Purpose of Review To give an up-to-date overview of the strengths and weaknesses of current imaging modalities in diagnosis and follow-up of skull base osteomyelitis (SBO). Recent Findings CT and MRI are both used for anatomical imaging, and nuclear techniques aid in functional process imaging. Hybrid techniques PET-CT and PET-MRI are the newest modalities which combine imaging strengths. Summary No single modality is able to address the scope of SBO. A combination of functional and anatomical imaging is needed, in the case of newly suspected SBO we suggest the use of PET-MRI (T1, T2, T1-FS-GADO, DWI) and separate HRCT for diagnosis and follow-up.

Because of the different attenuations of tissues at different energy levels, dual-energy CT offers tissue differentiation and characterization, reduction of artifacts, and remodeling of contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR), hereby creating new opportunities and insights in CT imaging. The applications for dual-energy imaging in neuroradiology are various and still expanding. Automated bone removal is used in CT angiography and CT venography of the intracranial vessels. Monoenergetic reconstructions can be used in patients with or without metal implants in the brain and spine to reduce artifacts, improve CNR and SNR, or to improve iodine conspicuity. Differentiation of iodine and hemorrhage is used in high-density lesions, after intra-arterial recanalization in stroke patients or after administration of contrast media. Detection of underlying (vascular and non-vascular) pathology and spot sign can be used in patients presenting with (acute) intracranial hemorrhage.

Purpose of Review To explain the technique of Dual-energy CT (DECT) and highlight its applications and advantages in head and neck radiology. Recent Findings Using DECT, additional datasets can be created next to conventional images. In head and neck radiology, three material decomposition algorithms can be used for improved lesion detection and delineation of the tumor. Iodine concentration measurements can aid in differentiating malignant from nonmalignant lymph nodes and benign posttreatment changes from tumor recurrence. Virtual non-calcium images can be used for detection of bone marrow edema. Virtual mono-energetic imaging can be useful for improved iodine conspicuity at lower keV and for reduction of metallic artifacts and increase in signal-to-noise ratio at higher keV. Summary DECT and its additional reconstructions can play an important role in head and neck cancer patients, from initial diagnosis and staging, to therapy planning, evaluation of treatment response and follow-up. Moreover, it can be helpful in imaging of infections and inflammation and parathyroid imaging as supplementary reconstructions can be obtained at lower or equal radiation dose compared with conventional single energy scanning.

OBJECTIVE. The purpose of this review is to summarize the principles and applications of dual-energy CT in evaluation of the brain and the intracranial blood vessels. CONCLUSION. One major advantage of dual-energy CT is the capability of material differentiation. In general, this property can be applied to bone removal in CT angiography for easier and faster postprocessing. In neuroradiology, material decomposition allows detection of hemorrhage on contrast-enhanced CT scans and facilitates the search for the underlying pathologic mechanism of hematomas. The combination of low radiation dose and advantageous spectral information (blood vs contrast) from these datasets justifies broad clinical implementation of dual-energy CT in neuroradiology.

Glioblastoma (GBM) is the most malignant primary brain tumor for which no curative treatment options exist. Non-invasive qualitative (Visually Accessible Rembrandt Images (VASARI)) and quantitative (radiomics) imaging features to predict prognosis and clinically relevant markers for GBM patients are needed to guide clinicians. A retrospective analysis of GBM patients in two neuro-oncology centers was conducted. The multimodal Cox-regression model to predict overall survival (OS) was developed using clinical features with VASARI and radiomics features in isocitrate dehydrogenase (IDH)-wild type GBM. Predictive models for IDH-mutation, 06-methylguanine-DNA-methyltransferase (MGMT)-methylation and epidermal growth factor receptor (EGFR) amplification using imaging features were developed using machine learning. The performance of the prognostic model improved upon addition of clinical, VASARI and radiomics features, for which the combined model performed best. This could be reproduced after external validation (C-index 0.711 95% CI 0.64–0.78) and used to stratify Kaplan–Meijer curves in two survival groups (p-value < 0.001). The predictive models performed significantly in the external validation for EGFR amplification (area-under-the-curve (AUC) 0.707, 95% CI 0.582–8.25) and MGMT-methylation (AUC 0.667, 95% CI 0.522–0.82) but not for IDH-mutation (AUC 0.695, 95% CI 0.436–0.927). The integrated clinical and imaging prognostic model was shown to be robust and of potential clinical relevance. The prediction of molecular markers showed promising results in the training set but could not be validated after external validation in a clinically relevant manner. Overall, these results show the potential of combining clinical features with imaging features for prognostic and predictive models in GBM, but further optimization and larger prospective studies are warranted.