Artificial Intelligence Integration into the Magnetic Resonance System

Abstract

The application of artificial intelligence (AI) in cardiac magnetic resonance (CMR) is rapidly growing. The role of AI in CMR, similar to what happened in cardiovascular computed tomography angiography, is aiming to range from images acquisition to evaluation of prognosis (Frick et al. J Magn Reson Imaging. 34:457–467, 2011; Guaricci et al. Int J Cardiol. 261:223–227, 2018). In CMR image acquisition, AI algorithms allow speeding up the process of acquisition, facilitating the scan prescription and improving the quality image while maintaining a short scan time (Nitta et al. MAGMA. 26:451–61, 2013; Blansit et al. Radiol Artif Intell. 2019;1:e180069).The future application of AI in MR systems will play a key role for reducing the major disadvantage of CMR, which is the tradeoff between image quality and scan time. It is not infrequent to have long scan time in clinical practice trying to obtain good image quality; therefore, the application of several algorithms of AI developed by several vendors (van der Velde et al. Eur Radiol. 2020; Moenninghoff et al. Acad Radiol. 20:721–730, 2013) can solve this issue providing images of good quality in a reasonable acquisition time.In this chapter, we are going to explore the advantages of AI integrated in MR systems showing the advantages and the updated technical availability in clinical practice.KeywordsMagnetic resonanceArtificial intelligenceWorkflowImage qualityReconstructionAcquisition