EANM practice guidelines for an appropriate use of PET and SPECT for patients with epilepsy.

Epilepsy is one of the most frequent neurological conditions with an estimated prevalence of more than 50 million people worldwide and an annual incidence of two million. Although pharmacotherapy with anti-seizure medication (ASM) is the treatment of choice, ~30% of patients with epilepsy do not respond to ASM and become drug resistant. Focal epilepsy is the most frequent form of epilepsy. In patients with drug-resistant focal epilepsy, epilepsy surgery is a treatment option depending on the localisation of the seizure focus for seizure relief or seizure freedom with consecutive improvement in quality of life. Beside examinations such as scalp video/electroencephalography (EEG) telemetry, structural, and functional magnetic resonance imaging (MRI), which are primary standard tools for the diagnostic work-up and therapy management of epilepsy patients, molecular neuroimaging using different radiopharmaceuticals with single-photon emission computed tomography (SPECT) and positron emission tomography (PET) influences and impacts on therapy decisions. To date, there are no literature-based praxis recommendations for the use of Nuclear Medicine (NM) imaging procedures in epilepsy. The aims of these guidelines are to assist in understanding the role and challenges of radiotracer imaging for epilepsy; to provide practical information for performing different molecular imaging procedures for epilepsy; and to provide an algorithm for selecting the most appropriate imaging procedures in specific clinical situations based on current literature. These guidelines are written and authorized by the European Association of Nuclear Medicine (EANM) to promote optimal epilepsy imaging, especially in the presurgical setting in children, adolescents, and adults with focal epilepsy. They will assist NM healthcare professionals and also specialists such as Neurologists, Neurophysiologists, Neurosurgeons, Psychiatrists, Psychologists, and others involved in epilepsy management in the detection and interpretation of epileptic seizure onset zone (SOZ) for further treatment decision. The information provided should be applied according to local laws and regulations as well as the availability of various radiopharmaceuticals and imaging modalities.

A 3D convolutional neural network to classify subjects as Alzheimer's disease, frontotemporal dementia or healthy controls using brain 18F-FDG PET.

With the arrival of disease-modifying drugs, neurodegenerative diseases will require an accurate diagnosis for optimal treatment. Convolutional neural networks are powerful deep learning techniques that can provide great help to physicians in image analysis. The purpose of this study is to introduce and validate a 3D neural network for classification of Alzheimer's disease (AD), frontotemporal dementia (FTD) or cognitively normal (CN) subjects based on brain glucose metabolism. Retrospective [18F]-FDG-PET scans of 199 CE, 192 FTD and 200 CN subjects were collected from our local database, Alzheimer's disease and frontotemporal lobar degeneration neuroimaging initiatives. Training and test sets were created using randomization on a 90 %-10 % basis, and training of a 3D VGG16-like neural network was performed using data augmentation and cross-validation. Performance was compared to clinical interpretation by three specialists in the independent test set. Regions determining classification were identified in an occlusion experiment and Gradient-weighted Class Activation Mapping. Test set subjects were age- and sex-matched across categories. The model achieved an overall 89.8 % accuracy in predicting the class of test scans. Areas under the ROC curves were 93.3 % for AD, 95.3 % for FTD, and 99.9 % for CN. The physicians' consensus showed a 69.5 % accuracy, and there was substantial agreement between them (kappa = 0.61, 95 % CI: 0.49-0.73). To our knowledge, this is the first study to introduce a deep learning model able to discriminate AD and FTD based on [18F]-FDG PET scans, and to isolate CN subjects with excellent accuracy. These initial results are promising and hint at the potential for generalization to data from other centers.

Optimization of Automated Radiosynthesis of Gallium-68-Labeled PSMA11 with Two [68Ge]Ge/[68Ga]Ga Generators: Fractional Elution or Prepurification?

