Constrained magnetic resonance image reconstruction from incomplete frequency measurements
In this thesis, we develop constrained reconstructions methods to reconstruct high quality MR images from down-sampled k-space data by exploiting sparsity of MR images under the theory of Compressed Sensing. In the first work, we present an orthonormal-expansion L1 optimization technique to improve...
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sg-ntu-dr.10356-621042023-07-04T17:15:11Z Constrained magnetic resonance image reconstruction from incomplete frequency measurements Deng, Jun Lu Wenmiao Tan Yap Peng School of Electrical and Electronic Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision In this thesis, we develop constrained reconstructions methods to reconstruct high quality MR images from down-sampled k-space data by exploiting sparsity of MR images under the theory of Compressed Sensing. In the first work, we present an orthonormal-expansion L1 optimization technique to improve computational efficiency of existing methods without sacrificing the reconstruction accuracy. In order to achieve more accurate reconstructions especially in high under-sampling ratios, a novel method for motion-compensated reference-driven MR image reconstruction is presented in the second work with two sources of constraints: sparsity and prior information from a reference image. In the third work, we focus on reconstructions of MR images that have metallic implants. In order to reduce scan time incurred to fully correct metal artefacts and meanwhile improve SNR of results brought by the state-of-the-art methods, we propose a projection on convex set reconstruction technique to combine CS denoising and parallel MRI. DOCTOR OF PHILOSOPHY (EEE) 2015-01-21T02:57:36Z 2015-01-21T02:57:36Z 2014 2014 Thesis Deng, J. (2014). Constrained magnetic resonance image reconstruction from incomplete frequency measurements. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/62104 10.32657/10356/62104 en 152 p. application/pdf |
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DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Deng, Jun Constrained magnetic resonance image reconstruction from incomplete frequency measurements |
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In this thesis, we develop constrained reconstructions methods to reconstruct high quality MR images from down-sampled k-space data by exploiting sparsity of MR images under the theory of Compressed Sensing. In the first work, we present an orthonormal-expansion L1 optimization technique to improve computational efficiency of existing methods without sacrificing the reconstruction accuracy. In order to achieve more accurate reconstructions especially in high under-sampling ratios, a novel method for motion-compensated reference-driven MR image reconstruction is presented in the second work with two sources of constraints: sparsity and prior information from a reference image. In the third work, we focus on reconstructions of MR images that have metallic implants. In order to reduce scan time incurred to fully correct metal artefacts and meanwhile improve SNR of results brought by the state-of-the-art methods, we propose a projection on convex set reconstruction technique to combine CS denoising and parallel MRI. |
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Lu Wenmiao |
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Lu Wenmiao Deng, Jun |
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Theses and Dissertations |
author |
Deng, Jun |
author_sort |
Deng, Jun |
title |
Constrained magnetic resonance image reconstruction from incomplete frequency measurements |
title_short |
Constrained magnetic resonance image reconstruction from incomplete frequency measurements |
title_full |
Constrained magnetic resonance image reconstruction from incomplete frequency measurements |
title_fullStr |
Constrained magnetic resonance image reconstruction from incomplete frequency measurements |
title_full_unstemmed |
Constrained magnetic resonance image reconstruction from incomplete frequency measurements |
title_sort |
constrained magnetic resonance image reconstruction from incomplete frequency measurements |
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2015 |
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https://hdl.handle.net/10356/62104 |
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1772827760376414208 |