Comparison of [¹⁸F]FEPPA regional total distribution volume (<i>V</i>_<i>T</i>) between healthy control subjects and AD patients calculated with PBIF75 for HABs (left) and MABs (right).

<p>(<i>cf</i>. figure 1 in ref [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0177785#pone.0177785.ref007" target="_blank">7</a>]). PBIF75 estimation led to the same conclusion as the previously published with ASIF: [¹⁸F]FEPPA <i>V</i>_<i>T</i> in AD patients is on average 13–48% higher than HC (A Factorial ANOVA with genotype and age as covariate showed: p<0.05 in the Temporal, Prefrontal, Parietal and Occipital and p = 0.4 in the hippocampus). Images were partial volume effect corrected prior to TAC extraction. * p< 0.05 in ANOVA within group.</p

Illustration of the ICA algorithm process steps.

<p>(1) the dynamic PET images derived from sequential measurement of the radioactivity are re-arranged into 2D matrix, the first dimension refers to time acquisition fames and the second dimension refers to the spatial distribution. (2) the principal component analysis reduce matrix dimension in order to keep the most significant activity (if columns of a mixture have relatively similar TACs, then, the corresponding columns tends to be estimated as one components). (3) update the de-mixing matrix until found convergence in separation between the two components.</p

Image Derived Input Function for [¹⁸F]-FEPPA: Application to Quantify Translocator Protein (18 kDa) in the Human Brain

<div><p>In [¹⁸F]-FEPPA positron emission topography (PET) imaging, automatic blood sampling system (ABSS) is currently the gold standard to obtain the blood time activity curve (TAC) required to extract the input function (IF). Here, we compare the performance of two image-based methods of IF extraction to the ABSS gold standard method for the quantification of translocator protein (TSPO) in the human brain. The IFs were obtained from a direct delineation of the internal carotid signal (CS) and a new concept of independent component analysis (ICA). PET scans were obtained from 18 healthy volunteers. The estimated total distribution volume (V_T) by CS-IF and ICA-IF were compared to the reference V_T obtained by ABSS-IF in the frontal and temporal cortex, cerebellum, striatum and thalamus regions. The V_T values estimated using ICA-IF were more reliable than CS-IF for all brain regions. Specifically, the slope regression in the frontal cortex with ICA-IF was r² = 0.91 (<i>p</i><0.05), and r² = 0.71 (<i>p</i><0.05) using CS-IF.</p></div

Total distribution volume (<i>V</i>_<i>T</i>) calculated using ASIF (2-TCM) and PBIF75 (Logan plot).

<p>In Temporal, Prefrontal, Hippocampus, Parietal and Occipital regions stratified as HAB and MAB groups. ASIF showed an average reduction of 29% in <i>V</i>_<i>T</i> across ROIs while PBIF showed a reduction of 26%. 5/7 ROIs survived the multiple region Bonferroni adjustment using ASIF and only 2/7 passed the Bonferroni adjustment using PBIF75. Images were partial volume effect corrected prior to TAC extraction. * p< 0.05, ** p<0.01 *** p< 0.001.</p

Group comparison of total distribution volume (V_T) in the frontal cortex for high affinity binders (HABs) and mixed affinity binders (MABs) calculated respectively with ABSS-IF, CS-IF and ICA-IF.

<p>Group comparison of total distribution volume (V_T) in the frontal cortex for high affinity binders (HABs) and mixed affinity binders (MABs) calculated respectively with ABSS-IF, CS-IF and ICA-IF.</p

Bland Altman plot of total distribution volume (V_T) in the frontal cortex comparing (A) ABSS-IF versus CS-IF, and (B) ABSS-IF versus ICA-IF.

<p>All candidates were within the 95% of limits agreed for and , with the exception of one subject for .</p

A typical double logarithmic scale of the input function estimated by CS and ICA plotted against the ABSS-IF.

<p>Two arterial blood samples were used to correct for a small inversion at 1.5 minutes (green point) and to calibrate curves at 15 minutes post injection (blue point).</p

Internal carotid segmentation performed on OSEM-PSF images using automatic thresholding for one subject.

<p>The red lines illustrate the lowest 36 planes containing the internal carotid artery. The black line surrounding the carotid artery represents the automatic binary mask.</p

Illustration of the normalized histogram of the first source, ADL, GGD, and Gaussian distributions respectively.

<p>The plot describes the data fit by three different distributions. The Gaussian distribution does not show a good model to represent data. The GGD fit better the sharper features of the histogram. However, it fails to fit well the asymmetry of data. The ADL is more appropriate to model the sharper feature of the histogram and, moreover, follows the asymmetric distribution of data through its skew parameter.</p

Population based input function.

<p>A) Average (n = 24) time evolution of unmetabolized [¹⁸F]FEPPA in plasma from HC (n = 8), PD (n = 8) and AD (n = 8) subjects. The inner plot shows details of the first 100 seconds post injection. B) The area under the curve of the input functions created from arterial blood sampling did not differ between 21 HC, 18 AD and 16 PD.</p