Prostate cancer is one of the most common forms of cancer in men. An imaging technique for its diagnosis is [68Ga]-prostate-specific membrane antigen ([68Ga]Ga-PSMA-11) positron emission tomography (PET). To address the increasing demand for [68Ga]-labeled peptides and reduce the cost of radiosynthesis, it is therefore necessary to optimize the elution process of [68Ge]Ge/[68Ga]Ga generators. This study aims to identify the most effective approach for optimizing radiosynthesis using double elution in parallel of two [68Ge]Ge/[68Ga]Ga generators. Two methods have been tested: one using prepurification, and the other using fractionated elution. Five synthesis sequences were conducted using each method. The mean labeling yields for double elution with prepurification were 45.8 ± 29.4 (mean ± standard deviation) and none met the required criteria. The mean labeling yields for the fractionated double elution were 97.5 ± 1.9 (mean ± standard deviation) meeting the criteria, significantly superior to the prepurification method (p = 0.012), and similar to those of simple elution. This study showed that fractionated double elution from [68Ge]Ge/[68Ga]Ga generators produced a significantly higher labeling yield than double elution with prepurification, resulting in a larger activity recovered via radiosynthesis, thereby allowing more diagnostic tests to be performed.

Deep Learning on Bone Scintigraphy to Detect Abnormal Cardiac Uptake at Risk of Cardiac Amyloidosis.

BACKGROUND: Cardiac uptake on technetium-99m whole-body scintigraphy (WBS) is almost pathognomonic of transthyretin cardiac amyloidosis. The rare false positives are often related to light-chain cardiac amyloidosis. However, this scintigraphic feature remains largely unknown, leading to misdiagnosis despite characteristic images. A retrospective review of all WBSs in a hospital database to detect those with cardiac uptake may allow the identification of undiagnosed patients. OBJECTIVES: The authors sought to develop and validate a deep learning-based model that automatically detects significant cardiac uptake (Perugini grade ≥2) on WBS from large hospital databases in order to retrieve patients at risk of cardiac amyloidosis. METHODS: The model is based on a convolutional neural network with image-level labels. The performance evaluation was performed with C-statistics using a 5-fold cross-validation scheme stratified so that the proportion of positive and negative WBSs remained constant across folds and using an external validation data set. RESULTS: The training data set consisted of 3,048 images: 281 positives (Perugini grade ≥2) and 2,767 negatives. The external validation data set consisted of 1,633 images: 102 positives and 1,531 negatives. The performance of the 5-fold cross-validation and external validation was as follows: 98.9% (± 1.0) and 96.1% for sensitivity, 99.5% (± 0.4) and 99.5% for specificity, and 0.999 (SD = 0.000) and 0.999 for the area under the curve of the receiver-operating characteristic curves. Sex, age <90 years, body mass index, injection-acquisition delay, radionuclides, and the indication of WBS only slightly affected performances. CONCLUSIONS: The authors' detection model is effective at identifying patients with cardiac uptake Perugini grade ≥2 on WBS and may help in the diagnosis of patients with cardiac amyloidosis.

EANM procedure guidelines for brain PET imaging using [18F]FDG, version 3.

The present procedural guidelines summarize the current views of the EANM Neuro-Imaging Committee (NIC). The purpose of these guidelines is to assist nuclear medicine practitioners in making recommendations, performing, interpreting, and reporting results of [18F]FDG-PET imaging of the brain. The aim is to help achieve a high-quality standard of [18F]FDG brain imaging and to further increase the diagnostic impact of this technique in neurological, neurosurgical, and psychiatric practice. The present document replaces a former version of the guidelines that have been published in 2009. These new guidelines include an update in the light of advances in PET technology such as the introduction of digital PET and hybrid PET/MR systems, advances in individual PET semiquantitative analysis, and current broadening clinical indications (e.g., for encephalitis and brain lymphoma). Further insight has also become available about hyperglycemia effects in patients who undergo brain [18F]FDG-PET. Accordingly, the patient preparation procedure has been updated. Finally, most typical brain patterns of metabolic changes are summarized for neurodegenerative diseases. The present guidelines are specifically intended to present information related to the European practice. The information provided should be taken in the context of local conditions and regulations.

