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Image-quality assessment is a fundamental step before clinical evaluation of magnetic resonance images. The aim of this study was to introduce a visual scoring system that provides a quality control standard for arterial spin labeling (ASL) and that can be applied to cerebral blood flow (CBF) maps, as well as to ancillary ASL images. The proposed image quality control (QC) system had two components: (1) contrast-based QC (cQC), describing the visual contrast between anatomical structures; and (2) artifact-based QC (aQC), evaluating image quality of the CBF map for the presence of common types of artifacts. Three raters evaluated cQC and aQC for 158 quantitative signal targeting with alternating radiofrequency labelling of arterial regions (QUASAR) ASL scans (CBF, T1 relaxation rate, arterial blood volume, and arterial transient time). Spearman correlation coefficient (r), intraclass correlation coefficients (ICC), and receiver operating characteristic analysis were used. Intra/inter-rater agreement ranged from moderate to excellent; inter-rater ICC was 0.72 for cQC, 0.60 for aQC, and 0.74 for the combined QC (cQC + aQC). Intra-rater ICC was 0.90 for cQC; 0.80 for aQC, and 0.90 for the combined QC. Strong correlations were found between aQC and CBF maps quality (r = 0.75), and between aQC and cQC (r = 0.70). A QC score of 18 was optimal to discriminate between high and low quality clinical scans. The proposed QC system provided high reproducibility and a reliable threshold for discarding low quality scans. Future research should compare this visual QC system with an automatic QC system.

PURPOSE: To compare registration strategies to align arterial spin labeling (ASL) with 3D T1-weighted (T1w) images, with the goal of reducing the between-subject variability of cerebral blood flow (CBF) images. MATERIALS AND METHODS: Multi-center 3T ASL data were collected at eight sites with four different sequences in the multi-center GENetic Frontotemporal dementia Initiative (GENFI) study. In a total of 48 healthy controls, we compared the following image registration options: (I) which images to use for registration (perfusion-weighted images [PWI] to the segmented gray matter (GM) probability map (pGM) (CBF-pGM) or M0 to T1w (M0-T1w); (II) which transformation to use (rigid-body or non-rigid); and (III) whether to mask or not (no masking, M0-based FMRIB software library Brain Extraction Tool [BET] masking). In addition to visual comparison, we quantified image similarity using the Pearson correlation coefficient (CC), and used the Mann-Whitney U rank sum test. RESULTS: CBF-pGM outperformed M0-T1w (CC improvement 47.2% ± 22.0%; P < 0.001), and the non-rigid transformation outperformed rigid-body (20.6% ± 5.3%; P < 0.001). Masking only improved the M0-T1w rigid-body registration (14.5% ± 15.5%; P = 0.007). CONCLUSION: The choice of image registration strategy impacts ASL group analyses. The non-rigid transformation is promising but requires validation. CBF-pGM rigid-body registration without masking can be used as a default strategy. In patients with expansive perfusion deficits, M0-T1w may outperform CBF-pGM in sequences with high effective spatial resolution. BET-masking only improves M0-T1w registration when the M0 image has sufficient contrast. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:131-140.

