Clinical evaluation of automated quantitative MRI reports for assessment of hippocampal sclerosis.

OBJECTIVES: Hippocampal sclerosis (HS) is a common cause of temporal lobe epilepsy. Neuroradiological practice relies on visual assessment, but quantification of HS imaging biomarkers-hippocampal volume loss and T2 elevation-could improve detection. We tested whether quantitative measures, contextualised with normative data, improve rater accuracy and confidence. METHODS: Quantitative reports (QReports) were generated for 43 individuals with epilepsy (mean age ± SD 40.0 ± 14.8 years, 22 men; 15 histologically unilateral HS; 5 bilateral; 23 MR-negative). Normative data was generated from 111 healthy individuals (age 40.0 ± 12.8 years, 52 men). Nine raters with different experience (neuroradiologists, trainees, and image analysts) assessed subjects' imaging with and without QReports. Raters assigned imaging normal, right, left, or bilateral HS. Confidence was rated on a 5-point scale. RESULTS: Correct designation (normal/abnormal) was high and showed further trend-level improvement with QReports, from 87.5 to 92.5% (p = 0.07, effect size d = 0.69). Largest magnitude improvement (84.5 to 93.8%) was for image analysts (d = 0.87). For bilateral HS, QReports significantly improved overall accuracy, from 74.4 to 91.1% (p = 0.042, d = 0.7). Agreement with the correct diagnosis (kappa) tended to increase from 0.74 ('fair') to 0.86 ('excellent') with the report (p = 0.06, d = 0.81). Confidence increased when correctly assessing scans with the QReport (p < 0.001, η2p = 0.945). CONCLUSIONS: QReports of HS imaging biomarkers can improve rater accuracy and confidence, particularly in challenging bilateral cases. Improvements were seen across all raters, with large effect sizes, greatest for image analysts. These findings may have positive implications for clinical radiology services and justify further validation in larger groups. KEY POINTS: • Quantification of imaging biomarkers for hippocampal sclerosis-volume loss and raised T2 signal-could improve clinical radiological detection in challenging cases. • Quantitative reports for individual patients, contextualised with normative reference data, improved diagnostic accuracy and confidence in a group of nine raters, in particular for bilateral HS cases. • We present a pre-use clinical validation of an automated imaging assessment tool to assist clinical radiology reporting of hippocampal sclerosis, which improves detection accuracy.

Noise removal in resting-state and task fMRI: functional connectivity and activation maps.

OBJECTIVE: Blood-oxygenated-level dependent (BOLD)-based functional magnetic resonance imaging (fMRI) is a widely used non-invasive tool for mapping brain function and connectivity. However, the BOLD signal is highly affected by non-neuronal contributions arising from head motion, physiological noise and scanner artefacts. Therefore, it is necessary to recover the signal of interest from the other noise-related fluctuations to obtain reliable functional connectivity (FC) results. Several pre-processing pipelines have been developed, mainly based on nuisance regression and independent component analysis (ICA). The aim of this work was to investigate the impact of seven widely used denoising methods on both resting-state and task fMRI. APPROACH: Task fMRI can provide some ground truth given that the task administered has well established brain activations. The resulting cleaned data were compared using a wide range of measures: motion evaluation and data quality, resting-state networks and task activations, FC. MAIN RESULTS: Improved signal quality and reduced motion artefacts were obtained with all advanced pipelines, compared to the minimally pre-processed data. Larger variability was observed in the case of brain activation and FC estimates, with ICA-based pipelines generally achieving more reliable and accurate results. SIGNIFICANCE: This work provides an evidence-based reference for investigators to choose the most appropriate method for their study and data.

Age-Specific 18F-FDG Image Processing Pipelines and Analysis Are Essential for Individual Mapping of Seizure Foci in Pediatric Patients with Intractable Epilepsy.

