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Presenter: Elmira Vafay Eslahi
Supervisor:

Date: Wed, August 17, 2022
Time: 11:00:00 - 12:00:00
Place: via Zoom - please see link below

ABSTRACT

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https://uvic.zoom.us/j/82034683840?pwd=cTVBOTdSV0VlYXlVczJwUU5JNjJtUT09

Meeting ID: 820 3468 3840

Password: 385942

ABSTRACT

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. One method to speed up MRI scans is skipping some signals in the k-space. Although the incomplete k-space or sub-sampling causes undersampling artifacts due to missing signals, reconstruction techniques can solve the problem by recovering the missing data. 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. However, CNN architecture has been an area under study in image reconstruction and needs more investigation. In this study, we propose a new deep learning algorithm for MRI reconstructions. The Inception module proposed by Google inspires this algorithm.