Presented by: Dr. Roberto Souza of the University of Calgary
Date: Nov 24, 2020, 12-1pm (mountain time)
Register at: Registration closes Nov 23 or once full.

Magnetic resonance imaging (MRI) is a diagnostic imaging modality that provides excellent image contrast that leads to diagnoses of several conditions, such as tumours and strokes. The major drawback of MRI exams is their long acquisition time. An MRI exam usually lasts between 30 to 60 minutes per patient. These long acquisition times, combined with the high costs (~$3,000,000) and space requirements to install a scanner and periodic maintenance, cause MRI exams to be expensive and create wait times that make it less accessible. An MRI exam’s average price is over $700, and nearly two million MRI exams are done in Canada yearly, making it a billion-dollar industry. In this talk, Dr. Souza will cover the basics of deep learning methods for image reconstruction. He will present a deep learning model called the W-net that can be used to make MRI exams up to 20 times faster. Dr. Souza will also briefly illustrate the use of the W-net to improve other imaging applications, such as JPEG image decompression and low-dose computed tomography reconstruction.