18F-FDG PET is an important tool for the presurgical assessment of children with drug-resistant epilepsy. Standard assessment is performed visually and is often subjective and highly user-dependent. Voxelwise statistics can be used to remove user-dependent biases by automatically identifying areas of significant hypo- or hypermetabolism associated with the epileptogenic area. In the clinical setting, this analysis is performed using commercially available software. These software packages suffer from two main limitations when applied to pediatric PET data: pediatric scans are spatially normalized to an adult standard template, and statistical comparisons use an adult control dataset. The aim of this work was to provide a reliable observer-independent pipeline for the analysis of pediatric 18F-FDG PET scans, as part of presurgical planning in epilepsy. Methods: A pseudocontrol dataset (19 subjects 6-9 y old, and 93 subjects 10-20 y old) was used to create two age-specific 18F-FDG PET pediatric templates in standard pediatric space. The 18F-FDG PET scans of 46 epilepsy patients (16 patients 6-9 y old, and 30 patients 10-17 y old) were retrospectively collated and analyzed using voxelwise statistics. This procedure was implemented with the standard pipeline available in the commercial software Scenium and an in-house Statistical Parametric Mapping, version 8 (SPM8), pipeline (including age-specific pediatric templates and reference database). A κ-test was used to assess the level of agreement between the findings of voxelwise analyses and the clinical diagnosis of each patient. The SPM8 pipeline was further validated using postsurgical seizure-free patients. Results: Improved agreement with the clinical diagnosis was reported using SPM8, in terms of focus localization, especially for the younger patient group: κ = 0.489 for Scenium versus 0.826 for SPM. The proposed pipeline also showed a sensitivity of about 70% in both age ranges for the localization of hypometabolic areas on pediatric 18F-FDG PET scans in postsurgical seizure-free patients. Conclusion: We showed that by creating age-specific templates and using pediatric control databases, our pipeline provides an accurate and sensitive semiquantitative method for assessing the 18F-FDG PET scans of patients under 18 y old.

Arterial Spin Labeling Reveals Disrupted Brain Networks and Functional Connectivity in Drug-Resistant Temporal Epilepsy.

Resting-state networks (RSNs) and functional connectivity (FC) have been increasingly exploited for mapping brain activity and identifying abnormalities in pathologies, including epilepsy. The majority of studies currently available are based on blood-oxygenation-level-dependent (BOLD) contrast in combination with either independent component analysis (ICA) or pairwise region of interest (ROI) correlations. Despite its success, this approach has several shortcomings as BOLD is only an indirect and non-quantitative measure of brain activity. Conversely, promising results have recently been achieved by arterial spin labeling (ASL) MRI, primarily developed to quantify brain perfusion. However, the wide application of ASL-based FC has been hampered by its complexity and relatively low robustness to noise, leaving several aspects of this approach still largely unexplored. In this study, we firstly aimed at evaluating the effect of noise reduction on spatio-temporal ASL analyses and quantifying the impact of two ad-hoc processing pipelines (basic and advanced) on connectivity measures. Once the optimal strategy had been defined, we investigated the applicability of ASL for connectivity mapping in patients with drug-resistant temporal epilepsy vs. controls (10 per group), aiming at revealing between-group voxel-wise differences in each RSN and ROI-wise FC changes. We first found ASL was able to identify the main network (DMN) along with all the others generally detected with BOLD but never previously reported from ASL. For all RSNs, ICA-based denoising (advanced pipeline) allowed to increase their similarity with the corresponding BOLD template. ASL-based RSNs were visibly consistent with literature findings; however, group differences could be identified in the structure of some networks. Indeed, statistics revealed areas of significant FC decrease in patients within different RSNs, such as DMN and cerebellum (CER), while significant increases were found in some cases, such as the visual networks. Finally, the ROI-based analyses identified several inter-hemispheric dysfunctional links (controls > patients) mainly between areas belonging to the DMN, right-left thalamus and right-left temporal lobe. Conversely, fewer connections, predominantly intra-hemispheric, showed the opposite pattern (controls < patients). All these elements provide novel insights into the pathological modulations characterizing a "network disease" as epilepsy, shading light on the importance of perfusion-based approaches for identifying the disrupted areas and communications between brain regions.