Advances in magnetic resonance imaging near metal

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It is estimated that 2.4% of the US population has an artificial hip or knee as of 2010, and the prevalence of metallic implants continues to grow. Current imaging evaluation of complications near implants relies on X-Ray, which has limited contrast in soft tissues and thus limited sensitivity for early disease. Magnetic Resonance Imaging (MRI) is a safe medical imaging modality known for its excellent soft tissue contrast, which could be a useful tool for accurate, early and non-invasive assessment of complications. However, severe magnetic field variations induced by metal often render conventional MRI techniques non-diagnostic near the implants. Multi-spectral imaging (MSI) techniques resolve metal-induced field perturbations, but they suffer from long scan times that delay their widespread clinical adoption, and residual frequency-encoding artifacts that cause resolution loss close to the metal. This dissertation focuses on techniques to improve MRI near metal in terms of scan efficiency, artifact correction and delineation of implants. First, a signal model of MSI was introduced to compactly represent the signal distribution in the spectral dimension and enable accelerations of MSI scans. The model-based reconstruction was demonstrated to provide 3-fold acceleration beyond conventional acceleration techniques including parallel imaging and partial Fourier reconstruction. Next, a deep-learning-based reconstruction was presented to reduce the reconstruction times of optimization-based reconstruction and improve the reconstructed image quality of accelerated imaging near metal. Then, the frequency-encoding artifacts induced by metal, including signal hyper-intensities, signal oscillations and resolution loss, were analyzed and alternating-gradient MSI acquisitions were introduced to correct these artifacts. Finally, a method for susceptibility mapping inside signal voids was presented to delineate the geometry and material of metallic implants.


Type of resource text
Form electronic resource; remote; computer; online resource
Extent 1 online resource.
Place California
Place [Stanford, California]
Publisher [Stanford University]
Copyright date 2018; ©2018
Publication date 2018; 2018
Issuance monographic
Language English


Author Shi, Xinwei
Degree supervisor Hargreaves, Brian Andrew
Thesis advisor Hargreaves, Brian Andrew
Thesis advisor Nishimura, Dwight George
Thesis advisor Pauly, John (John M.)
Degree committee member Nishimura, Dwight George
Degree committee member Pauly, John (John M.)
Associated with Stanford University, Department of Electrical Engineering.


Genre Theses
Genre Text

Bibliographic information

Statement of responsibility Xinwei Shi.
Note Submitted to the Department of Electrical Engineering.
Thesis Thesis Ph.D. Stanford University 2018.
Location electronic resource

Access conditions

© 2018 by Xinwei Shi
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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