Image‐derived and arterial blood sampled input functions for quantitative PET imaging of the angiotensin II subtype 1 receptor in the kidney

Feng, Tao; Tsui, B.M.W.; Li, Xin; Vranesic, Melin; Lodge, Martin A.; Gülaldı, Nedim; Szabó, Zsolt

doi:10.1118/1.4934375

Cited by 15 publications

(11 citation statements)

References 23 publications

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“…Therefore a Logan graphical analysis is assumed to be less dependent on the accurate determination of the rapidly changing peak of the input function which could be poorly estimated 23 . This is confirmed by several previous studies 25 , 38 , 39 . However, since metabolization of [ 18 F]JNJ-739 is rather fast (only 30% ± 5% of parent tracer at 20 min post-injection), the contribution of later time points of the metabolite-corrected arterial plasma input function is limited such that cumulated activity concentrations at later time points, which are used for the Logan graphical analysis estimation, are more dependent on the accurate estimation of peak activity concentrations, shortly after tracer injection.…”

Section: Discussionsupporting

confidence: 92%

“…Partial volume effects (PVE) between PET activity concentration in the carotid arteries ( ) and corresponding background were modeled as: where and represented the whole blood activity concentration and the PET activity concentration in the background VOI, respectively, while w was the partial volume weighting factor which models the spill-in and spill-over effects ( ) between background and carotid arterial activity 25 – 27 .…”

Section: Methodsmentioning

confidence: 99%

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Minimally invasive quantification of cerebral P2X7R occupancy using dynamic [18F]JNJ-64413739 PET and MRA-driven image derived input function

Mertens

Schmidt

Hijzen

et al. 2021

Sci Rep

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Abstract[18F]JNJ-64413739 has been evaluated as PET-ligand for in vivo quantification of purinergic receptor subtype 7 receptor (P2X7R) using Logan graphical analysis with a metabolite-corrected arterial plasma input function. In the context of a P2X7R PET dose occupancy study, we evaluated a minimally invasive approach by limiting arterial sampling to baseline conditions. Meanwhile, post dose distribution volumes (VT) under blocking conditions were estimated by combining baseline blood to plasma ratios and metabolite fractions with an MR angiography driven image derived input function (IDIF). Regional postdose VT,IDIF values were compared with corresponding VT,AIF estimates using a arterial input function (AIF), in terms of absolute values, test–retest reliability and receptor occupancy. Compared to an invasive AIF approach, postdose VT,IDIF values and corresponding receptor occupancies showed only limited bias (Bland–Altman analysis: 0.06 ± 0.27 and 3.1% ± 6.4%) while demonstrating a high correlation (Spearman ρ = 0.78 and ρ = 0.98 respectively). In terms of test–retest reliability, regional intraclass correlation coefficients were 0.98 ± 0.02 for VT,IDIF compared to 0.97 ± 0.01 for VT,AIF. These results confirmed that a postdose IDIF, guided by MR angiography and using baseline blood and metabolite data, can be considered for accurate [18F]JNJ-64413739 PET quantification in a repeated PET study design, thus avoiding multiple invasive arterial sampling and increasing dosing flexibility.

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Section: Discussionsupporting

confidence: 92%

Section: Methodsmentioning

confidence: 99%

Minimally invasive quantification of cerebral P2X7R occupancy using dynamic [18F]JNJ-64413739 PET and MRA-driven image derived input function

Mertens

Schmidt

Hijzen

et al. 2021

Sci Rep

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“…The variance of the estimation can be high for voxel‐wise analysis. Another disadvantage of such method is the requirement for accurate plasma input functions, which either requires invasive blood sampling or longer acquisitions from the beginning of injection to generate image‐derived input function 14 . As a result, graphical methods such as Patlak analysis have been widely adopted due to its simplicity and robustness 15 .…”

Section: Introductionmentioning

confidence: 99%

“…Another disadvantage of such method is the requirement for accurate plasma input functions, which either requires invasive blood sampling or longer acquisitions from the beginning of injection to generate image-derived input function. 14 As a result, graphical methods such as Patlak analysis have been widely adopted due to its simplicity and robustness. 15 Studies have shown that Patlak slope is a more accurate index of glucose metabolic rate than SUV because of the unmetabolized FDG measured by SUV and the inability of SUV to account for the available dose.…”

mentioning

confidence: 99%

Simplified protocol for whole‐body Patlak parametric imaging with ¹⁸F‐FDG PET/CT: Feasibility and error analysis

