Page 75 - Quantitative Imaging of Small Tumours with Positron Emission Tomography

Chapter 3 has been proposed with use of VOIs defined on uncorrected images, due to the expected propagation of image noise after PVC (30). We evaluated the impact of delineation on deconvoluted images with HYPR denoising, and found not only substantial decreases in MATVs (Figure 3) but also an increase in PVCs effect on kinetic parameter estimates (Supplemental Table 3). Nonetheless, our previous study demonstrated that the reduction in MATV after PVC may not necessarily lead to more accurate definition of tumour volumes (11). In brain PET studies, frequently a small vessel such as the carotid artery needs to be utilized for IDIF generation. This mandates PVC due to the small artery diameter (32,33). In this study on thoracic oncological PET-CTs, the ascending aorta, a large vessel, was used for IDIF generation. We noted that PVC introduced negligible differences in IDIF area-under-the-curves, and that without denoising this introduced small but significant differences in kinetic parameter estimates (Supplemental Table 2). However, since HYPR denoising using a single composite image (providing maximum noise reduction) appeared to completely mitigate this effect, the effect of PVC on these input functions seems to be based on PVC-induced noise-propagation. Therefore, when input functions derived from large blood pool structures are used, PVC is preferably avoided to evade noise-induced inaccuracies in kinetic parameter estimates (assuming no spillover from nearby high activity structures). Iterative deconvolution algorithms are known to propagate image noise, which may necessitate denoising methods to be applied to preserve image quality. Several approaches have been proposed, such as wavelet-based denoising for static PET-CT and HYPR denoising for dynamic acquisitions, respectively (26,34). We observed that HYPR needs to be optimized for tracer kinetics using a moving composite image, since when applied using a single composite image (maximal denoising) it seems to lose the temporal dynamic course of the PVC (Figure 3.1). Including HYPRmoving resulted in very similar outcomes compared to PVC alone, and slightly mitigated the increase in kinetic parameter estimates after PVC. The latter may not only be attributed to reduced statistical noise, but also to some smoothing effects inherent to the algorithm. Also, at late time frames it had no effect on intratumoural COV% (Supplemental Figure 1). This might be explained by the high tumour contrast and high count number (due to the long frame duration), as Golla et al. previously demonstrated (21). The increase in COV% at late time frames thus seems to be a resultant of increased intratumoural heterogeneity by PVC itself. Therefore, in region-based non-linear 74