Traumatic neuroradiological emergencies, such as brain or spinal cord injuries, require swift and precise diagnosis for effective treatment. Computed tomography (CT) scans are crucial in emergency settings due to their speed and accessibility, making them a primary tool for assessing trauma. However, the use of ionizing radiation in CT scans raises concerns about long-term risks, including the potential development of cancer. To mitigate these risks, modern artificial intelligence (AI) reconstruction algorithms have been introduced, offering the possibility of maintaining high-quality imaging while reducing radiation exposure.

Researchers from Eberhard Karls-University Tuebingen in Germany conducted a study to evaluate a deep learning-based denoising (DLD) algorithm’s effectiveness in reducing radiation doses during traumatic neuroradiological CT scans. The study compared full-dose and low-dose CT scans processed with both traditional iterative reconstruction (IR2) and the DLD algorithm. A total of 100 patients were included, and four neuroradiologists assessed the images’ quality. The results indicated that the DLD algorithm could produce high-quality, diagnostic images at just 25% of the standard radiation dose.

These findings highlight the potential of AI-driven algorithms to enhance patient care by minimizing unnecessary radiation exposure, particularly in frequent head CT scans. This is a critical advancement, as reducing radiation-related risks is a growing concern, especially in younger patients with recurrent imaging needs. The study emphasizes the need for further exploration of dose-reduction techniques in medical imaging to improve safety and outcomes.

Article written by Medi imaging

25/09/2024

Source:

Medi imaging

https://www.medimaging.net/general-imaging/articles/294802556/ai-algorithm-reduces-unnecessary-radiation-exposure-in-traumatic-neuroradiological-ct-scans.html#:~:text=The%20algorithm%27s%20ability%20to%20maintain,CT%20use%20in%20neuroradiological%20emergencies.