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This paper asks whether integrating multimodal EEG and fMRI data offers a better characterisation of functional brain architectures than either modality alone. This evaluation rests upon a dynamic causal model that generates both EEG and fMRI data from the same neuronal dynamics. We introduce the use of Bayesian fusion to provide informative (empirical) neuronal priors – derived from dynamic causal modelling (DCM) of EEG data – for subsequent DCM of fMRI data. To illustrate this procedure, we generated synthetic EEG and fMRI timeseries for a mismatch negativity (or auditory oddball) paradigm, using biologically plausible model parameters (i.e., posterior expectations from a DCM of empirical, open access, EEG data). Using model inversion, we found that Bayesian fusion provided a substantial improvement in marginal likelihood or model evidence, indicating a more efficient estimation of model parameters, in relation to inverting fMRI data alone. We quantified the benefits of multimodal fusion with the information gain pertaining to neuronal and haemodynamic parameters – as measured by the Kullback-Leibler divergence between their prior and posterior densities. Remarkably, this analysis suggested that EEG data can improve estimates of haemodynamic parameters; thereby furnishing proof-of-principle that Bayesian fusion of EEG and fMRI is necessary to resolve conditional dependencies between neuronal and haemodynamic estimators. These results suggest that Bayesian fusion may offer a useful approach that exploits the complementary temporal (EEG) and spatial (fMRI) precision of different data modalities. We envisage the procedure could be applied to any multimodal dataset that can be explained by a DCM with a common neuronal parameterisation. •Multimodal DCM shows how the same neuronal activity causes multiple measurements.•Bayesian fusion of EEG/fMRI resolves conditional dependencies between parameters.•Information gain quantifies the added benefits of multimodal Bayesian fusion.

PreambleThe Society of Nuclear Medicine and Molecular Imaging (SNMMI) is an international scientific and professional organization founded in 1954 to promote the science, technology, and practical application of nuclear medicine. The European Association of Nuclear Medicine (EANM) is a professional non-profit medical association that facilitates communication worldwide between individuals pursuing clinical and research excellence in nuclear medicine. The EANM was founded in 1985. The merged International Society for Magnetic Resonance in Medicine (ISMRM) is an international, nonprofit, scientific association whose purpose is to promote communication, research, development, and applications in the field of magnetic resonance in medicine and biology and other related topics and to develop and provide channels and facilities for continuing education in the field.The ISMRM was founded in 1994 through the merger of the Society of Magnetic Resonance in Medicine and the Society of Magnetic Resonance Imaging. SNMMI, ISMRM, and EANM members are physicians, technologists, and scientists specializing in the research and practice of nuclear medicine and/or magnetic resonance imaging.The SNMMI, ISMRM, and EANM will periodically define new guidelines for nuclear medicine practice to help advance the science of nuclear medicine and/or magnetic resonance imaging and to improve the quality of service to patients throughout the world. Existing practice guidelines will be reviewed for revision or renewal, as appropriate, on their fifth anniversary or sooner, if indicated. Each practice guideline, representing a policy statement by the SNMMI/EANM/ISMRM, has undergone a thorough consensus process in which it has been subjected to extensive review. The SNMMI, ISMRM, and EANM recognize that the safe and effective use of diagnostic nuclear medicine imaging and magnetic resonance imaging requires specific training, skills, and techniques, as described in each document. Reproduction or modification of the published practice guideline by those entities not providing these services is not authorized.These guidelines are an educational tool designed to assist practitioners in providing appropriate care for patients. They are not inflexible rules or requirements of practice and are not intended, nor should they be used, to establish a legal standard of care. For these reasons and those set forth below, the SNMMI, the ISMRM, and the EANM caution against the use of these guidelines in litigation in which the clinical decisions of a practitioner are called into question.The ultimate judgment regarding the propriety of any specific procedure or course of action must be made by the physician or medical physicist in light of all the circumstances presented. Thus, there is no implication that an approach differing from the guidelines, standing alone, is below the standard of care. To the contrary, a conscientious practitioner may responsibly adopt a course of action different from that set forth in the guidelines when, in the reasonable judgment of the practitioner, such course of action is indicated by the condition of the patient, limitations of available resources, or advances in knowledge or technology subsequent to publication of the guidelines.The practice of medicine includes both the art and the science of the prevention, diagnosis, alleviation, and treatment of disease. The variety and complexity of human conditions make it impossible to always reach the most appropriate diagnosis or to predict with certainty a particular response to treatment.Therefore, it should be recognized that adherence to these guidelines will not ensure an accurate diagnosis or a successful outcome. All that should be expected is that the practitioner will follow a reasonable course of action based on current knowledge, available resources, and the needs of the patient to deliver effective and safe medical care. The sole purpose of these guidelines is to assist practitioners in achieving this objective.