et al. 2021

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Parametric imaging using the Patlak model has been shown to provide improved lesion detectability and specificity. The Patlak model requires both tissue time-activity curves (TACs) after equilibrium and knowledge of the input function from the start of injection. Therefore, the conventional dynamic scanning protocol typically starts from the radiotracer injection all the way to equilibrium. In this paper, we propose the use of hybrid population-based and model-based input function estimation and evaluate its use for whole-body Patlak analysis, in order to reduce the total scan time and simplify clinical Patlak parametric imaging protocols. Possible quantitative errors caused by the simplified scanning protocol were also analyzed both theoretically and with the use of clinical data. Materials and methods: Clinical data from 24 patients referred for tumor staging were included in this study. The patients underwent a whole-body dynamic PET study, 20 min after FDG injection (0.13 mCi/kg). The proposed whole-body scanning protocol includes 6 passes with 4-5 bed positions, depending on the size of the patient, with 2 min for each bed position. An input function from the literature was selected as the shape of the population-based input function. The descending aorta from the corresponding CT image was segmented and applied on the reconstructed dynamic PET images to acquire an image-based input function, which was later fitted using an exponential model. Due to the late scan time, only the later portion of the input function was available, which was used to scale the population-based input function. The hybrid input function was used to derive the wholebody Patlak images. Assuming a given error in the population-based input function, its influence on the final Patlak images were also derived theoretically and verified using the clinical data sets. Finally, the image quality of the reconstructed Patlak slope image was evaluated by an experienced radiologist in four different aspects: image artifacts, image noise, lesion sharpness, and lesion detectability. Results: It was found that errors in the population-based input function only affect the absolute scale of the Patlak slope image. The induced error is proportional to the percentage area-under-curve (AUC) error in the input function. These findings were also confirmed by numerical analysis. The predicted global scale was in good agreement with results from both image-based Patlak and direct Patlak approach. The fractions of the AUC from the early portion population-based input function were also found to be around 18% of the total AUC of the input function, further limiting the propagation of quantitation error from population-based input function to the final Patlak slope image. The reconstructed Patlak images were also found by the radiologist to provide excellent confidence in lesion detection tasks. Conclusions: We have proposed a simplified whole-body scanning protocol that utilizes both population-based input function and model-based input function. The error fr...

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“…In some studies, the requirement of invasive arterial blood sampling for the input function makes the scan protocol impractical. Even with the use of image-derived input functions (18,19) or population-based input functions (20,21), a minimal scan time of 30 minutes is often required to reveal dynamic information using Patlak analysis. Compared with state-of-art whole-body SUV scans which take around 20 minutes and some in less than 10 minutes, the increased scan time and more complex protocol have shown to be one of the major practical factors that hinder the clinical use of dynamic analysis with whole-body imaging.…”

Section: Introductionmentioning

confidence: 99%

Total-Body Quantitative Parametric Imaging of Early Kinetics of ¹⁸F-FDG

et al. 2020

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Parametric imaging has been shown to provide better quantitation physiologically compared with SUV imaging in PET. With the increased sensitivity from a recently developed total-body PET scanner, whole-bodyscans with higher temporal resolution become possible for dynamic analysis and parametric imaging. In this paper, we focus on deriving the parameter k 1 using compartmental modeling, and on developing a method to acquire whole-body FDG-PET parametric images using only the first 90 seconds of the post-injection scan data with the total-body PET system. Dynamic projections were acquired with a time interval of 1 second for the first 30 seconds and 2 seconds for the following minute. Image-derived input functions were acquired from the reconstructed dynamic sequences in the ascending aorta. The one-tissue compartment model with the total of 4 parameters (k 1 , k 2 , blood fraction, delay time) was used. A maximum-likelihood based estimation method was developed with the 1-tissue compartment model solution. The accuracy of the acquired parameters was compared with the ones estimated using a 2-tissue irreversible model with 1-hour long data.All four parametric images were successfully calculated using data from two volunteers. By comparing the timeactivity-curves acquired from the volume of interests, it was shown that the parameters estimated using our method were able to predict the time-activity curves of the early dynamics of FDG in different organs. The time delay effects for different organs were also clearly visible in the reconstructed time delay image with delay variations as large as 40 seconds. The estimated parameters using both 90 seconds data and 1-hour long data were in good agreement for k 1 and blood fraction, while a large difference of k 2 was found between the 90 seconds and 1-hour data, suggesting k 2 can't be reliably estimated from the 90 second scan.We have shown that with the use of total-body PET and the increased sensitivity, it is possible to estimate parametric images based on the very early dynamics following FDG injection. The estimated k 1 could potentially be used clinically as an indicator for identifying abnormalities.

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Image‐derived and arterial blood sampled input functions for quantitative PET imaging of the angiotensin II subtype 1 receptor in the kidney

Cited by 15 publications

References 23 publications

Minimally invasive quantification of cerebral P2X7R occupancy using dynamic [18F]JNJ-64413739 PET and MRA-driven image derived input function

Minimally invasive quantification of cerebral P2X7R occupancy using dynamic [18F]JNJ-64413739 PET and MRA-driven image derived input function

Simplified protocol for whole‐body Patlak parametric imaging with ¹⁸F‐FDG PET/CT: Feasibility and error analysis

Total-Body Quantitative Parametric Imaging of Early Kinetics of ¹⁸F-FDG

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Image‐derived and arterial blood sampled input functions for quantitative PET imaging of the angiotensin II subtype 1 receptor in the kidney

Cited by 15 publications

References 23 publications

Minimally invasive quantification of cerebral P2X7R occupancy using dynamic [18F]JNJ-64413739 PET and MRA-driven image derived input function

Minimally invasive quantification of cerebral P2X7R occupancy using dynamic [18F]JNJ-64413739 PET and MRA-driven image derived input function

Simplified protocol for whole‐body Patlak parametric imaging with 18F‐FDG PET/CT: Feasibility and error analysis

Total-Body Quantitative Parametric Imaging of Early Kinetics of 18F-FDG

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Product

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Simplified protocol for whole‐body Patlak parametric imaging with ¹⁸F‐FDG PET/CT: Feasibility and error analysis

Total-Body Quantitative Parametric Imaging of Early Kinetics of ¹⁸F-FDG