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.

PET radiomics applied to oncology allow the measurement of intratumoral 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 interest, this study explored how radiomic features are affected by changes in 18F-FDG uptake time, image reconstruction, lesion delineation, and radiomic binning settings. Methods: Ten non-small cell lung cancer patients underwent 18F-FDG PET on 2 consecutive days. On each day, scans were obtained at 60 and 90 min after injection and reconstructed following EARL version 1 and with point-spread-function resolution modeling (PSF-EARL2). Lesions were delineated with an SUV threshold of 4.0, with 40% of SUVmax, and with a contrast-based isocontour. PET image intensity was discretized with both a fixed bin width (FBW) and a fixed bin number before the calculation of the radiomic features. Repeatability of features was measured with the intraclass correlation coefficient, and the change in feature value over time was calculated as a function of its repeatability. Features were then classified into use-case scenarios based on their repeatability and susceptibility to tracer uptake time. Results: With PSF-EARL2 reconstruction, 40% of SUVmax lesion delineation, and FBW intensity discretization, most features (94%) were repeatable at both uptake times (intraclass correlation coefficient > 0.9), 35% being classified for dual-time-point use cases as being sensitive to changes in uptake time, 39% were classified for cross-sectional studies with an unclear dependency on time, 20% were classified for cross-sectional use while being robust to uptake time changes, and 6% were discarded for poor repeatability. EARL version 1 images had 1 fewer repeatable feature (neighborhood gray-level different matrix coarseness) than PSF-EARL2; the contrast-based delineation had the poorest repeatability of the delineation methods, with 45% of features being discarded; and fixed bin number resulted in lower repeatability than FBW (45% and 6% of features were discarded, respectively). Conclusion: Repeatability was maximized with PSF-EARL2 reconstruction, lesion delineation at 40% of SUVmax, and FBW intensity discretization. On the basis of their susceptibility to uptake time, radiomic features were classified into specific non-small cell lung cancer PET radiomics use cases.

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

BACKGROUND: [18F]MK-6240 is a PET tracer with sub-nanomolar affinity for neurofibrillary tangles. Therefore, tau quantification is possible with [18F]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 (TAC120) were simulated using a two-tissue compartment model with metabolite corrected arterial input function from 90-min dynamic [18F]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 TAC120, with a regional bias smaller than 2.5%. Moreover, SUVR90 estimates were more susceptible (regional bias ≤ 19%) to changes in perfusion compared to DVR from a DTW TAC (regional bias ≤ 10%). Similarly, SUVR90 was affected by late-time scanning protocol delays reaching an increase of 8% for a 20-min delay, while DVR was not affected (regional bias < 1.5%) by DTW protocol non-compliance. CONCLUSIONS: Therefore, such DTW protocol has the potential to increase patient comfort and throughput without compromising quantitative accuracy and is more reliable against SUVR in terms of perfusion changes and protocol deviations, which could prove beneficial for drug effect assessment and patient follow-up using longitudinal [18F]MK-6240 PET imaging.

Amyloid burden quantification depends on PET and MR image processing methodology.

Quantification of amyloid load with positron emission tomography can be useful to assess Alzheimer's Disease in-vivo. However, quantification can be affected by the image processing methodology applied. This study's goal was to address how amyloid quantification is influenced by different semi-automatic image processing pipelines. Images were analysed in their Native Space and Standard Space; non-rigid spatial transformation methods based on maximum a posteriori approaches and tissue probability maps (TPM) for regularisation were explored. Furthermore, grey matter tissue segmentations were defined before and after spatial normalisation, and also using a population-based template. Five quantification metrics were analysed: two intensity-based, two volumetric-based, and one multi-parametric feature. Intensity-related metrics were not substantially affected by spatial normalisation and did not significantly depend on the grey matter segmentation method, with an impact similar to that expected from test-retest studies (≤10%). Yet, volumetric and multi-parametric features were sensitive to the image processing methodology, with an overall variability up to 45%. Therefore, the analysis should be carried out in Native Space avoiding non-rigid spatial transformations. For analyses in Standard Space, spatial normalisation regularised by TPM is preferred. Volumetric-based measurements should be done in Native Space, while intensity-based metrics are more robust against differences in image processing pipelines.

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 [18F]2-Fluoro-2-deoxy-2-D-glucose ([18F]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.