Enhancing Pulmonary MRI Imaging through an Ann-Based Approach

Despite improvements in the morphological quality of lung images in recent years, using ultra-short echo time sequences [1], the low signal-to-noise ratio caused by the low proton density of lung tissue (~0.1 g/ml) and cardiac and respiratory motions that cause artifacts have been the cause of limitations in magnetic resonance imaging (MRI) [2]. Spatial resolution and diagnostic quality are then reduced. This is mainly evident in emergency situations of pulmonary embolism or in chronic bronchopulmonary diseases. To improve the BOLD signal, this study proposes a method using artificial intelligence techniques, particularly artificial neural networks, as a tool for modeling these disruptive factors, thus improving the reliability of lung MRI images. The proposed ANN system supports disruptive input variables in correspondence with the output variables that express image quality and reliability. Input variables are taken from clinical data. This tool is intended to be considered as a diagnostic aid in thoracic imaging.