The magnitude of the partial volume effect in SPECT imaging of the kidneys: a phantom study

Abstract

BackgroundSingle-photon emission computed tomography (SPECT) can cause an over- or underestimation of tissue activity concentration due to limitations in spatial resolution compared to the structures under study. This is commonly referred to as partial volume effect (PVE). Ideally, the PVE should be controlled for and corrected. One such correction method involves determining recovery coefficients (RC) from phantom measurements. In the literature, several studies applying simplified geometries are available. In this study, we aimed to determine kidney PVE for realistic kidney geometries. Furthermore, we proposed a new surrogate metric for predicting the extent of PVE in kidneys.Material and methodsBased on patients’ CT data, we manufactured fillable phantoms using a 3D-printer. Nine cortex-only and ten whole-parenchyma phantoms were obtained, and one ellipsoidal phantom for comparison. To measure PVE, we placed the phantoms in a torso phantom and filled them with a specified activity concentration. The phantoms’ RCs were determined from fully quantitative SPECT/CT acquisitions at three different target-to-background ratios (TBRs). Additionally, the surface area-to-volume (SA:V) ratio was determined for all phantoms and correlated with RCs.ResultsFor SPECT reconstructions with 36 iterations, average RC ± one standard deviation at a 10-to-1 TBR was 76.3 ± 1.5% and 48.4 ± 8.3% for whole-parenchyma and cortex-only phantoms, respectively. The RC for the ellipsoidal phantom was 85.4%. The RC for whole-parenchyma was significantly higher than for cortex-only phantoms (p < 0.01). The RC variance was significantly higher for cortex-only phantoms (p < 0.01). A highly significant correlation of the SA:V ratio and RC was found for all phantoms. (R2 of linear regression was between 0.96 and 0.98.)ConclusionChanges in the specific shape of the kidneys cause large changes in PVE magnitude. Therefore, RCs derived from more simple phantoms are most likely insufficient to correct the PVE in patient images. Furthermore, one should account for the fact that intra-renal activity distribution significantly influences the extent of PVE. Additionally, we found that the SA:V ratio excellently models kidney RCs; potentially, this approach could also be applied to other geometries and represents an alternative to full imaging process simulations to determine the extent of PVE.