EANM practice guideline/SNMMI procedure standard for dopaminergic imaging in Parkinsonian syndromes 1.0.

PURPOSE: This joint practice guideline or procedure standard was developed collaboratively by the European Association of Nuclear Medicine (EANM) and the Society of Nuclear Medicine and Molecular Imaging (SNMMI). The goal of this guideline is to assist nuclear medicine practitioners in recommending, performing, interpreting, and reporting the results of dopaminergic imaging in parkinsonian syndromes. METHODS: Currently nuclear medicine investigations can assess both presynaptic and postsynaptic function of dopaminergic synapses. To date both EANM and SNMMI have published procedural guidelines for dopamine transporter imaging with single photon emission computed tomography (SPECT) (in 2009 and 2011, respectively). An EANM guideline for D2 SPECT imaging is also available (2009). Since the publication of these previous guidelines, new lines of evidence have been made available on semiquantification, harmonization, comparison with normal datasets, and longitudinal analyses of dopamine transporter imaging with SPECT. Similarly, details on acquisition protocols and simplified quantification methods are now available for dopamine transporter imaging with PET, including recently developed fluorinated tracers. Finally, [18F]fluorodopa PET is now used in some centers for the differential diagnosis of parkinsonism, although procedural guidelines aiming to define standard procedures for [18F]fluorodopa imaging in this setting are still lacking. CONCLUSION: All these emerging issues are addressed in the present procedural guidelines for dopaminergic imaging in parkinsonian syndromes.

Metabolic correlates of cognitive impairment in mesial temporal lobe epilepsy.

PURPOSE: The purpose of the study was to determine the correlations between brain metabolism and cognitive impairment in patients with drug-resistant mesial temporal lobe epilepsy (MTLE). METHODS: [18F]-FluoroDeoxyGlucose positron emission tomography ([18F]-FDG-PET) and neuropsychological assessment were performed in 97 patients with MTLE (53 females, 15-56 years old, mean: 31.6 years, standard deviation (SD) = 10.4) with unilateral hippocampal sclerosis (HS, 49 left). We compared brain metabolism and gray matter volume (GMV) between patients with cognitive impairment (intelligence quotient (IQ) and memory index <80) and patients with normal cognition, using statistical parametric mapping (SPM), in the whole population then in right and left HS (RHS, LHS) separately. RESULTS: Intelligence quotient (40-121, mean: 83.7 ±â€¯16.9) and memory index (45-133, mean: 80.7 ±â€¯19.3) were impaired in 43% and 51% of the patients, respectively, similarly in RHS and LHS. We did not find any correlations between IQ and clinical factors related to epilepsy; however, there was a significant correlation between low memory index and early age of onset in LHS (p = 0.021), and widespread epileptogenic zone in the whole population (p = 0.033). Impaired IQ correlated with extratemporal hypometabolism, involving frontoparietal networks implicated in the default mode network (DMN), predominantly in the midline cortices. Metabolic asymmetry regarding HS lateralization included the precuneus (pC) in LHS and the anterior cingulate cortex (ACC) in RHS, both areas corresponding to key nodes of the DMN. Memory index correlated with the same frontoparietal networks as for IQ, with an additional involvement of the temporal lobes, which was ipsilateral in RHS and contralateral in LHS. A diffuse decrease of GMV including the ipsilateral hippocampus correlated with cognitive impairment; however, the structural alterations did not match with the hypometabolic areas. CONCLUSIONS: Cognitive impairment in MTLE correlates with extratemporal hypometabolism, involving the mesial frontoparietal networks implicated in the DMN and suggesting a disconnection with the affected hippocampus. Asymmetric alterations of connectivity may sustain the predominant ACC and pC metabolic decrease in patients with cognitive impairment.

Three-year changes of cortical 18F-FDG in amnestic vs. non-amnestic sporadic early-onset Alzheimer's disease.

