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Background Particularly at the beginning, the COVID-19 (coronavirus disease 2019) pandemic caused a reduction in the number of interventions in interventional radiology. At the same time, interventional training became more challenging. Infectious patients and disease transmission within interventional radiology departments continue to pose significant challenges. Objectives This article describes the status and recommendations for interventional radiological procedures in COVID-19 patients. Materials and methods Guidelines and recommendations from international and national societies as well as original works and reviews were evaluated. Results Interventional radiological care of COVID-19 patients with complicated courses of infection has become established during the course of the pandemic. To protect patients and staff, interventions in COVID-19 patients should be prioritized, performed in separate procedure rooms if possible, and patients should be tested before interventions. Logistics, staff planning, and hygiene measures should be continuously optimized. Conclusions Structured workflows within interventional radiology in dealing with COVID-19 patients appear necessary to minimize infection risks and to guarantee the staff's work capability and health. In order to develop concepts for the handling of COVID-19 patients and to be prepared for potential upcoming waves of infections, recommendations of the Robert Koch Institute (RKI) and (inter-)national professional societies are helpful.

AIM: To evaluate the diagnostic value of spectral detector computed tomography (SDCT)-derived iodine overlay maps and low-energy virtual mono-energetic images (VMI) for the initial locoregional assessment of primary, therapy-naive head and neck cancer. MATERIALS AND METHODS: Fifty-six patients with histologically confirmed untreated squamous cell carcinoma of the head and neck who underwent SDCT of the neck for staging purposes were included in this retrospective study. Attenuation, image noise as well as signal-and contrast-to-noise ratios (S-/CNR) in VMI40-70keV were obtained from region of interest (ROI)-based measurements in the tumour and important anatomical landmarks (sternocleidomastoid muscle, subcutaneous fat, thyroid gland, submandibular gland, carotid artery, and jugular vein). Tumour conspicuity and delineation, as well as subjective image quality, were rated for conventional images, VMI40-70keV, and iodine overlay maps using five-point Likert scales. RESULTS: The CNR of the tumour versus the floor of the mouth and the CNR of the tumour versus the sternocleidomastoid muscle was significantly higher in VMI40keV in comparison to conventional images (10.0 f 7.3 versus 3.8 f 3.3 and 11.3 f 7.6 versus 3.6 f 2.8; p<0.05 each). This was supported by qualitative results, as tumour conspicuity and delineation received superior ratings in iodine overlay maps and VMI40keV compared to conventional images (5 [3-5] and 5 [4-5] versus 3 [2-5]; 5 [2-5] and 5 [3-5] versus 3 [2-4], respectively, all p<0.05). VMI40keV yielded the highest score among all included image reconstructions for overall image quality (p<0.05 all). CONCLUSION: Iodine overlay maps and low-energy VMI derived from SDCT improve initial assessment of primary squamous cell carcinoma of the head and neck compared to conventional images. (c) 2022 Published by Elsevier Ltd on behalf of The Royal College of Radiologists.

BACKGROUND AND PURPOSE: MR imaging is the technique of choice for follow-up of patients with brain metastases, yet the radiologic assessment is often tedious and error-prone, especially in examinations with multiple metastases or subtle changes. This study aimed to determine whether using automated color-coding improves the radiologic assessment of brain metastases compared with conventional reading. MATERIALS AND METHODS: One hundred twenty-one pairs of follow-up examinations of patients with brain metastases were assessed. Two radiologists determined the presence of progression, regression, mixed changes, or stable disease between the follow-up examinations and indicated subjective diagnostic certainty regarding their decisions in a conventional reading and a second reading using automated color-coding after an interval of 8 weeks. RESULTS: The rate of correctly classified diagnoses was higher (91.3%, 221/242, versus 74.0%, 179/242, P?

Deep learning-based automated detection and segmentation of brain metastases in malignant melanoma yield high detection and segmentation accuracy with false-positive findings of <1 per scan. BACKGROUND AND PURPOSE: Malignant melanoma is an aggressive skin cancer in which brain metastases are common. Our aim was to establish and evaluate a deep learning model for fully automated detection and segmentation of brain metastases in patients with malignant melanoma using clinical routine MR imaging. MATERIALS AND METHODS: Sixty-nine patients with melanoma with a total of 135 brain metastases at initial diagnosis and available multiparametric MR imaging datasets (T1-/T2-weighted, T1-weighted gadolinium contrast-enhanced, FLAIR) were included. A previously established deep learning model architecture (3D convolutional neural network; DeepMedic) simultaneously operating on the aforementioned MR images was trained on a cohort of 55 patients with 103 metastases using 5-fold cross-validation. The efficacy of the deep learning model was evaluated using an independent test set consisting of 14 patients with 32 metastases. Manual segmentations of metastases in a voxelwise manner (T1-weighted gadolinium contrast-enhanced imaging) performed by 2 radiologists in consensus served as the ground truth. RESULTS: After training, the deep learning model detected 28 of 32 brain metastases (mean volume, 1.0 [SD, 2.4]?cm(3)) in the test cohort correctly (sensitivity of 88%), while false-positive findings of 0.71 per scan were observed. Compared with the ground truth, automated segmentations achieved a median Dice similarity coefficient of 0.75. CONCLUSIONS: Deep learning?based automated detection and segmentation of brain metastases in malignant melanoma yields high detection and segmentation accuracy with false-positive findings of <1 per scan.

BACKGROUND AND PURPOSE: Our aim was to evaluate whether improved gray-white matter differentiation in cranial CT by means of 65-keV virtual monoenergetic images enables a radiation dose reduction compared to conventional images. MATERIALS AND METHODS: One hundred forty consecutive patients undergoing 171 spectral detector CTs of the head between February and November 2017 (56 +/- 19 years of age; male/female ratio, 56%/44%) were retrospectively included. The tube current-time product was reduced during the study period, resulting in 61, 55, and 55 patients being examined with 320, 290, and 260 mAs, respectively. All other scanning parameters were kept identical. The volume CT dose index was recorded. ROIs were placed in gray and white matter on conventional images and copied to identical positions in 65- keV virtual monoenergetic images. The contrast-to-noise ratio was calculated. Two radiologists blinded to the reconstruction technique evaluated image quality on a 5-point Likert-scale. Statistical assessment was performed using ANOVA and Wilcoxon test adjusted for multiple comparisons. RESULTS: The mean volume CT dose index was 55, 49.8, and 44.7 mGy using 320, 290, and 260 mAs, respectively. Irrespective of the volume CT dose index, noise was significantly lower in 65- keV virtual monoenergetic images compared with conventional images (65- keV virtual monoenergetic images/conventional images: extraocular muscle with 49.8 mGy, 3.7 +/- 1.3/5.6 +/- 1.6 HU, P < .001). Noise slightly increased with a reduced radiation dose (eg, extraocular muscle in conventional images: 5.3 +/- 1.4/5.6 +/- 1.6/6.1 +/- 2.1 HU). Overall, the contrast-to-noise ratio in 65- keV virtual monoenergetic images was superior to that in conventional images irrespective of the volume CT dose index (P < .001). Particularly, 65-keV virtual monoenergetic images with 44.7 mGy showed significantly lower noise and a higher contrast-to-noise ratio than conventional images with 55 mGy (P < .001). Subjecti