BackgroundMachine learning assisted MRI radiomics, which combines MRI techniques with machine learning methodology, is rapidly gaining attention as a promising method for staging of brain gliomas. This study assesses the diagnostic value of such framework applied to dynamic susceptibility contrast (DSC)-MRI in classifying treatment-naïve gliomas from a multi-center patient pool into WHO grades II-IV and across their isocitrate dehydrogenase (IDH) mutation status.Methods333 patients from 6 tertiary centres, diagnosed histologically and molecularly with primary gliomas (IDH-mutant=151 or IDH-wildtype=182) were retrospectively identified. Raw DSC-MRI data was post-processed for normalised leakage-corrected relative cerebral blood volume (rCBV) maps. Shape, intensity distribution (histogram) and rotational invariant Haralick texture features over the tumour mask were extracted. Differences in extracted features between IDH-wildtype and IDH-mutant gliomas and across three glioma grades were tested using the Wilcoxon two-sample test. A random forest algorithm was employed (2-fold cross-validation, 250 repeats) to predict grades or mutation status using the extracted features.ResultsFeatures from all types (shape, distribution, texture) showed significant differences across mutation status. WHO grade II-III differentiation was mostly driven by shape features while texture and intensity feature were more relevant for the III-IV separation. Increased number of features became significant when differentiating grades further apart from one another. Gliomas were correctly stratified by IDH mutation status in 71% of the cases and by grade in 53% of the cases. In addition, 87% of the gliomas grades predicted with an error distance up to 1.ConclusionDespite large heterogeneity in the multi-center dataset, machine learning assisted DSC-MRI radiomics hold potential to address the inherent variability and presents a promising approach for non-invasive glioma molecular subtyping and grading.Key points-On highly heterogenous, multi-centre data, machine learning on DSC-MRI features can correctly predict glioma IDH subtyping in 71% of cases and glioma grade II-IV in 53% of the cases (87% -Shape features distinguish best grade II from grade III gliomas.-Texture and distribution features distinguish best grade III from grade IV tumours.Importance of studyThis work illustrates the diagnostic value of combining machine learning and dynamic susceptibility contrast-enhanced MRI (DSC-MRI) radiomics in classifying gliomas into WHO grades II-IV as well as across their isocitrate dehydrogenase (IDH) mutation status. Despite the data heterogeneity inherent to the multi-centre design of the studied cohort (333 subjects, 6 centres) that greatly increases the theoretical challenges of machine learning frameworks, good classification performance (accuracy of 53% across grades (87% With its strong generalisability property, its ability to further incorporate participating centres and its possible use to identify borderline cases, the proposed machine learning framework has the potential to contribute to the clinical translation of machine-learning assisted diagnostic tools in neuro-oncology.

Background and purpose: Biomarkers support the aetiological diagnosis of neurocognitive disorders in vivo. Incomplete evidence is available to drive clinical decisions; available diagnostic algorithms are generic and not very helpful in clinical practice. The aim was to develop a biomarker-based diagnostic algorithm for mild cognitive impairment patients, leveraging on knowledge from recognized national experts. Methods: With a Delphi procedure, experienced clinicians making variable use of biomarkers in clinical practice and representing five Italian scientific societies (neurology – Società Italiana di Neurologia per le Demenze; neuroradiology – Associazione Italiana di Neuroradiologia; biochemistry – Società Italiana di Biochimica Clinica; psychogeriatrics – Associazione Italiana di Psicogeriatria; nuclear medicine – Associazione Italiana di Medicina Nucleare) defined the theoretical framework, relevant literature, the diagnostic issues to be addressed and the diagnostic algorithm. An N–1 majority defined consensus achievement. Results: The panellists chose the 2011 National Institute on Aging and Alzheimer's Association diagnostic criteria as the reference theoretical framework and defined the algorithm in seven Delphi rounds. The algorithm includes baseline clinical and cognitive assessment, blood examination, and magnetic resonance imaging with exclusionary and inclusionary roles; dopamine transporter single-photon emission computed tomography (if no/unclear parkinsonism) or metaiodobenzylguanidine cardiac scintigraphy for suspected dementia with Lewy bodies with clear parkinsonism (round VII, votes(yes-no-abstained): 3-1-1); 18F-fluorodeoxyglucose positron emission tomography for suspected frontotemporal lobar degeneration and low diagnostic confidence of Alzheimer’s disease (round VII, 4-0-1); cerebrospinal fluid for suspected Alzheimer’s disease (round IV, 4-1-0); and amyloid positron emission tomography if cerebrospinal fluid was not possible/accepted (round V, 4-1-0) or inconclusive (round VI, 5-0-0). Conclusions: These consensus recommendations can guide clinicians in the biomarker-based aetiological diagnosis of mild cognitive impairment, whilst guidelines cannot be defined with evidence-to-decision procedures due to incomplete evidence.

