Dear readers,
CT has been definitely acknowledged as one of the most accurate and reliable imaging techniques for screening procedures, diagnostic process, disease staging and treatment planning, as well as representing the gold standard for the clinical assessment of a wide range of conditions and pathologies.
The clinical utility of CT is directly related to image quality, while high-quality and effective imaging is dependent on radiation dose, which in turn is detrimental for patients. A balance between an optimal imaging performance and the reduction of radiation exposure can be achieved through the application of the Artificial Intelligence (AI) to CT.
As reported in detail in this month’s Focus on by Prof. Laghi’s team, an increasing number of studies and the recent availability of AI-based software packages for CT reconstruction and post-processing phases demonstrate the significant contribution of AI to improving diagnostic accuracy at the lowest radiation dose, without compromising the patients’ safety.
The two scientific articles selected for this Newsletter illustrate the advantages of AI applied to CT in general and to abdominal CT in particular, with a special emphasis on the Deep Learning Image Reconstruction technique.
As described by Laghi’s team, the strength of AI-based CT in clinical practice is its very high sensitivity and accuracy in detecting lesions and, consequently, in improving the diagnostic process: the three clinical cases from the MDCT.net archive reported below provide some examples of conditions where the integration of AI in CT may provide a better diagnostic performance.
As usual, suggestions for topics to be covered in future issues of this newsletter are welcome: please send your proposals to us at editorial@mdct.net.
From the MDCT.net team
