Impact of reducing iodinated intravenous contrast volume in brain CT on image diagnostic quality.

PURPOSE: To investigate whether reducing the volume of intravenous iodinated contrast material injected during brain computed tomography (CT) provides reliable and accurate imaging without compromising diagnostic accuracy. METHODS: This prospective study enrolled patients undergoing enhanced brain CT at a single tertiary hospital. Subjects who agreed to participate received a reduced dose of 60 ml contrast. The images were compared to an age and gender-matched control group who received the conventional 80 cc dose. Neuroradiologists assessed image quality and interpretation using a 5-point Likert scale with six specific domains. Based on ICC, inter-rater reliability was high at 0.873. Multiple linear regression predicted overall diagnostic accuracy based on contrast dose, age, and gender. Visual Grading Characteristics (VGC) analysis was also performed to quantify regional brain enhancement differences between the two contrast groups. RESULTS: The study included 47 patients in the 60 cc group and 55 in the 80 cc control group. The results showed the 80 cc group had significantly higher enhancement ratings compared to 60 cc for all six structures assessed. The differences between groups ranged from -0.241 to -0.433 (p < 0.001) on the 5-point scale.The VGC analysis confirmed significantly greater brain parenchymal enhancement in the 80 cc group compared to the 60 cc group. CONCLUSION: The findings indicate that reducing the intravenous iodinated contrast material volume during brain CT from 80 cc to 60 cc leads to a statistically significant reduction in image quality and diagnostic accuracy. Further research with larger cohorts is needed to confirm these findings and assess the clinical impact of these differences.

Evaluation of Artificial Intelligence-Powered Identification of Large-Vessel Occlusions in a Comprehensive Stroke Center.

BACKGROUND AND PURPOSE: Artificial intelligence algorithms have the potential to become an important diagnostic tool to optimize stroke workflow. Viz LVO is a medical product leveraging a convolutional neural network designed to detect large-vessel occlusions on CTA scans and notify the treatment team within minutes via a dedicated mobile application. We aimed to evaluate the detection accuracy of the Viz LVO in real clinical practice at a comprehensive stroke center. MATERIALS AND METHODS: Viz LVO was installed for this study in a comprehensive stroke center. All consecutive head and neck CTAs performed from January 2018 to March 2019 were scanned by the algorithm for detection of large-vessel occlusions. The system results were compared with the formal reports of senior neuroradiologists used as ground truth for the presence of a large-vessel occlusion. RESULTS: A total of 1167 CTAs were included in the study. Of these, 404 were stroke protocols. Seventy-five (6.4%) patients had a large-vessel occlusion as ground truth; 61 were detected by the system. Sensitivity was 0.81, negative predictive value was 0.99, and accuracy was 0.94. In the stroke protocol subgroup, 72 (17.8%) of 404 patients had a large-vessel occlusion, with 59 identified by the system, showing a sensitivity of 0.82, negative predictive value of 0.96, and accuracy of 0.89. CONCLUSIONS: Our experience evaluating Viz LVO shows that the system has the potential for early identification of patients with stroke with large-vessel occlusions, hopefully improving future management and stroke care.