Background: Although temporal lobe pathology may explain some of the symptoms of multiple sclerosis (MS), its role in the pathogenesis of seizures has not been clarified yet. Objectives: To investigate the role of temporal lobe damage in MS patients suffering from epilepsy, by the application of advanced multimodal 3T magnetic resonance imaging (MRI) analysis. Methods: A total of 23 relapsing remitting MS patients who had epileptic seizures (RRMS/E) and 23 disease duration matched RRMS patients without any history of seizures were enrolled. Each patient underwent advanced 3T MRI protocol specifically conceived to evaluate grey matter (GM) damage. This includes grey matter lesions (GMLs) identification, evaluation of regional cortical thickness and indices derived from the Neurite Orientation Dispersion and Density Imaging model. Results: Regional analysis revealed that in RRMS/E, the regions most affected by GMLs were the hippocampus (14.2%), the lateral temporal lobe (13.5%), the cingulate (10.0%) and the insula (8.4%). Cortical thinning and alteration of diffusion metrics were observed in several regions of temporal lobe, in insular cortex and in cingulate gyrus of RRMS/E compared to RRMS ( p< 0.05 for all comparisons). Conclusions: Compared to RRMS, RRMS/E showed more severe damage of temporal lobe, which exceeds what would be expected on the basis of the global GM damage observed.

Objective To investigate blood oxygenation level-dependent (BOLD) activation during somatosensory electrical stimulation of the median nerve in acute stroke patients and to determine its correlation with ischemic damage and clinical recovery over time. Methods Fourteen acute stroke patients underwent functional magnetic resonance imaging (fMRI) during contralesional median-nerve electrical stimulation 12–48 h after stroke. Findings were then validated by diffusion tensor imaging (DTI) and motor evoked potential by transcranial magnetic stimulation (TMS). Results Poor clinical recovery at three months was noted in four patients with no activation in the early days after stroke, whereas good clinical recovery was observed in eight patients with a normal activation pattern in the primary sensory motor area in the acute phase. In two patients BOLD activation correlated weakly with clinical recovery. Findings from TMS and DTI partially correlated with clinical recovery and functional scores. Conclusions Clinically relevant insights into the “functional reserve” of stroke patients gained with peripheral nerve stimulation during fMRI may carry prognostic value already in the acute period of a cerebrovascular accident. Significance BOLD activation maps could provide insights into the functional organization of the residual systems and could contribute to medical decision making in neurological and rehabilitative treatment.

PURPOSE: To assess the applicability of arterial spin labeling (ASL) in comparison to blood-oxygenation-level-dependent (BOLD) contrast fMRI in detecting brain activations elicited by active and passive hand movements. MATERIALS AND METHODS: A block design for ASL and BOLD fMRI was applied in 8 healthy subjects using active and passive hand tasks. Data analyses were performed at individual and group level, comparing both the different movements and the performance of the two techniques. RESULTS: Group analyses showed involvement of the same areas during both tasks, as the contralateral sensorimotor cortex, supplementary motor area, cerebellum, inferior parietal lobes, thalamus. ASL detected smaller activation volumes than BOLD, but the areas had a high degree of colocalization. Few significant differences (P < 0.05) were found when the two tasks were compared for the number of activated voxels, coordinates of center of mass, and CBF estimates. Considering together all the areas, the mean %BOLD change was 0.79 ± 0.27 and 0.73 ± 0.24 for the active and passive movements respectively, while the mean %CBF changes were 34.1 ± 8.9 and 27.1 ± 14.8. CONCLUSION: Our findings confirm passive and active tasks are strongly coupled, supporting the importance of passive tasks as a diagnostic tool in the clinical setting. ASL fMRI proved suitable for functional mapping and quantifying CBF changes, making it a promising technique for patient cohort applications.

OBJECTIVE: Among other neuroimaging techniques, functional magnetic resonance imaging (fMRI) can be useful for studying the development of motor fatigue. The aim of this study was to identify differences in cortical neuronal activation in nine subjects on three motor tasks: right-hand movement with minimum, maximum, and post-fatigue maximum finger flexion. MATERIALS AND METHODS: fMRI activation maps for each subject and during each condition were obtained by estimating the optimal model of the hemodynamic response function (HRF) out of four standard HRF models and an individual-based HRF model (ibHRF). RESULTS: ibHRF was selected as the optimal model in six out of nine subjects for minimum movement, in five out of nine for maximum movement, and in eight out of nine for post-fatigue maximum movement. As compared to maximum movement, a large reduction in the total number of active voxels (primary sensorimotor area, supplementary motor area and cerebellum) was observed in post-fatigue maximum movement. CONCLUSION: This is the first approach to the evaluation of long-lasting contraction effort in healthy subjects by means of the fMRI paradigm with the use of an individual-based hemodynamic response. The results may be relevant for defining a baseline in future studies on central fatigue in patients with neuropathological disorders.

