A dual-time-window protocol to reduce acquisition time of dynamic tau PET imaging using [F-18]MK-6240

Background [F-18]MK-6240 is a PET tracer with sub-nanomolar affinity for neurofibrillary tangles. Therefore, tau quantification is possible with [F-18]MK-6240 PET/CT scans, and it can be used for assessment of Alzheimer's disease. However, long acquisition scans are required to provide fully quantitative estimates of pharmacokinetic parameters. Therefore, on the present study, dual-time-window (DTW) acquisitions was simulated to reduce PET/CT acquisition time, while taking into consideration perfusion changes and possible scanning protocol non-compliance. To that end, time activity curves (TACs) representing a 120-min acquisition (TAC(120)) were simulated using a two-tissue compartment model with metabolite corrected arterial input function from 90-min dynamic [F-18]MK-6240 PET scans of three healthy control subjects and five subjects with mild cognitive impairment or Alzheimer's disease. Therefore, TACs corresponding to different levels of specific binding were generated and then various perfusion changes were simulated. Next, DTW acquisitions were simulated consisting of an acquisition starting at tracer injection, a break and a second acquisition starting at 90 min post-injection. Finally, non-compliance with the PET/CT scanning protocol were simulated to assess its impact on quantification. All TACs were quantified using reference Logan's distribution volume ratio (DVR) and standardized uptake value ratio (SUVR90) using the cerebellar cortex as reference region. Results It was found that DVR from a DTW protocol with a 60-min break between two 30-min dynamic scans closely approximates the DVR from the uninterrupted TAC(120), with a regional bias smaller than 2.5%. Moreover, SUVR90 estimates were more susceptible (regional bias</p

Repeatability of [¹⁸F]FDG PET/CT total metabolic active tumour volume and total tumour burden in NSCLC patients

Background: Total metabolic active tumour volume (TMATV) and total tumour burden (TTB) are increasingly studied as prognostic and predictive factors in non-small cell lung cancer (NSCLC) patients. In this study, we investigated the repeatability of TMATV and TTB as function of uptake interval, positron emission tomography/computed tomography (PET/CT) image reconstruction settings, and lesion delineation method. We used six lesion delineation methods, four direct PET image-derived delineations and two based on a majority vote approach, i.e. intersection between two or more delineations (MV2) and between three or more delineations (MV3). To evaluate the accuracy of those methods, they were compared with a reference delineation obtained from the consensus of the segmentations performed by three experienced observers. Ten NSCLC patients underwent two baseline whole-body [ 18 F]2-Fluoro-2-deoxy-2-D-glucose ([ 18 F]FDG) PET/CT studies on separate days, within 3 days. Two scans were obtained on each day at 60 and 90 min post-injection to assess the influence of tracer uptake interval. PET/CT images were reconstructed following the European Association of Nuclear Medicine Research Ltd. (EARL) compliant settings and with point-spread-function (PSF) modelling. Repeatability between the measurements of each day was determined and the influence of uptake interval, reconstruction settings, and lesion delineation method was assessed using the generalized estimating equations model. Results: Based on the Jaccard index with the reference delineation, the MV2 lesion delineation method was the most successful method for automated lesion segmentation. The best overall repeatability (lowest repeatability coefficient, RC) was found for TTB from 90 min of tracer uptake scans reconstructed with EARL compliant settings and delineated with 41% of lesion’s maximum SUV method (RC = 11%). In most cases, TMATV and TTB repeatability were not significantly affected by changes in tracer uptake time or reconstruction settings. However, some lesion delineation methods had significantly different repeatability when applied to the same images. Conclusions: This study suggests that under some circumstances TMATV and TTB repeatability are significantly affected by the lesion delineation method used. Performing the delineation with a majority vote approach improves reliability and does not hamper repeatability, regardless of acquisition and reconstruction settings. It is therefore concluded that by using a majority vote based tumour segmentation approach, TMATV and TTB in NSCLC patients can be measured with high reliability and precision

The effect of lesion filling on brain network analysis in multiple sclerosis using structural magnetic resonance imaging

BACKGROUND: Graph theoretical network analysis with structural magnetic resonance imaging (MRI) of multiple sclerosis (MS) patients can be used to assess subtle changes in brain networks. However, the presence of multiple focal brain lesions might impair the accuracy of automatic tissue segmentation methods, and hamper the performance of graph theoretical network analysis. Applying "lesion filling" by substituting the voxel intensities of a lesion with the voxel intensities of nearby voxels, thus creating an image devoid of lesions, might improve segmentation and graph theoretical network analysis. This study aims to determine if brain networks are different between MS subtypes and healthy controls (HC) and if the assessment of these differences is affected by lesion filling. METHODS: The study included 49 MS patients and 19 HC that underwent a T1w, and T2w-FLAIR MRI scan. Graph theoretical network analysis was performed from grey matter fractions extracted from the original T1w-images and T1w-images after lesion filling. RESULTS: Artefacts in lesion-filled T1w images correlated positively with total lesion volume (r = 0.84, p < 0.001) and had a major impact on grey matter segmentation accuracy. Differences in sensitivity for network alterations were observed between original T1w data and after application of lesion filling: graph theoretical network analysis obtained from lesion-filled T1w images produced more differences in network organization in MS patients. CONCLUSION: Lesion filling might reduce variability across subjects resulting in an increased detection rate of network alterations in MS, but also induces significant artefacts, and therefore should be applied cautiously especially in individuals with higher lesions loads

Effects of Tracer Uptake Time in Non-Small Cell Lung Cancer 18F-FDG PET Radiomics

Positron emission tomography (PET) radiomics applied to oncology allows the measurement of intra-tumoral heterogeneity. This quantification can be affected by image protocols hence there is an increased interest in understanding how radiomic expression on PET images is affected by different imaging conditions. To address that, this study explores how radiomic features are affected by changes in 18F-FDG uptake time, image reconstruction, lesion delineation, and radiomics binning settings. Methods: Ten non-small cell lung cancer (NSCLC) patients underwent 18F-FDG PET scans on two consecutive days. On each day, scans were obtained at 60min and 90min post-injection and reconstructed following EARL version 1 (EARL1) and with point-spread-function resolution modelling (PSF-EARL2). Lesions were delineated using thresholds at SUV=4.0, 40% of SUVmax, and with a contrast-based isocontour. PET image intensity was discretized with both fixed bin width (FBW) and fixed bin number (FBN) before the calculation of the radiomic features. Repeatability of features was measured with intraclass correlation (ICC), and the change in feature value over time was calculated as a function of its repeatability. Features were then classified on use-case scenarios based on their repeatability and susceptibility to tracer uptake time. Results: With PSFEARL2 reconstruction, 40% of SUVmax lesion delineation, and FBW intensity discretization, most features (94%) were repeatable at both uptake times (ICC>0.9), 39% being classified for dual-time-point use-case for being sensitive to changes in uptake time, 39% were classified for cross-sectional studies with unclear dependency on time, 20% classified for cross-sectional use while being robust to tracer uptake time changes, and 6% were discarded for poor repeatability. EARL1 images had one less repeatable feature than PSF-EARL2 (Neighborhood Gray-Level Different Matrix Coarseness), the contrast-based delineation had the poorest repeatability of the delineation methods with 45% features being discarded, and FBN resulted in lower repeatability than FBW (45% and 6% features were discarded, respectively). Conclusion: Repeatability was maximized with PSF-EARL2 reconstruction, lesion delineation at 40% of SUVmax, and FBW intensity discretization. Based on their susceptibility to tracer uptake time, radiomic features were classified into specific NSCLC PET radiomics use-cases