Reversible vasoconstriction syndrome is a complex of clinical symptoms and angiographic findings, which, while having a mostly benign clinical course, has clinical and imaging overlap with more serious disorders such as vasculitis and aneurysmal SAH and itself includes a minority of patients with fulminant vasoconstriction resulting in severe intracranial complications. Endovascular options for patients with refractory reversible cerebral vasoconstriction syndrome include intra-arterial vasodilator infusion similar to therapy for patients with vasospasm after SAH. To date, only case reports and 1 small series have discussed the utility of intraarterial vasodilators for the treatment of reversible cerebral vasoconstriction syndrome. We report an additional series of 11 medically refractory cases of presumed or proved reversible cerebral vasoconstriction syndrome successfully treated with intra-arterial verapamil infusion. Furthermore, we propose that the reversal of vasoconstriction, as seen on angiography, could fulfill a diagnostic criterion.

Background Brain tumors are the most common solid tumors and the leading cause of cancer-related death among all childhood cancers. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high interoperator variability. We present a multi-institutional deep learning-based method for automated brain extraction and segmentation of pediatric brain tumors based on multi-parametric MRI scans. Methods Multi-parametric scans (T1w, T1w-CE, T2, and T2-FLAIR) of 244 pediatric patients (n=215 internal and n=29 external cohorts) with de novo brain tumors, including a variety of tumor subtypes, were preprocessed and manually segmented to identify the brain tissue and tumor subregions into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). The internal cohort was split into training (n=151), validation (n=43), and withheld internal test (n=21) subsets. DeepMedic, a three-dimensional convolutional neural network, was trained and the model parameters were tuned. Finally, the network was evaluated on the withheld internal and external test cohorts. Results Dice similarity score (median±SD) was 0.91±0.10/0.88±0.16 for the whole tumor, 0.73±0.27/0.84±0.29 for ET, 0.79±19/0.74±0.27 for union of all non-enhancing components (i.e., NET, CC, ED), and 0.98±0.02 for brain tissue in both internal/external test sets. Conclusions Our proposed automated brain extraction and tumor subregion segmentation models demonstrated accurate performance on segmentation of the brain tissue and whole tumor regions in pediatric brain tumors and can facilitate detection of abnormal regions for further clinical measurements.

Low-field, portable MR imaging may expedite patient management in the setting of critical illness. We successfully implemented low-field MR imaging at the Queen Elizabeth Central Hospital in Malawi; a low-resource setting. We present our experience of low-field, portable MR imaging start-up and use in Malawi; the first of its kind in Sub-Saharan Africa, together with complementary troubleshooting mechanisms that may be used especially in similar resource-constrained contexts.

Real-time MRI-guided percutaneous sclerotherapy is a novel and evolving treatment for congenital lymphatic malformations in the head and neck. We elaborate on the specific steps necessary to perform an MRI-guided percutaneous sclerotherapy of lymphatic malformations including pre-procedure patient work-up and preparation, stepwise intraprocedural interventional techniques and post-procedure management. Based on our institutional experience, MRI-guided sclerotherapy with a doxycycline-gadolinium-based mixture as a sclerosant for lymphatic malformations of the head and neck region in children is well tolerated and effective.