PURPOSE: To examine and compare longitudinal changes of cortical glucose metabolism in amnestic and non-amnestic sporadic forms of early-onset Alzheimer's disease and assess potential associations with neuropsychological performance over a 3-year period time. METHODS: Eighty-two participants meeting criteria for early-onset (< 65 years) sporadic form of probable Alzheimer's disease and presenting with a variety of clinical phenotypes (47 amnestic and 35 non-amnestic forms) were included at baseline and followed up for 1.44 ± 1.23 years. All of the participants underwent a work-up at baseline and every year during the follow-up period, which includes clinical examination, neuropsychological testing, genotyping, cerebrospinal fluid biomarker assays, and structural MRI and 18F-FDG PET. Vertex-wise partial volume-corrected glucose metabolic maps across the entire cortical surface were generated and longitudinally assessed together with the neuropsychological scores using linear mixed-effects modeling as a function of amnestic and non-amnestic sporadic forms of early-onset Alzheimer's disease. RESULTS: Similar evolution patterns of glucose metabolic decline between amnestic and non-amnestic forms were observed in widespread neocortical cortices. However, only non-amnestic forms appeared to have a greater reduction of glucose metabolism in lateral orbitofrontal and bilateral medial temporal cortices associated with more severe declines of neuropsychological performance compared with amnestic forms. Furthermore, results suggest that glucose metabolic decline in amnestic forms would progress along an anterior-to-posterior axis, whereas glucose metabolic decline in non-amnestic forms would progress along a posterior-to-anterior axis. CONCLUSIONS: We found differences in spatial distribution and temporal trajectory of glucose metabolic decline between amnestic and non-amnestic early-onset Alzheimer's disease groups, suggesting that one might want to consider treating the two forms of the disease as two separate entities.

Using EQ·PET to reduce reconstruction-dependent variations in [18F]FDG-PET brain imaging.

This study aims at assessing whether EANM harmonisation strategy combined with EQ·PET methodology could be successfully applied to harmonize brain 2-deoxy-2[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography (PET) images. The NEMA NU 2 body phantom was prepared according to the EANM guidelines with an [18F]FDG solution. Raw PET phantom data were reconstructed with three different reconstruction protocols frequently used in clinical PET brain imaging: ([Formula: see text]) Ordered subset expectation maximization (OSEM) 3D with time of flight (TOF), 2 iterations and 21 subsets; ([Formula: see text]) OSEM 3D with TOF, 6 iterations and 21 subsets; and ([Formula: see text]) OSEM 3D with TOF, point spread function (PSF), and 8 iterations and 21 subsets. EQ·PET filters were computed as the Gaussian smoothing that best independently aligned the recovery coefficients (RCs) of reconstructions [Formula: see text] and [Formula: see text] with the RCs of the reference reconstruction, [Formula: see text]. The performance of the EQ·PET filter to reduce variations in quantification due to differences in reconstruction was investigated using clinical PET brain images of 35 early-onset Alzheimer's disease (EOAD) patients. Qualitative assessments and multiple quantitative metrics on the cortical surface at different scale levels with or without partial volume effect correction were evaluated on the [18F]FDG brain data before and after application of the EQ·PET filter. The EQ·PET methodology succeeded in finding the optimal smoothing that minimised root-mean-square error (RMSE) calculated using human brain [18F]FDG-PET datasets of EOAD patients, providing harmonized comparisons in the neurological context. Performance was superior for TOF than for TOF + PSF reconstructions. Results showed the capability of the EQ·PET methodology to minimize reconstruction-induced variabilities between brain [18F]FDG-PET images. However, moderate variabilities remained after harmonizing PSF reconstructions with standard non-PSF OSEM reconstructions, suggesting that precautions should be taken when using PSF modelling.

The relationship between CSF biomarkers and cerebral metabolism in early-onset Alzheimer's disease.

