Ricerc@Sapienza

Background: In recent years Artificial intelligence (AI) has represented a very innovative research field with applications in several of medical branches, in particular in medical imaging. In fact, AI is particularly prone to be applied in medical imaging due to the large amount of data generated through image acquisitions available in digital format, that enable the AI process of ¿learning¿ from huge dataset. Among AI methods, deep learning (DL) has been recently implemented for Magnetic Resonance Imaging (MRI) protocols applied on different anatomical regions with interesting feed-backs in terms of image quality and reduced acquisition time. Up to now, a few study assessed the potential benefit of DL applied on upper-abdomen MRI in clinical routine.
Aims:
1. To evaluate image quality of the deep learning-based upper abdomen MRI protocol compared to standard non-deep learning protocol.
2. To compare acquisition time between deep learning-based MRI protocol and standard non-deep learning protocol.
Methods: Thirty patients who will undergo upper abdomen MRI we will be prospectively included. Each patient will perform a dedicated MRI protocol with the same scanner (1.5T GE Signa Voyager, GE Healthcare, Waukesha, WI) including axial T2 single-shot fast spin echo (SSFSE) sequences and axial Diffusion Weighted Imaging (DWI) acquired with deep learning (DL) method (AIR¿ Recon DL, GE Healthcare, Waukesha, WI) and non-DL with standard protocol. Acquisition time will be recorded for each sequence. Each acquisition will be anonymized and two readers independently will assess image quality by quantifying signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) drawing circular region of interest in the liver parenchyma, in the image background and in the gall-bladder. Moreover, visual image quality including sharpness, contrast, artifacts and overall quality will be performed by using a five-point Likert scale.