R Matheoud1, O Ferrando2, S Valzano3, D Lizio3, G Sacchetti4, A Ciarmiello5, F Foppiano2, M Brambilla6. 1. Medical Physics Department, University Hospital "Maggiore della Carità", Novara, Italy. Electronic address: roberta.matheoud@maggioreosp.novara.it. 2. Medical Physics Department, Ospedale S.Andrea, Asl5 Spezzino, La Spezia, Italy. 3. Medical Physics Department, University Hospital "Maggiore della Carità", Novara, Italy. 4. Nuclear Medicine Department, University Hospital, "Maggiore della Carità", Novara, Italy. 5. Nuclear Medicine Department, Ospedale S.Andrea, Asl5 Spezzino, La Spezia, Italy. 6. Medical Physics Department, University Hospital "Maggiore della Carità", Novara, Italy. Electronic address: marco.brambilla@maggioreosp.novara.it.
Abstract
PURPOSE: Resolution modeling (RM) of PET systems has been introduced in iterative reconstruction algorithms for oncologic PET. The RM recovers the loss of resolution and reduces the associated partial volume effect. While these methods improved the observer performance, particularly in the detection of small and faint lesions, their impact on quantification accuracy still requires thorough investigation. The aim of this study was to characterize the performances of the RM algorithms under controlled conditions simulating a typical (18)F-FDG oncologic study, using an anthropomorphic phantom and selected physical figures of merit, used for image quantification. METHODS: Measurements were performed on Biograph HiREZ (B_HiREZ) and Discovery 710 (D_710) PET/CT scanners and reconstructions were performed using the standard iterative reconstructions and the RM algorithms associated to each scanner: TrueX and SharpIR, respectively. RESULTS: RM determined a significant improvement in contrast recovery for small targets (≤17 mm diameter) only for the D_710 scanner. The maximum standardized uptake value (SUVmax) increased when RM was applied using both scanners. The SUVmax of small targets was on average lower with the B_HiREZ than with the D_710. Sharp IR improved the accuracy of SUVmax determination, whilst TrueX showed an overestimation of SUVmax for sphere dimensions greater than 22 mm. The goodness of fit of adaptive threshold algorithms worsened significantly when RM algorithms were employed for both scanners. CONCLUSIONS: Differences in general quantitative performance were observed for the PET scanners analyzed. Segmentation of PET images using adaptive threshold algorithms should not be undertaken in conjunction with RM reconstructions.
PURPOSE: Resolution modeling (RM) of PET systems has been introduced in iterative reconstruction algorithms for oncologic PET. The RM recovers the loss of resolution and reduces the associated partial volume effect. While these methods improved the observer performance, particularly in the detection of small and faint lesions, their impact on quantification accuracy still requires thorough investigation. The aim of this study was to characterize the performances of the RM algorithms under controlled conditions simulating a typical (18)F-FDG oncologic study, using an anthropomorphic phantom and selected physical figures of merit, used for image quantification. METHODS: Measurements were performed on Biograph HiREZ (B_HiREZ) and Discovery 710 (D_710) PET/CT scanners and reconstructions were performed using the standard iterative reconstructions and the RM algorithms associated to each scanner: TrueX and SharpIR, respectively. RESULTS: RM determined a significant improvement in contrast recovery for small targets (≤17 mm diameter) only for the D_710 scanner. The maximum standardized uptake value (SUVmax) increased when RM was applied using both scanners. The SUVmax of small targets was on average lower with the B_HiREZ than with the D_710. Sharp IR improved the accuracy of SUVmax determination, whilst TrueX showed an overestimation of SUVmax for sphere dimensions greater than 22 mm. The goodness of fit of adaptive threshold algorithms worsened significantly when RM algorithms were employed for both scanners. CONCLUSIONS: Differences in general quantitative performance were observed for the PET scanners analyzed. Segmentation of PET images using adaptive threshold algorithms should not be undertaken in conjunction with RM reconstructions.