Image Derived Input Functions: Effects of Motion on Tracer Kinetic Analyses

Purpose: To quantify the effects of motion affected image-derived input functions (IDIF) on the outcome of tracer kinetic analyses. Procedures: Two simulation studies, one based on high and the other on low cortical uptake, were performed. Different degrees of rotational and axial translational motion were added to the final frames of simulated dynamic positron emission tomography scans. Extracted IDIFs from motion affected simulated scans were compared to original IDIFs and to outcome of tracer kinetic analysis (volume of distribution, V T). Results: Differences in IDIF values of up to 239 % were found for the last frames. Patient motion of more than 6 ° or 5 mm resulted in at least 10 % higher or lower VT values for the high cortical tracer. Conclusion: The degrees of motion studied are commonly observed in clinical studies and hamper the extraction of accurate IDIFs. Therefore, it is essential to ensure that patient motion is minimal and corrected for

Influence of water on the sulfidation of Co/NaY and Co/CaY prepared by impregnation, A. Mössbauer Emission spectroscopy and EXAFS Study

The influence of physisorbed water on the sulfidation of impregnation type Co/NaY(imp) and Co/CaY(imp) catalysts which contain about 4 wt % Co and are prepared via pore volume impregnation is studied by Mossbauer emission spectroscopy and extended X-ray absorption fine structure. It is found that in the absence as well as in the presence of water, sulfidation of the impregnated samples finally results in the formation of Co9S8-like species at the outer zeolite surface. In the presence of water, this process proceeds via the intermediate formation of a well-ordered ''CoS1+x'' phase (NiAs structure). Water removal prior to sulfidation retards the formation of Co9S8-like species. This retardation in Co9S8 formation is found to be more effective for CaY-zeolite than for NaY- zeolite, which means that a larger part of the formed ''Co- sulfide'' is preserved in highly dispersed ''Co-sulfide'' particles. These findings are clearly different from those obtained earlier with ion exchanged CoNaY(ion ex) samples, for which drying prior to sulfidation resulted in very small ''Co- sulfide'' species with no resemblance to ''Co9S8'

Quantitative implications of the updated EARL 2019 PET-CT performance standards

Purpose Recently, updated EARL specifications (EARL2) have been developed and announced. This study aims at investigating the impact of the EARL2 specifications on the quantitative reads of clinical PET-CT studies and testing a method to enable the use of the EARL2 standards whilst still generating quantitative reads compliant with current EARL standards (EARL1). Methods Thirteen non-small cell lung cancer (NSCLC) and seventeen lymphoma PET-CT studies were used to derive four image datasets-the first dataset complying with EARL1 specifications and the second reconstructed using parameters as described in EARL2. For the third (EARL2F6) and fourth (EARL2F7) dataset in EARL2, respectively, 6 mm and 7 mm Gaussian post-filtering was applied. We compared the results of quantitative metrics (MATV, SUVmax, SUVpeak, SUVmean, TLG, and tumor-to-liver and tumor-to-blood pool ratios) obtained with these 4 datasets in 55 suspected malignant lesions using three commonly used segmentation/volume of interest (VOI) methods (MAX41, A50P, SUV4). Results We found that with EARL2 MAX41 VOI method, MATV decreases by 22%, TLG remains unchanged and SUV values increase by 23-30% depending on the specific metric used. The EARL2F7 dataset produced quantitative metrics best aligning with EARL1, with no significant differences between most of the datasets (p>0.05). Different VOI methods performed similarly with regard to SUV metrics but differences in MATV as well as TLG were observed. No significant difference between NSCLC and lymphoma cancer types was observed. Conclusions Application of EARL2 standards can result in higher SUVs, reduced MATV and slightly changed TLG values relative to EARL1. Applying a Gaussian filter to PET images reconstructed using EARL2 parameters successfully yielded EARL1 compliant data

