Clinical Research Directory

Defining Dosimetric Reference Levels in Computed Tomography Spectral Scanning

Spectral computed tomography or dual-energy CT imaging can overcome the limitations of conventional CT in differentiating between two materials with equivalent total attenuation. It can generate several types of images, such as virtual monochromatic images, which improve the contrast-to-noise ratio for low energy levels and reduce artifacts for high energy levels. It also allows for quantitative image analysis and thus better characterization of lesions and tissues through material mapping (e.g., iodinated contrast agent mapping). This technique is increasingly used in routine clinical practice thanks to improvements in image flow management and technological advances. It also involves exposing patients to ionizing radiation, as with conventional CT but, unlike conventional CT scans, for which dosimetric reference levels (RLs) are defined for the most common examinations in France (RL decree dated 2019), there are currently no dosimetric reference levels for examinations performed using this technique. Yet RLs are an important and effective tools for optimizing patient exposure to ionizing radiation. Several articles were published between 2012 and 2017 when the first dual-energy scanners arrived in clinics. However, the results presented in these studies are now far removed from recent practices, as they do not take into account the latest technological developments used in dual-energy scanners, which reduce X-ray doses. The main objective of the study is to define dosimetric reference levels for the most commonly performed spectral computed tomography examinations in France.

Exploring the Application Efficacy of Artificial Intelligence (AI) Diagnostic Tools in Medical Imaging (MI) of Respiratory(R) Infectious (I) Disease (D)

The early identification and severe warning of acute respiratory infectious diseases are of paramount importance. Utilizing effective means to make correct diagnoses of the source of infection at an early stage is the premise of all effective measures. AI-MID is a research initiative that uses artificial intelligence tools to assist in the clinical medical imaging diagnosis of respiratory diseases, aiming to reduce the time doctors spend reviewing images, increase work efficiency, and enhance the sensitivity and specificity of pneumonia detection, thereby improving the detection rate of pneumonia at the grassroots level. This approach facilitates precise prevention, accurate diagnosis, and precise treatment.