When localization of the epileptic focus is uncertain, the epileptic activity generator may be more accurately identified with non-invasive imaging techniques which could also serve to guide stereo-electroencephalography (sEEG) electrode implantation. The aim of this study was to assess the diagnostic value of perfusion magnetic resonance imaging with arterial spin labeling (ASL) in the identification of the epileptogenic zone, as compared to the more invasive positron-emission tomography (PET) and other established investigation methods for source imaging of electroencephalography (EEG) data. In 6 patients with drug-resistant focal epilepsy, standard video-EEG was performed to identify clinical seizure semeiology, and high-density EEG, ASL and FDG-PET to non-invasively localize the epileptic focus. A standardized source imaging procedure, low-resolution brain electromagnetic tomography constrained to the individual matter, was applied to the averaged spikes of high-density EEG. Quantification of current density, cerebral blood flow, and standardized uptake value were compared over the same anatomical areas. In most of the patients, source in the interictal phase was associated with an area of hypoperfusion and hypometabolism. Conversely, in the patients presenting with early post-ictal discharges, the brain area identified by electrical source imaging (ESI) as the generating zone appeared to be hyperperfused. In 2 patients in whom the focus remained uncertain, the postoperative follow-up showed the disappearance of epileptic activity. As an innovative and more comprehensive approach to the study of epilepsy, the combined use of ESI, perfusion MRI, and PET may play an increasingly important role in the non-invasive evaluation of patients with refractory focal epilepsy.

Non-invasive pulsed arterial spin labeling (PASL) MRI is a method to study brain perfusion that does not require the administration of a contrast agent, which makes it a valuable diagnostic tool as it reduces cost and side effects. The purpose of the present study was to establish the viability of PASL as an alternative to dynamic susceptibility contrast (DSC-MRI) and other perfusion imaging methods in characterizing changes in perfusion patterns caused by seizures in epileptic patients. We evaluated 19 patients with PASL. Of these, the 9 affected by high-frequency seizures were observed during the peri-ictal period (within 5 hours since the last seizure), while the 10 patients affected by low-frequency seizures were observed in the post-ictal period. For comparison, 17/19 patients were also evaluated with DSC-MRI and CBF/CBV. PASL imaging showed focal vascular changes, which allowed the classification of patients in three categories: 8 patients characterized by increased perfusion, 4 patients with normal perfusion and 7 patients with decreased perfusion. PASL perfusion imaging findings were comparable to those obtained by DSC-MRI. Since PASL is a) sensitive to vascular alterations induced by epileptic seizures, b) comparable to DSC-MRI for detecting perfusion asymmetries, c) potentially capable of detecting time-related perfusion changes, it can be recommended for repeated evaluations, to identify the epileptic focus, and in follow-up and/or therapy-response assessment.

We analyzed the effect of afferent input on patterns of brain electrical activation in a 31-year-old man with progressive myoclonic epilepsy (PME) by measuring the somatosensory evoked potential (SSEP) amplitude at the scalp after median nerve stimulation and examining the changes in the functional magnetic resonance imaging blood oxygen level-dependent (fMRI BOLD) signal. High-amplitude SSEPs were elicited at the wrist in association with highly focal BOLD activation of the contralateral sensorimotor areas. By contrast, no diffuse activation of either the frontal or the posterior parietal cortical areas was observed, as seen in previously recorded data on SSEPs from a healthy control group. The highly focal BOLD activation in this patient suggests that cortex hyperexcitability might be limited to the sensorimotor cortex in PME. The combined EEG–fMRI findings highlight a dissociation between BOLD activation and neurophysiological findings.