Development of A Deep Neural Network for Synthesis of Non-Contrast Cranial T1-Weighted Magnetic Resonance Imaging

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Authors

Agueda M. Taylor, Oakland University William Beaumont School of Medicine Medical Student
Evan Porter, Beaumont Health
Thomas Guerrero, Beaumont Health

Description

INTRODUCTION
Although 122 out of 1000 people in the US have MRI’s done each year, there are over 4 million with contraindications that subsequently forgo the diagnostic benefits. Studies in recent years have implemented artificial intelligence (AI) algorithms such as deep neural networks (DNN) for production of synthetic medical imaging. The goals of this project are to develop a DNN, specifically a Generative Adversarial Network (GAN) that will predict synthetic Cranial T1 Weighted MRI from non-contrast CT, and to evaluate the model quality.

Publication Date

5-2-2022

Keywords

MRI, neurons

Disciplines

Oncology | Radiation Medicine

Recommended Citation

Taylor AM, Porter E, Guerrero T. Development of a deep neural network for synthesis of non-contrast cranial T1-weighted magnetic resonance imaging. Poster presented at: Oakland University William Beaumont School of Medicine Embark Capstone Colloquium; 2022 May 2; Rochester Hills, MI.

Comments

The Embark Capstone Colloquium at the Oakland University William Beaumont School of Medicine, Rochester Hills, MI, May 2, 2022.