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Objective Giant cell arteritis (GCA) is a large vessel vasculitis, typically involving the aorta and its branches with predilection for the scalp arteries. Intracranial involvement is still part of ongoing research. We assessed inflammation of the intracranial arteries on 3D compressed sensing black-blood MRI (3D-CS-BB-MRI) in patients with GCA and age-matched controls. Methods One hundred and five patients with 3D-CS-BB-MRI of the brain were included in this retrospective dual-centre case–control study, 55 with diagnosed GCA and 50 age-matched controls. High-resolution 3D-CS-BB-MRI was performed on a 3 T MR scanner with a post-contrast 3D-compressed-sensing MR pulse sequence, specifically a T1-weighted sampling perfection, application-optimized contrasts using different flip angle evolution (SPACE) pulse sequence with whole-brain coverage and isotropic resolution of 0.55 mm3. Two neuroradiologists blinded to clinical data independently scored the cerebral arteries qualitatively for inflammation; circumferential vessel wall thickening and contrast enhancement were scored positive for vasculitis. Results Eight of 55 GCA patients (14.5%) showed inflammation of at least one intracranial artery. The internal carotid artery (ICA) was affected in 6/55 (10.9%), the vertebral artery in 4/55 (7.3%) and the basilar artery and posterior cerebral artery in 1/55 (1.8%). All patients with inflammatory changes reported headaches and none showed any focal neurological deficit. Besides headache and general weakness, there was no significant correlation between inflammation of the intracranial arteries and clinical symptoms. No age-matched control patient showed inflammatory changes of the intracranial arteries. Conclusion High-resolution 3D-CS-BB-MRI revealed inflammatory changes of intracranial arteries in 14.5% of GCA patients, with the intradural ICA as the most frequently affected vessel. [ABSTRACT FROM AUTHOR]

Background and Purpose: Paramagnetic rim lesions (PRLs) are an MRI biomarker of chronic inflammation in people with multiple sclerosis (MS). PRLs may aid in the diagnosis and prognosis of MS. However, manual identification of PRLs is time‐consuming and prone to poor interrater reliability. To address these challenges, the Automated Paramagnetic Rim Lesion (APRL) algorithm was developed to automate PRL detection. The primary objective of this study is to evaluate the accuracy of APRL for detecting PRLs in a multicenter setting. Methods: We applied APRL to a multicenter dataset, which included 3‐Tesla MRI acquired in 92 participants (43 with MS, 14 with clinically isolated syndrome [CIS]/radiologically isolated syndrome [RIS], 35 without RIS/CIS/MS). Subsequently, we assessed APRL's performance by comparing its results with manual PRL assessments carried out by a team of trained raters. Results: Among the 92 participants, expert raters identified 5637 white matter lesions and 148 PRLs. The automated segmentation method successfully captured 115 (78%) of the manually identified PRLs. Within these 115 identified lesions, APRL differentiated between manually identified PRLs and non‐PRLs with an area under the curve (AUC) of.73 (95% confidence interval [CI]: [.68,.78]). At the subject level, the count of APRL‐identified PRLs predicted MS diagnosis with an AUC of.69 (95% CI: [.57,.81]). Conclusion: Our study demonstrated APRL's capability to differentiate between PRLs and lesions without paramagnetic rims in a multicenter study. Automated identification of PRLs offers greater efficiency over manual identification and could facilitate large‐scale assessments of PRLs in clinical trials. [ABSTRACT FROM AUTHOR]

Introduction: In the current paper, the "carotid artery calcium score" method is presented with the target to offer a metric method to quantify the amount of calcification in the carotid artery. Model and Definition: The Volume of Interest (VOI) should be extracted and those voxels, with a Hounsfield Unit (HU) value ≥130, should be considered. The total weight value is determined by calculating the sum of the HU attenuation values of all voxels with values ≥130 HU. This value should be multiplied by the conversion factor ("or voxel size") and divided by a weighting factor, the attenuation threshold to consider a voxel as calcified (and therefore 130 HU): this equation determines the Carotid Artery Calcium Score (CACS). Results: In order to provide the demonstration of the potential feasibility of the model, the CACS was calculated in 131 subjects (94 males; mean age 72.7 years) for 235 carotid arteries (in 27 subjects, unilateral plaque was present) considered. The CACS value ranged from 0.67 to 11716. A statistically significant correlation was found (rho value = 0.663, p value =.0001) between the CACS in the right and left carotid plaques. Moreover, a statistically significant correlation between the age and the total CACS was present (rho value = 0.244, p value =.005), whereas no statistically significant difference was found in the distribution of CACS by gender (p =.148). The CACS was also tested at baseline and after contrast and no statistically significant difference was found. Conclusion: In conclusion, this method is of easy application, and it weights at the same time the volume and the degree of calcification in a unique parameter. This method needs to be tested to verify its potential utility, similar to the coronary artery calcium score, for the risk stratification of the occurrence of cerebrovascular events of the anterior circulation. Further studies using this new diagnostic tool to determine the prognostic value of carotid calcium quantification are needed. [ABSTRACT FROM AUTHOR]