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Full-Text Articles in Medical Specialties
Development Of A Deep Neural Network For Synthesis Of Non-Contrast Cranial T1-Weighted Magnetic Resonance Imaging, Agueda M. Taylor, Evan Porter, Thomas Guerrero
Development Of A Deep Neural Network For Synthesis Of Non-Contrast Cranial T1-Weighted Magnetic Resonance Imaging, Agueda M. Taylor, Evan Porter, Thomas Guerrero
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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.
Pre And Postnatal Magnetic Resonance Imaging Of Ventriculomegaly, Ryan Kelsch, Megan Moore, Anant Krishnan
Pre And Postnatal Magnetic Resonance Imaging Of Ventriculomegaly, Ryan Kelsch, Megan Moore, Anant Krishnan
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Purpose or Case Report: The purpose of this research was to analyze our institution’s large database of fetal magnetic resonance (MR) for cases of ventriculomegaly in order to understand trends in pre and postnatal MR.
Methods & Materials: In this retrospective study, 316 individual fetal MR exams from the past 10 years at our institution were reviewed. Of those, 86 patients had fetal MRs with findings of either ventriculomegaly or an ordering indication of ventriculomegaly. Our inclusion criteria of a diagnosis of ventriculomegaly (lateral ventricle measured at the trigone on coronal imaging of over 10mm) on fetal MR with a …