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Abstract #4628

Combined Super Resolution and Partial Fourier Reconstruction for 3D Magnetic Resonance Imaging with varying Partial Fourier settings

Punith B Venkategowda1,2, Keerthi Prabhu M1, Asha K Kumaraswamy1, Bhairav Mehta1, Vignesh Anandan1, Majd Helo3,4, Thomas Benkert3, Seung Su Yoon3, Till Hülnhagen3, and Dominik Nickel3
1Magnetic Resonance, Siemens Healthcare Pvt. Ltd., Bengaluru, India, 2International Institute of Information Technology, Bengaluru, India, 3Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany, 4Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University of Tuebingen, Tuebingen, Germany

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Image Reconstruction, Super Resolution, Partial Fourier, Reconstruction

Motivation: Current approaches using neural networks for combined super-resolution and partial Fourier (PF) reconstruction require separate models for each PF factor and direction resulting in increased training and maintenance efforts.

Goal(s): Develop a single deep learning model capable of handling any PF range and direction, eliminating the need for multiple networks.

Approach: Input images for training are zero-padded in the Fourier domain to simulate randomly varying PF from 75% to 100% in all three acquisition directions.

Results: Compared to zero-filling and models trained for specific PF factors, the proposed approach shows improved sharpness, reduced ringing artifacts, and enhanced quantitative metrics.

Impact: Our unified network performs super-resolution and PF reconstruction across a large range of PF factors applied in arbitrary directions. This removes the need for multiple dedicated networks trained for specific PF factors and simplifies pre- and post-processing operations.

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