PURPOSE: One can reasonably suppose that cerebrospinal spinal fluid (CSF) biomarkers can identify distinct subgroups of Alzheimer's disease (AD) patients. In order to better understand differences in CSF biomarker patterns, we used FDG PET to assess cerebral metabolism in CSF-based subgroups of AD patients. METHODS: Eighty-five patients fulfilling the criteria for probable early-onset AD (EOAD) underwent lumbar puncture, brain 18F-FDG PET and MRI. A cluster analysis was performed, with the CSF biomarkers for AD as variables. Vertex-wise, partial-volume-corrected metabolic maps were computed for the patients and compared between the clusters of patients. Linear correlations between each CSF biomarker and the metabolic maps were assessed. RESULTS: Three clusters emerged. The "Aß42" cluster contained 32 patients with low levels of Aß42, while tau and p-tau remained within the normal range. The "Aß42 + tau" cluster contained 41 patients with low levels of Aß42 and high levels of tau and p-tau. Lastly, the "tau" cluster contained 12 patients with very high levels of tau and p-tau and low-normal levels of Aß42. There were no inter-cluster differences in age, sex ratio, educational level, APOE genotype, disease duration or disease severity. The "Aß42 + tau" and "tau" clusters displayed more marked frontal hypometabolism than the "Aß42" cluster did, and frontal metabolism was significantly negatively correlated with the CSF tau level. The "Aß42" and "Aß42 + tau" clusters displayed more marked hypometabolism in the left occipitotemporal region than the "tau" cluster did, and metabolism in this region was significantly and positively correlated with the CSF Aß42 level. CONCLUSION: The CSF biomarkers can be used to identify metabolically distinct subgroups of patients with EOAD. Future research should seek to establish whether these biochemical differences have clinical consequences.

Correction to: 18F-FDG PET in drug-resistant epilepsy due to focal cortical dysplasia type 2: additional value of electroclinical data and coregistration with MRI.

The original version of this article has added numbers in the text which are unnecessary. Correct line should be: "We also performed PET/MRI based surgical resections in an increasing number of MRI negative/ doubtful cases with favourable outcome."

18F-FDG PET in drug-resistant epilepsy due to focal cortical dysplasia type 2: additional value of electroclinical data and coregistration with MRI.

PURPOSE: To assess the localizing value of 18F-FDG PET in patients operated on for drug-resistant epilepsy due to focal cortical dysplasia type 2 (FCD2). METHODS: We analysed 18F-FDG PET scans from 103 consecutive patients (52 males, 7-65 years old) with histologically proven FCD2. PET and MRI data were first reviewed by visual analysis blinded to clinical information and FCD2 location. The additional value of electroclinical data and PET/MRI coregistration was assessed by comparison with pathological results and surgical outcomes. RESULTS: Visual analysis of PET scans showed focal or regional hypometabolism corresponding to the FCD2 in 45 patients (44%), but the findings were doubtful or misleading in 37 patients and negative in 21. When considering electroclinical data, positive localization was obtained in 73 patients, and this increased to 85 (83%) after coregistration of PET and MRI data. Under the same conditions, MRI was positive in 61 patients (59%), doubtful in 15 and negative in 27. The additional value of PET was predominant in patients negative or doubtful on MRI, localizing the FCD2 in 35 patients (83%). Interobserver agreement correlated with the grade of hypometabolism: it was good in patients with mild to severe hypometabolism (82-95%), but moderate in those with subtle/doubtful hypometabolism (45%). The main factors influencing positive PET localization were the grade of hypometabolism and the size of the FCD2 (P < 0.0001). Misleading location (nine patients) was associated with a small FCD2 in the mesial frontal and central regions. Following limited cortical resection mainly located in extratemporal areas (mean follow-up 5.6 years), a seizure-free outcome was achieved in 94% of patients, including Engel's class IA in 72%. CONCLUSION: In this series, 18F-FDG PET contributed to the localization of FCD2 in 83% of patients. This high localizing value was obtained by integration of electroclinical data and PET/MRI coregistration. This approach may help improve the surgical outcome in extratemporal epilepsy, even in patients negative on MRI.

Added value of 18F-florbetaben amyloid PET in the diagnostic workup of most complex patients with dementia in France: A naturalistic study.