Quantitative implications of the updated EARL 2019 PET-CT performance standards

Purpose Recently, updated EARL specifications (EARL2) have been developed and announced. This study aims at investigating the impact of the EARL2 specifications on the quantitative reads of clinical PET-CT studies and testing a method to enable the use of the EARL2 standards whilst still generating quantitative reads compliant with current EARL standards (EARL1). Methods Thirteen non-small cell lung cancer (NSCLC) and seventeen lymphoma PET-CT studies were used to derive four image datasets-the first dataset complying with EARL1 specifications and the second reconstructed using parameters as described in EARL2. For the third (EARL2F6) and fourth (EARL2F7) dataset in EARL2, respectively, 6 mm and 7 mm Gaussian post-filtering was applied. We compared the results of quantitative metrics (MATV, SUVmax, SUVpeak, SUVmean, TLG, and tumor-to-liver and tumor-to-blood pool ratios) obtained with these 4 datasets in 55 suspected malignant lesions using three commonly used segmentation/volume of interest (VOI) methods (MAX41, A50P, SUV4). Results We found that with EARL2 MAX41 VOI method, MATV decreases by 22%, TLG remains unchanged and SUV values increase by 23-30% depending on the specific metric used. The EARL2F7 dataset produced quantitative metrics best aligning with EARL1, with no significant differences between most of the datasets (p>0.05). Different VOI methods performed similarly with regard to SUV metrics but differences in MATV as well as TLG were observed. No significant difference between NSCLC and lymphoma cancer types was observed. Conclusions Application of EARL2 standards can result in higher SUVs, reduced MATV and slightly changed TLG values relative to EARL1. Applying a Gaussian filter to PET images reconstructed using EARL2 parameters successfully yielded EARL1 compliant data

Should vascular wall F-18-FDG uptake be adjusted for the extent of atherosclerotic burden?

Vascular wall 18F-FDG uptake is often used as a surrogate marker of atherosclerotic plaque inflammation. A potential caveat is that vascular wall 18F-FDG uptake is higher simply because more atherosclerosis is present. To determine if the degree of inflammation is high or low relative to the extent of atherosclerosis, vascular wall 18F-FDG uptake may require statistical adjustment for a non-inflammatory marker reflecting the extent of atherosclerosis, e.g. calcification. Adjustments is probably needed if (1) vascular wall 18F-FDG uptake correlates sufficiently strongly with arterial calcification and (2) adjustment for extent of calcification affects determinants of vascular 18F-FDG uptake. This study addresses these questions. 18F-FDG PET/low-dose-CT scans of 99 patients were used. Cardiovascular risk factors were assessed and PET/CT scans were analysed for standardized 18F-FDG uptake values and calcification. ANOVA was used to establish the association between vascular 18F-FDG uptake and calcification. Multiple linear regression (with and without calcification as independent variable) was used to show whether determinants of vascular 18F-FDG uptake were affected by the degree of calcification. 18F-FDG uptake was related to increased calcification in the aortic arch, descending and abdominal aorta. However, 18F-FDG uptake showed considerable overlap between categories of calcification. Age and body mass index were main determinants of vascular 18F-FDG uptake. In multiple regression analyses, most standardized beta coefficients of these determinants were not affected by adjustment for the degree of calcification. Although vascular 18F-FDG uptake is related to total atherosclerotic burden, as reflected by vascular calcification, the association is weak and unlikely to affect the identification of determinants of atherosclerotic inflammation implicating no need for adjustment in future studies

