“…For smaller vessels (diameter < 8 mm), like the femoral artery, a partial volume correction may be done using the CT data and on the basis of phantom measurements of the recovery function. In recent years, novel approaches for calculation of image-derived arterial input function using integrated PET/CT and PET/MRI images have been developed with very promising results [ 26 , 27 ]. Fig.…”
Dynamic PET (dPET) studies have been used until now primarily within research purposes. Although it is generally accepted that the information provided by dPET is superior to that of conventional static PET acquisitions acquired usually 60 min post injection of the radiotracer, the duration of dynamic protocols, the limited axial field of view (FOV) of current generation clinical PET systems covering a relatively small axial extent of the human body for a dynamic measurement, and the complexity of data evaluation have hampered its implementation into clinical routine. However, the development of new-generation PET/CT scanners with an extended FOV as well as of more sophisticated evaluation software packages that offer better segmentation algorithms, automatic retrieval of the arterial input function, and automatic calculation of parametric imaging, in combination with dedicated shorter dynamic protocols, will facilitate the wider use of dPET. This is expected to aid in oncological diagnostics and therapy assessment. The aim of this review is to present some general considerations about dPET analysis in oncology by means of kinetic modeling, based on compartmental and noncompartmental approaches, and parametric imaging. Moreover, the current clinical applications and future perspectives of the modality are outlined.
“…For smaller vessels (diameter < 8 mm), like the femoral artery, a partial volume correction may be done using the CT data and on the basis of phantom measurements of the recovery function. In recent years, novel approaches for calculation of image-derived arterial input function using integrated PET/CT and PET/MRI images have been developed with very promising results [ 26 , 27 ]. Fig.…”
Dynamic PET (dPET) studies have been used until now primarily within research purposes. Although it is generally accepted that the information provided by dPET is superior to that of conventional static PET acquisitions acquired usually 60 min post injection of the radiotracer, the duration of dynamic protocols, the limited axial field of view (FOV) of current generation clinical PET systems covering a relatively small axial extent of the human body for a dynamic measurement, and the complexity of data evaluation have hampered its implementation into clinical routine. However, the development of new-generation PET/CT scanners with an extended FOV as well as of more sophisticated evaluation software packages that offer better segmentation algorithms, automatic retrieval of the arterial input function, and automatic calculation of parametric imaging, in combination with dedicated shorter dynamic protocols, will facilitate the wider use of dPET. This is expected to aid in oncological diagnostics and therapy assessment. The aim of this review is to present some general considerations about dPET analysis in oncology by means of kinetic modeling, based on compartmental and noncompartmental approaches, and parametric imaging. Moreover, the current clinical applications and future perspectives of the modality are outlined.
“…These methods, however, usually suffer from partial volume effects due to limited spatial resolution of the PET image. With combined PET/MR, the highresolution MR image can be used to outline the arteries and when combined with partial volume correction approaches [8], provide a more accurate blood input function for kinetic modeling [9].…”
Background: The positron emission tomography (PET) ligand 68 Ga-Glu-urea-Lys(Ahx)-HBED-CC (68 Ga-PSMA-11) targets the prostate-specific membrane antigen (PSMA), upregulated in prostate cancer cells. Although 68 Ga-PSMA-11 PET is widely used in research and clinical practice, full kinetic modeling has not yet been reported nor have simplified methods for quantification been validated. The aims of our study were to quantify 68 Ga-PSMA-11 uptake in primary prostate cancer patients using compartmental modeling with arterial blood sampling and to validate the use of standardized uptake values (SUV) and image-derived blood for quantification. Results: Fifteen patients with histologically proven primary prostate cancer underwent a 60-min dynamic 68 Ga-PSMA-11 PET scan of the pelvis with axial T1 Dixon, T2, and diffusion-weighted magnetic resonance (MR) images acquired simultaneously. Time-activity curves were derived from volumes of interest in lesions, normal prostate, and muscle, and mean SUV calculated. In total, 18 positive lesions were identified on both PET and MR. Arterial blood activity was measured by automatic arterial blood sampling and manual blood samples were collected for plasmato-blood ratio correction and for metabolite analysis. The analysis showed that 68 Ga-PSMA-11 was stable in vivo. Based on the Akaike information criterion, 68 Ga-PSMA-11 kinetics were best described by an irreversible two-tissue compartment model. The rate constants K 1 and k 3 and the net influx rate constants K i were all significantly higher in lesions compared to normal tissue (p < 0.05). K i derived using image-derived blood from an MR-guided method showed excellent agreement with K i derived using arterial blood sampling (intraclass correlation coefficient = 0.99). SUV correlated significantly with K i with the strongest correlation of scan time-window 30-45 min (rho 0.95, p < 0.001). Both K i and SUV correlated significantly with serum prostate specific antigen (PSA) level and PSA density. Conclusions: 68 Ga-PSMA-11 kinetics can be described by an irreversible two-tissue compartment model. An MR-guided method for image-derived blood provides a non-invasive alternative to blood sampling for kinetic modeling studies. SUV showed strong correlation with K i and can be used in routine clinical settings to quantify 68 Ga-PSMA-11 uptake.
“…Other methods of PVE correction, such as those relying on recovery coefficients, will apply a linear correction to the ROI, based solely on size and regardless of the relative intensity of the background region and tube [ 1 , 6 , 23 , 24 , 25 , 26 , 27 ]. This may result in an AIF with a misrepresentative shape.…”
Section: Discussionmentioning
confidence: 99%
“…The most common approach applies the use of recovery coefficients, which is a typical approach applied in static PET scans. This approach involves performing a relative correction, using a constant for a region based on its true size [ 1 , 5 , 23 , 24 , 25 , 26 , 27 ]. These approaches are primarily designed for spherical lesions and intended to correct only the SUV max (a measurement of 1 voxel) of a region of interest, leaving the SUV mean unaccounted for.…”
Dynamic PET (dPET) imaging can be utilized to perform kinetic modelling of various physiologic processes, which are exploited by the constantly expanding range of targeted radiopharmaceuticals. To date, dPET remains primarily in the research realm due to a number of technical challenges, not least of which is addressing partial volume effects (PVE) in the input function. We propose a series of equations for the correction of PVE in the input function and present the results of a validation study, based on a purpose built phantom. 18F-dPET experiments were performed using the phantom on a set of flow tubes representing large arteries, such as the aorta (1” 2.54 cm ID), down to smaller vessels, such as the iliac arteries and veins (1/4” 0.635 cm ID). When applied to the dPET experimental images, the PVE correction equations were able to successfully correct the image-derived input functions by as much as 59 ± 35% in the presence of background, which resulted in image-derived area under the curve (AUC) values within 8 ± 9% of ground truth AUC. The peak heights were similarly well corrected to within 9 ± 10% of the scaled DCE-CT curves. The same equations were then successfully applied to correct patient input functions in the aorta and internal iliac artery/vein. These straightforward algorithms can be applied to dPET images from any PET-CT scanner to restore the input function back to a more clinically representative value, without the need for high-end Time of Flight systems or Point Spread Function correction algorithms.
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