four different tracer concentration arrangements were segmented using three SUV threshold methods (Max40%, SUV40% and 2.5SUV) and a gradient based method (GradientSeg). Segmentations in static 3D-PET scans (PETsta) specified the reference conditions for the individual segmentation methods, target lesions and tracer concentrations. The motion included PET images followed a 4D-PET (PET4D) and a 3D-PET (PETmot) scan protocol. Moreover, motion-corrected PET images (PETdeb) were derived from the PETmot images. Segmentations in PET4D, PETmot and PETdeb were compared to the PETsta segmentations according to volume changes (Delta Vol) and an error estimate (lowUptake(error)) for the lesion part covering the low tracer concentration. In PET4D images, all segmentation methods provided lowUptakeerror estimates equivalent to PETsta segmentations and, except for the Max40% segmentations, a slight volume expansion. In the PET(mo)t images, the GradientSeg method results in an average 0.43 increased volume and an overestimation of 0.33 for the lowUptakeerror. The most accurate segmentations in PETmot, relative to PETsta, were accomplished by the 2.5SUV and SUV40% methods. In the PETdeb images, the GradientSeg method solitary provided segmentations equivalent to segmentation in PETsta images. The use of FDG with various tracer concentrations revealed, according to PETsta images, that the most constant segmentations for motion-corrected PET images (PET4D or PETdeb) were achieved using the GradientSeg method. In the absence of PET4D or PETdeb images, the 2.5SUV and SUV40% methods are most consistent to PETsta segmentations.