Mitigation of noise-induced bias of PET radiomic features

INTRODUCTION: One major challenge in PET radiomics is its sensitivity to noise. Low signal-to-noise ratio (SNR) affects not only the precision but also the accuracy of quantitative metrics extracted from the images resulting in noise-induced bias. This phantom study aims to identify the radiomic features that are robust to noise in terms of precision and accuracy and to explore some methods that might help to correct noise-induced bias. METHODS: A phantom containing three 18F-FDG filled 3D printed inserts, reflecting heterogeneous tracer uptake and realistic tumor shapes, was used in the study. The three different phantom inserts were filled and scanned with three different tumor-to-background ratios, simulating a total of nine different tumors. From the 40-minute list-mode data, ten frames each for 5 s, 10 s, 30 s, and 120 s frame duration were reconstructed to generate images with different noise levels. Under these noise conditions, the precision and accuracy of the radiomic features were analyzed using intraclass correlation coefficient (ICC) and similarity distance metric (SDM) respectively. Based on the ICC and SDM values, the radiomic features were categorized into four groups: poor, moderate, good, and excellent precision and accuracy. A "difference image" created by subtracting two statistically equivalent replicate images was used to develop a model to correct the noise-induced bias. Several regression methods (e.g., linear, exponential, sigmoid, and power-law) were tested. The best fitting model was chosen based on Akaike information criteria. RESULTS: Several radiomic features derived from low SNR images have high repeatability, with 68% of radiomic features having ICC ≥ 0.9 for images with a frame duration of 5 s. However, most features show a systematic bias that correlates with the increase in noise level. Out of 143 features with noise-induced bias, the SDM values were improved based on a regression model (53 features to excellent and 67 to good) indicating that the noise-induced bias of these features can be, at least partially, corrected. CONCLUSION: To have a predictive value, radiomic features should reflect tumor characteristics and be minimally affected by noise. The present study has shown that it is possible to correct for noise-induced bias, at least in a subset of the features, using a regression model based on the local image noise estimates

Feasibility of pharmacokinetic parametric PET images in scaled subprofile modelling using principal component analysis

Scaled subprofile model using principal component analysis (SSM/PCA) is a multivariate analysis technique used, mainly in [18F]-2-fluoro-2-deoxy-D-glucose (FDG) PET studies, for the generation of disease-specific metabolic patterns (DP) that may aid with the classification of subjects with neurological disorders, like Alzheimer's disease (AD). The aim of this study was to explore the feasibility of using quantitative parametric images for this type of analysis, with dynamic [11C]-labelled Pittsburgh Compound B (PIB) PET data as an example. Therefore, 15 AD patients and 15 healthy control subjects were included in an SSM/PCA analysis to generate four AD-DPs using relative cerebral blood flow (R1), binding potential (BPND) and SUVR images derived from dynamic PIB and static FDG-PET studies. Furthermore, 49 new subjects with a variety of neurodegenerative cognitive disorders were tested against these DPs. The AD-DP was characterized by a reduction in the frontal, parietal, and temporal lobes voxel values for R1 and SUVR-FDG DPs; and by a general increase of values in cortical areas for BPND and SUVR-PIB DPs. In conclusion, the results suggest that the combination of parametric images derived from a single dynamic scan might be a good alternative for subject classification instead of using 2 independent PET studies

Quantification of Dynamic 11C-Phenytoin PET Studies

The overexpression of P-glycoprotein (Pgp) is thought to be an important mechanism of pharmacoresistance in epilepsy. Recently, 11C-phenytoin has been evaluated preclinically as a tracer for Pgp. The aim of the present study was to assess the optimal plasma kinetic model for quantification of 11C-phenytoin studies in humans. Methods: Dynamic 11C-phenytoin PET scans of 6 healthy volunteers with arterial sampling were acquired twice on the same day and analyzed using single- and 2-tissue-compartment models with and without a blood volume parameter. Global and regional test– retest (TRT) variability was determined for both plasma to tissue rate constant (K1) and volume of distribution (VT). Results: According to the Akaike information criterion, the reversible single-tissue-compartment model with blood volume parameter was the preferred plasma input model. Mean TRT variability ranged from 1.5% to 16.9% for K1 and from 0.5% to 5.8% for VT. Larger volumes of interest showed better repeatabilities than smaller regions. A 45-min scan provided essentially the same K1 and VT values as a 60-min scan. Conclusion: A reversible single-tissue-compartment model with blood volume seems to be a good candidate model for quantification of dynamic 11C-phenytoin studies. Scan duration may be reduced to 45 min without notable loss of accuracy and precision of both K1 and VT, although this still needs to be confirmed under pathologic conditions