INTRODUCTION: Although some studies have previously addressed the clinical impact of amyloid positron emission tomography (PET), none has specifically addressed its selective and hierarchical implementation in relation to cerebrospinal fluid analysis in a naturalistic setting. METHODS: This multicenter study was performed at French tertiary memory clinics in patients presenting with most complex clinical situations (i.e., early-onset, atypical clinical profiles, suspected mixed etiological conditions, unexpected rate of progression), for whom cerebrospinal fluid analysis was indicated but either not feasible or considered as noncontributory (ClinicalTrials.gov: NCT02681172). RESULTS: Two hundred five patients were enrolled with evaluable florbetaben PET scans; 64.4% of scans were amyloid positive. PET results led to changed diagnosis and improved confidence in 66.8% and 81.5% of patients, respectively, and altered management in 80.0% of cases. DISCUSSION: High-level improvement of diagnostic certainty and management is provided by selective and hierarchical implementation of florbetaben PET into current standard practices for the most complex dementia cases.

18F-FDG PET hypometabolism patterns reflect clinical heterogeneity in sporadic forms of early-onset Alzheimer's disease.

Until now, hypometabolic patterns and their correlations with neuropsychological performance have not been assessed as a function of the various presentations of sporadic early-onset Alzheimer's disease (EOAD). Here, we processed and analyzed the patients' metabolic maps at the vertex and voxel levels by using a nonparametric, permutation method that also regressed out the effects of cortical thickness and gray matter volume, respectively. The hypometabolism patterns in several areas of the brain were significantly correlated with the clinical manifestations. These areas included the paralimbic regions for typical presentations of sporadic EOAD. For atypical presentations, the hypometabolic regions included Broca's and Wernicke's areas and the pulvinar in language forms, bilateral primary and higher processing visual regions (with right predominance) in visuospatial forms, and the bilateral prefrontal cortex in executive forms. Similar hypometabolism patterns were also observed in a correlation analysis of the 18F-FDG PET data versus domain-specific, neuropsychological test scores. These heterogeneities might reflect different underlying pathophysiological processes in particular clinical presentations of sporadic EOAD and should be taken into account in future longitudinal and therapeutic studies.

18F-FDG-PET patterns of surgical success and failure in mesial temporal lobe epilepsy.

OBJECTIVE: To search for [18F]-fluorodeoxyglucose (FDG)-PET patterns predictive of long-term prognosis in surgery for drug-resistant mesial temporal lobe epilepsy (MTLE) due to hippocampal sclerosis (HS). METHODS: We analyzed metabolic data with [18F]-FDG-PET in 97 patients with MTLE (53 female participants; age range 15-56 years) with unilateral HS (50 left) and compared the metabolic patterns, electroclinical features, and structural atrophy on MRI in patients with the best outcome after anteromesial temporal resection (Engel class IA, completely seizure-free) to those with a non-IA outcome, including suboptimal outcome and failure. Imaging processing was performed with statistical parametric mapping (SPM5). RESULTS: With a mean follow-up of >6 years (range 2-14 years), 85% of patients achieved a class I outcome, including 45% in class IA. Class IA outcome was associated with a focal anteromesial temporal hypometabolism, whereas non-IA outcome correlated with extratemporal metabolic changes that differed according to the lateralization: ipsilateral mesial frontal and perisylvian hypometabolism in right HS and contralateral fronto-insular hypometabolism and posterior white matter hypermetabolism in left HS. Suboptimal outcome presented a metabolic pattern similar to the best outcome but with a larger involvement of extratemporal areas, including the contralateral side in left HS. Failure was characterized by a mild temporal involvement sparing the hippocampus and relatively high extratemporal hypometabolism on both sides. These findings were concordant with electroclinical features reflecting the organization of the epileptogenic zone but were independent of the structural abnormalities detected on MRI. CONCLUSIONS: [18F]-FDG-PET patterns help refine the prognostic factors in MTLE and should be implemented in predictive models for epilepsy surgery.