Accurate image derived input function in [18F]SynVesT-1 mouse studies using isoflurane and ketamine/xylazine anesthesia

BACKGROUND

Kinetic modeling in positron emission tomography (PET) requires measurement of the tracer plasma activity in the absence of a suitable reference region. To avoid invasive blood sampling, the use of an image derived input function has been proposed. However, an accurate delineation of the blood pool region in the PET image is necessary to obtain unbiased blood activity. Here, to perform brain kinetic modeling in [18F]SynVesT-1 dynamic scans, we make use of non-negative matrix factorization (NMF) to unmix the activity signal from the different tissues that can contribute to the heart region activity, and extract only the left ventricle activity in an unbiased way. This method was implemented in dynamic [18F]SynVesT-1 scans of mice anesthetized with either isoflurane or ketamine-xylazine, two anesthestics that we showed to affect differently radiotracer kinetics. The left ventricle activity (NMF-IDIF) and a manually delineated cardiac activity (IDIF) were compared with arterial blood samples (ABS), and for isoflurane anesthetized mice, arteriovenous (AV) shunt blood data were compared as well. Finally, brain regional 2 tissue compartment modeling was performed using IDIF and NMF-IDIF, and the model fit accuracy (weighted symmetrical mean absolute percentage error, wsMAPE) as well as the total volume of distribution (VT) were compared.

RESULTS

In isoflurane anesthetized mice, the difference between ABS and NMF-IDIF activity (+ 12.8 [Formula: see text] 11%, p = 0.0023) was smaller than with IDIF (+ 16.4 [Formula: see text] 9.8%, p = 0.0008). For ketamine-xylazine anesthetized mice the reduction in difference was larger (NMF-IDIF: 16.9 [Formula: see text] 10%, p = 0.0057, IDIF: 56.3 [Formula: see text] 14%, p < 0.0001). Correlation coefficient between isoflurane AV-shunt time activity curves and NMF-IDIF (0.97 [Formula: see text] 0.01) was higher than with IDIF (0.94 [Formula: see text] 0.03). The brain regional 2TCM wsMAPE was improved using NMF-IDIF compared with IDIF, in isoflurane (NMF-IDIF: 1.24 [Formula: see text] 0.24%, IDIF: 1.56 [Formula: see text] 0.30%) and ketamine-xylazine (NMF-IDIF: 1.40 [Formula: see text] 0.24, IDIF: 2.62 [Formula: see text] 0.27) anesthetized mice. Finally, brain VT was significantly (p < 0.0001) higher using NMF-IDIF compared with IDIF, in isoflurane (3.97 [Formula: see text] 0.13% higher) and ketamine-xylazine (32.7 [Formula: see text] 2.4% higher) anesthetized mice.

CONCLUSIONS

Image derived left ventricle blood activity calculated with NMF improves absolute activity quantification, and reduces the error in the kinetic modeling fit. These improvements are more pronounced in ketamine-xylazine than in isoflurane anesthetized mice.

Non-invasive kinetic modelling approaches for quantitative analysis of brain PET studies

Pharmacokinetic modelling with arterial sampling is the gold standard for analysing dynamic PET data of the brain. However, the invasive character of arterial sampling prevents its widespread clinical application. Several methods have been developed to avoid arterial sampling, in particular reference region methods. Unfortunately, for some tracers or diseases, no suitable reference region can be defined. For these cases, other potentially non-invasive approaches have been proposed: (1) a population based input function (PBIF), (2) an image derived input function (IDIF), or (3) simultaneous estimation of the input function (SIME). This systematic review aims to assess the correspondence of these non-invasive methods with the gold standard. Studies comparing non-invasive pharmacokinetic modelling methods with the current gold standard methods using an input function derived from arterial blood samples were retrieved from PubMed/MEDLINE (until December 2021). Correlation measurements were extracted from the studies. The search yielded 30 studies that correlated outcome parameters (V_T, DVR, or BP_ND for reversible tracers; K_i or CMR_glu for irreversible tracers) from a potentially non-invasive method with those obtained from modelling using an arterial input function. Some studies provided similar results for PBIF, IDIF, and SIME-based methods as for modelling with an arterial input function (R² = 0.59-1.00, R² = 0.71-1.00, R² = 0.56-0.96, respectively), if the non-invasive input curve was calibrated with arterial blood samples. Even when the non-invasive input curve was calibrated with venous blood samples or when no calibration was applied, moderate to good correlations were reported, especially for the IDIF and SIME (R² = 0.71-1.00 and R² = 0.36-0.96, respectively). Overall, this systematic review illustrates that non-invasive methods to generate an input function are still in their infancy. Yet, IDIF and SIME performed well, not only with arterial blood calibration, but also with venous or no blood calibration, especially for some tracers without plasma metabolites, which would potentially make these methods better suited for clinical application. However, these methods should still be properly validated for each individual tracer and application before implementation.

