Image Quantification for TSPO PET with a Novel Image-Derived Input Function Method

Abstract

There is a growing interest in using 18F-DPA-714 PET to study neuroinflammation and microglial activation through imaging the 18-kDa translocator protein (TSPO). Although quantification of 18F-DPA-714 binding can be achieved through kinetic modeling analysis with an arterial input function (AIF) measured with blood sampling procedures, the invasiveness of such procedures has been an obstacle for wide application. To address these challenges, we developed an image-derived input function (IDIF) that noninvasively estimates the arterial input function from the images acquired for 18F-DPA-714 quantification. Methods: The method entails three fully automatic steps to extract the IDIF, including a segmentation of voxels with highest likelihood of being the arterial blood over the carotid artery, a model-based matrix factorization to extract the arterial blood signal, and a scaling optimization procedure to scale the extracted arterial blood signal into the activity concentration unit. Two cohorts of human subjects were used to evaluate the extracted IDIF. In the first cohort of five subjects, arterial blood sampling was performed, and the calculated IDIF was validated against the measured AIF through the comparison of distribution volumes from AIF (VT,AIF) and IDIF (VT,IDIF). In the second cohort, PET studies from twenty-eight healthy controls without arterial blood sampling were used to compare VT,IDIF with VT,REF measured using a reference region-based analysis to evaluate whether it can distinguish high-affinity (HAB) and mixed-affinity (MAB) binders. Results: In the arterial blood-sampling cohort, VT derived from IDIF was found to be an accurate surrogate of the VT from AIF. The bias of VT, IDIF was −5.8 ± 7.8% when compared to VT,AIF, and the linear mixed effect model showed a high correlation between VT,AIF and VT, IDIF (p < 0.001). In the nonblood-sampling cohort, VT, IDIF showed a significance difference between the HAB and MAB healthy controls. VT, IDIF and standard uptake values (SUV) showed superior results in distinguishing HAB from MAB subjects than VT,REF. Conclusions: A novel IDIF method for 18F-DPA-714 PET quantification was developed and evaluated in this study. This IDIF provides a noninvasive alternative measurement of VT to quantify the TSPO binding of 18F-DPA-714 in the human brain through dynamic PET scans.

Keywords: TSPO PET; image-derived input function; kinetic modeling analysis; neuroinflammation.

Figure 1

Demonstration of the IDIF extraction. (a) The coronal maximum intensity projection (MIP) of the summed early frames of the DPA-714 scan of one subject. (b) One axial slice of the neck area at the early frame. (c) The segmented regions (green contour) for carotid artery over the same slice. (d) The MBMF-extracted time-activity curves for the blood and tissue components. Note both curves are in the normalized and dimensionless form. (e) The resultant IDIF (blue circle) after the magnitude is re-scaled by the proposed method. The activities measured with arterial blood sampling are plotted as red stars, showing a satisfactory agreement between AIF and IDIF.

Figure 1

Demonstration of the IDIF extraction. (a) The coronal maximum intensity projection (MIP) of the summed early frames of the DPA-714 scan of one subject. (b) One axial slice of the neck area at the early frame. (c) The segmented regions (green contour) for carotid artery over the same slice. (d) The MBMF-extracted time-activity curves for the blood and tissue components. Note both curves are in the normalized and dimensionless form. (e) The resultant IDIF (blue circle) after the magnitude is re-scaled by the proposed method. The activities measured with arterial blood sampling are plotted as red stars, showing a satisfactory agreement between AIF and IDIF.

Figure 2

(a) The scatter plot for V_T,IDIF plotted against V_T,AIF. The dashed line is the unity line. The red line is the fitted regression trend line. The scatter plot shows a strong correlation between V_T,IDIF plotted against V_T,AIF. The statistical test also showed a strong correlation through the mixed effect model analysis. (b) The Bland–Altman plot for V_T,IDIF and V_T,AIF.

Figure 3

The box plot of the SUV measured for target brain regions for high- and mixed-affinity binders in the nonblood-sampling cohort. * denotes significant differences under two-sample t-test (p < 0.05), while ** denotes p < 0.01. All nine target regions showed significant differences between the HAB and MAB subjects. SUV is 31 ± 3% higher in HAB than in MAB.

Figure 4

The box plot of the V_T,IDIF. V_T,IDIF is 37 ± 3% higher in HAB than in MAB. All nine regions were found with significant difference (*: p < 0.05, **: p < 0.01) between the HAB and MAB subjects. The overall increase pattern in the HAB is similar as the SUV pattern of increase.

Figure 5

The box plot of the V^T,REF using the cerebellum as the reference region. The difference between HAB and MAB is 3 ± 3% and only reaches significance (*: p < 0.05) in just one of nine regions.