Taking into account the complexity and limitations of clinical assessment in hypertrophic cardiomyopathy (HCM), imaging techniques play an essential role in the evaluation of patients with this disease. Thus, in HCM patients, imaging provides solutions for most clinical needs, from diagnosis to prognosis and risk stratification, from anatomical and functional assessment to ischaemia detection, from metabolic evaluation to monitoring of treatment modalities, from staging and clinical profiles to follow-up, and from family screening and preclinical diagnosis to differential diagnosis. Accordingly, a multimodality imaging (MMI) approach (including echocardiography, cardiac magnetic resonance, cardiac computed tomography, and cardiac nuclear imaging) is encouraged in the assessment of these patients. The choice of which technique to use should be based on a broad perspective and expert knowledge of what each technique has to offer, including its specific advantages and disadvantages. Experts in different imaging techniques should collaborate and the different methods should be seen as complementary, not as competitors. Each test must be selected in an integrated and rational way in order to provide clear answers to specific clinical questions and problems, trying to avoid redundant and duplicated information, taking into account its availability, benefits, risks, and cost.

Alzheimer's disease is a progressive neurodegenerative disease that typically manifests clinically as an isolated amnestic deficit that progresses to a characteristic dementia syndrome. Advances in neuroimaging research have enabled mapping of diverse molecular, functional, and structural aspects of Alzheimer's disease pathology in ever increasing temporal and regional detail. Accumulating evidence suggests that distinct types of imaging abnormalities related to Alzheimer's disease follow a consistent trajectory during pathogenesis of the disease, and that the first changes can be detected years before the disease manifests clinically. These findings have fuelled clinical interest in the use of specific imaging markers for Alzheimer's disease to predict future development of dementia in patients who are at risk. The potential clinical usefulness of single or multimodal imaging markers is being investigated in selected patient samples from clinical expert centres, but additional research is needed before these promising imaging markers can be successfully translated from research into clinical practice in routine care.

Hybrid imaging consists of a combination of two or more imaging modalities, which equally contribute to image information. To date, hybrid cardiovascular imaging can be performed by either merging images acquired on different scanners, or with truly hybrid PET/CT and PET/MR scanners. The European Association of Nuclear Medicine (EANM), and the European Association of Cardiovascular Imaging (EACVI) of the European Society of Cardiology (ESC) aim to review clinical situations that may benefit from the use of hybrid cardiac imaging and provide advice on acquisition protocols providing the most relevant information to reach diagnosis in various clinical situations.

Objectives To compare the diagnostic efficacies of B-mode ultrasound (US), strain elastography (SE), contrast-enhanced ultrasound (CEUS) and the combination of these modalities for breast lesions <1 cm in size. Methods Between January 2013 and October 2015, 203 inpatients with 209 sub-centimetre breast lesions categorised as BI-RADS-US (Breast Imaging Reporting and Data System for Ultrasound) 3-5 were included. US, SE and CEUS were performed to evaluate each lesion. The diagnostic performances of different ultrasonic modalities were compared. The diagnostic efficacies of BI-RADS-US and our re-rating systems were also compared. The pathology findings were used as the reference standard. Results The specificities of US, SE and CEUS for tumour differentiation were 17.4 %, 56.2 % and 86.0 %, respectively ( P  < 0.05); and the sensitivities were 100 %, 93.2 % and 93.2 % for US, SE and CEUS, respectively ( P  < 0.05). The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was 0.867 for original BI-RADS-US, 0.882 for BI-RADS-US combined with only SE, 0.953 for BI-RADS-US combined with only CEUS and 0.924 for BI-RADS-US combined with both SE and CEUS. The best combination was BI-RADS-US combined with only CEUS. Conclusions Evaluating sub-centimetre breast lesions with SE and CEUS could increase the diagnostic specificity while retaining high sensitivity compared with B-mode ultrasound. Key Points • Evaluating breast lesions with SE and CEUS could increase the diagnostic specificity • SE and CEUS offer alternatives to biopsy and possibly allow shorter-interval follow-ups • BI-RADS-US combined with CEUS exhibited the best diagnostic performance

The enhanced functional sensitivity offered by ultra-high field imaging may significantly benefit simultaneous EEG-fMRI studies, but the concurrent increases in artifact contamination can strongly compromise EEG data quality. In the present study, we focus on EEG artifacts created by head motion in the static B0 field. A novel approach for motion artifact detection is proposed, based on a simple modification of a commercial EEG cap, in which four electrodes are non-permanently adapted to record only magnetic induction effects. Simultaneous EEG-fMRI data were acquired with this setup, at 7 T, from healthy volunteers undergoing a reversing-checkerboard visual stimulation paradigm. Data analysis assisted by the motion sensors revealed that, after gradient artifact correction, EEG signal variance was largely dominated by pulse artifacts (81-93%), but contributions from spontaneous motion (4-13%) were still comparable to or even larger than those of actual neuronal activity (3-9%). Multiple approaches were tested to determine the most effective procedure for denoising EEG data incorporating motion sensor information. Optimal results were obtained by applying an initial pulse artifact correction step (AAS-based), followed by motion artifact correction (based on the motion sensors) and ICA denoising. On average, motion artifact correction (after AAS) yielded a 61% reduction in signal power and a 62% increase in VEP trial-by-trial consistency. Combined with ICA, these improvements rose to a 74% power reduction and an 86% increase in trial consistency. Overall, the improvements achieved were well appreciable at single-subject and single-trial levels, and set an encouraging quality mark for simultaneous EEG-fMRI at ultra-high field.