Validation of kinetic modeling of [15O]H2O PET using an image derived input function on hybrid PET/MRI

In present study we aimed to validate the use of image-derived input functions (IDIF) in the kinetic modeling of cerebral blood flow (CBF) measured by [¹⁵O]H₂O PET by comparing with the accepted reference standard arterial input function (AIF). Additional comparisons were made to mean cohort AIF and CBF values acquired by methodologically independent phase-contrast mapping (PCM) MRI. Using hybrid PET/MRI an IDIF was generated by measuring the radiotracer concentration in the internal carotid arteries and correcting for partial volume effects using the intravascular volume measured from MRI-angiograms. Seven patients with carotid steno-occlusive disease and twelve healthy controls were examined at rest, after administration of acetazolamide, and, in the control group, during hyperventilation. Agreement between the techniques was examined by linear regression and Bland-Altman analysis. Global CBF values modeled using IDIF correlated with values from AIF across perfusion states in both patients (p<10^-6, R²=0.82, 95% limits of agreement (LoA)=[-11.3-9.9] ml/100 g/min) and controls (p<10^-6, R²=0.87, 95% LoA=[-17.1-13.7] ml/100 g/min). The reproducibility of gCBF using IDIF was identical to AIF (15.8%). Values from IDIF and AIF had equally good correlation to measurements by PCM MRI, R²=0.86 and R²=0.84, (p<10^-6), respectively. Mean cohort AIF performed substantially worse than individual IDIFs (p<10^-6, R²=0.63, LoA=[-12.8-25.3] ml/100 g/min). In the patient group, use of IDIF provided similar reactivity maps compared to AIF. In conclusion, global CBF values modeled using IDIF correlated with values modeled by AIF and similar perfusion deficits could be established in a patient group.

Image-derived input function estimation on a TOF-enabled PET/MR for cerebral blood flow mapping

15O-H2O PET imaging is an accurate method to measure cerebral blood flow (CBF) but it requires an arterial input function (AIF). Historically, image-derived AIF estimation suffers from low temporal resolution, spill-in, and spill-over problems. Here, we optimized tracer dose on a time-of-flight PET/MR according to the acquisition-specific noise-equivalent count rate curve. An optimized dose of 850 MBq of 15O-H2O was determined, which allowed sufficient counts to reconstruct a short time-frame PET angiogram (PETA) during the arterial phase. This PETA enabled the measurement of the extent of spill-over, while an MR angiogram was used to measure the true arterial volume for AIF estimation. A segment of the high cervical arteries outside the brain was chosen, where the measured spill-in effects were minimal. CBF studies were performed twice with separate [15O]-H2O injections in 10 healthy subjects, yielding values of 88 ± 16, 44 ± 9, and 58 ± 11 mL/min/100 g for gray matter, white matter, and whole brain, with intra-subject CBF differences of 5.0 ± 4.0%, 4.1 ± 3.3%, and 4.5 ± 3.7%, respectively. A third CBF measurement after the administration of 1 g of acetazolamide showed 35 ± 23%, 29 ± 20%, and 33 ± 22% increase in gray matter, white matter, and whole brain, respectively. Based on these findings, the proposed noninvasive AIF method provides robust CBF measurement with 15O-H2O PET.