Elmira Vafay Eslahi and Amirali Baniasadi, University of Victoria, Canada

Magnetic resonance imaging (MRI) is one of the best imaging techniques that produce high quality images of objects. The long scan time is one of the biggest challenges in MRI acquisitions. To address this challenge, many researchers have aimed at finding methods to speed up the process. Faster MRI can reduce patient discomfort and motion artifacts. Many reconstruction methods are used in this matter, like deep learning-based MRI reconstruction, parallel MRI, and compressive sensing. Among these techniques, the convolutional neural network (CNN) generates high-quality images with faster scan and reconstruction procedures compared to the other techniques. The Inception module proposed by Google inspires the algorithm of this study for MRI reconstruction. In other words, we introduce a new MRI U-Net modification by using the Inception module. Our method is more flexible and robust compared to the standard U-Net.

Magnetic Resonance Imaging, Convolutional Neural Network, Fast Fourier Transform, Inception Module, U-Net, Deep Learning, Machine Learning, Low Frequency, Mean Square Error, Structural Similarity Index Measure & Peak Signal-to-Noise Ratio.