Inter-individual variability in symptoms and the dynamic nature of brain pathophysiology present significant challenges in constructing a robust diagnostic model for migraine. In this study, we aimed to integrate different types of magnetic resonance imaging (MRI), providing structural and functional information, and develop a robust machine learning model that classifies migraine patients from healthy controls by testing multiple combinations of hyperparameters to ensure stability across different migraine phases and longitudinally repeated data. Specifically, we constructed a diagnostic model to classify patients with episodic migraine from healthy controls, and validated its performance across ictal and interictal phases, as well as in a longitudinal setting. We obtained T1-weighted and resting-state functional MRI data from 50 patients with episodic migraine and 50 age- and sex-matched healthy controls, with follow-up data collected after one year. Morphological features, including cortical thickness, curvature, and sulcal depth, and functional connectivity features, such as low-dimensional representation of functional connectivity (gradient), degree centrality, and betweenness centrality, were utilized. We employed a regularization-based feature selection method combined with a random forest classifier to construct a diagnostic model. By testing the models with varying feature combinations, penalty terms, and spatial granularities within a strict cross-validation framework, we found that the combination of curvature, sulcal depth, cortical thickness, and functional gradient achieved a robust classification performance. The model performance was assessed using the test dataset and achieved 87% accuracy and 0.94 area under the curve (AUC) at distinguishing migraine patients from healthy controls, with 85%, 0.97 and 84%, 0.93 during the interictal and ictal/peri-ictal phases, respectively. When validated using follow-up data, which was not included during model training, the model achieved 91%, 94%, 89% accuracies and 0.96, 0.94, 0.98 AUC for the total, interictal, and ictal/peri-ictal phases, respectively, confirming its robustness. Feature importance and clinical association analyses exhibited that the somatomotor, limbic, and default mode regions could be reliable markers of migraine. Our findings, which demonstrate a robust diagnostic performance using multimodal MRI features and a machine-learning framework, may offer a valuable approach for clinical diagnosis across diverse cohorts and help alleviate the decision-making burden for clinicians.

PurposePET/MRI was introduced for clinical use in 2011 and is now an established modality for the imaging of brain and certain pelvic cancers, whereas clinical use for the imaging of other forms of cancer is not yet widespread. We therefore systematically investigated what has been published on the use of PET/MRI compared to PET/CT in the imaging of cancers outside the brain, focusing on clinical areas of application related to diagnosis, staging and restaging.MethodsA systematic search of PubMed/MEDLINE, Embase and the Cochrane Library was performed. Studies evaluating the diagnostic performance of simultaneous PET/MRI in cancer patients were chosen.ResultsA total of 3,138 publications were identified and 116 published during the period 2012–2018 were included and were grouped according to the major cancer forms: 13 head and neck (HNC), 9 breast (BC), 21 prostate (PC), 14 gynaecological, 13 gastrointestinal (GIC), and 46 various cancers. Data from studies comparing PET/MRI and PET/CT for staging/restaging suggested the superiority of 18F-FDG PET/MRI for the detection of tumour extension and retropharyngeal lymph node metastases in nasopharyngeal cancer, and for the detection of liver metastases and possibly bone marrow metastases in high-risk BC. FDG PET/MRI tended to be inferior for the detection of lung metastases in HNC and BC. 68Ga-PSMA-11 PET/MRI was superior to PET/CT for the detection of local PC recurrence. FDG PET/MRI was superior to FDG PET/CT for the detection of local tumour invasion in cervical cancer and had higher accuracy for the detection of liver metastases in colorectal cancer.ConclusionThe scoping review methodology resulted in the identification of a huge number of records, of which less than 5% were suitable for inclusion and only a limited number allowed conclusions on the advantages/disadvantages of PET/MRI compared to PET/CT in the oncological setting. There was evidence to support the use of FDG PET/MRI in staging of nasopharyngeal cancer and high-risk BC. Preliminary data indicate the superiority of PET/MRI for the detection of local recurrence in PC, local tumour invasion in cervical cancer, and liver metastases in colorectal cancer. These conclusions are based on small datasets and need to be further explored.