FDG-PET/CT imaging plays an important role in the diagnostic evaluation of cancer. However, this technique has two major limitations: a low spatial resolution and a limited system sensitivity. Consequently, the detection of small lesions (<20 mm) is limited and PET images have a relatively low signal-to-noise ratio. Since the clinical introduction of whole-body PET 20 years ago, several techniques were introduced, such as new reconstruction methods and digital detectors, to improve the image quality and diagnostic performance of PET. The aim of this thesis was to evaluate the impact of these new PET techniques on the detection of small lesions in cancer imaging. We studied the influence of conventional TOF-PET scanners and small-voxel reconstructions on small lesion detectability in lung and breast cancer. Furthermore, we studied digital TOF-PET scanners and determined their impact on semi-quantitative uptake measurements, image quality and lesion detectability in patients with cancer. Moreover, we evaluated the impact of conventional and digital PET scanners on European guidelines and procedure standards (EARL) for PET imaging using two different radionuclides. We performed three retrospective and two prospective studies in patients with cancer. We found that the use of smaller voxels and digital PET technology resulted in an improved detection of small lesions with higher standardized uptake values, higher signal-to-noise ratio’s, improved visual image quality and disease upstaging in some cases. We also demonstrated that images acquired with state-of-the-art PET scanners provided higher uptake values for both benign and malignant lesions. Therefore, PET readers should adapt their reference standards to distinguish benign from malignant when evaluating PET images acquired on such modern PET systems. Meanwhile, we also demonstrated that when using standardised EARL protocols and following European guidelines, conventional and digital PET systems can be used interchangeably.
|Qualification||Doctor of Philosophy|
|Award date||22 Nov 2019|
|Publication status||Published - 22 Nov 2019|