Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Entire DC Network
Fast Magnetic Resonance Image Reconstruction With Deep Learning Using An Efficientnet Encoder, Tahsin Rahman
Fast Magnetic Resonance Image Reconstruction With Deep Learning Using An Efficientnet Encoder, Tahsin Rahman
Open Access Theses & Dissertations
This thesis aims to develop an efficient, deep network based method for Magnetic Resonance Imaging (MRI) acceleration through undersampled MR image reconstruction. Deep Neural Networks, particularly Deep Convolutional Networks, have been demonstrated to be highly effective in a wide variety of computer vision tasks, including MRI reconstruction. However, modern highly efficient encoder structures, such as the EfficientNet can potentially reduce reconstruction times further while improving reconstruction quality. To that end, we have developed a multi-channel U-Net MRI reconstruction network which uses an EfficientNet encoder and a custom asymmetric. The network was trained and tested using 5x undersampled multi-channel brain MR …
The Theory And Design Of Class E Power Amplifiers For Impulse Excitation In Nuclear Magnetic Resonance, Owen D. Riemer
The Theory And Design Of Class E Power Amplifiers For Impulse Excitation In Nuclear Magnetic Resonance, Owen D. Riemer
Browse all Theses and Dissertations
A new method for analyzing the effectiveness of NMR impulse power amplifiers was developed using a classical linear systems approach to NMR. The method demonstrates a way to compare NMR power amplifiers and outputs. Thermodynamic calculations and the harmonic content of NMR amplifiers is presented to provide a complete description of the NMR power amplifier design problem. A design procedure for class E NMR power amplifiers with a pi-impedance matching network is outlined for matching the amplifier to the transmitter coil. The thesis concludes with the presentation of a 53 MHz power amplifier developed with the procedure. The complete amplifier …