Tianrui Luo

Tianrui Luo
University of Michigan | U-M · Department of Biomedical Engineering

Doctor of Philosophy

About

6
Publications
654
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34
Citations

Publications

Publications (6)
Article
Optimizing k-space sampling trajectories is a promising yet challenging topic for fast magnetic resonance imaging (MRI). This work proposes to optimize a reconstruction method and sampling trajectories jointly concerning image reconstruction quality in a supervised learning manner. We parameterize trajectories with quadratic B-spline kernels to red...
Article
This paper proposes a new method for joint design of radiofrequency (RF) and gradient waveforms in Magnetic Resonance Imaging (MRI), and applies it to the design of 3D spatially tailored saturation and inversion pulses. The joint design of both waveforms is characterized by the ODE Bloch equations, to which there is no known direct solution. Existi...
Preprint
Full-text available
Optimizing k-space sampling trajectories is a challenging topic for fast magnetic resonance imaging (MRI). This work proposes to optimize a reconstruction algorithm and sampling trajectories jointly concerning image reconstruction quality. We parameterize trajectories with quadratic B-spline kernels to reduce the number of parameters and enable mul...
Thesis
Excitation pulse design and image reconstruction are two important topics in MR research for enabling faster imaging. On the pulse design side, selective excitations that confine signals to be within a small region-of-interest (ROI) instead of the full imaging field-of-view (FOV) can be used to reduce sampling density in the k-space, which is a dir...
Preprint
Full-text available
**https://github.com/tianrluo/AutoDiffPulses** **https://arxiv.org/abs/2008.10594** This paper proposes a new method for joint design of radiofrequency (RF) and gradient waveforms in Magnetic Resonance Imaging (MRI), and applies it to the design of 3D spatially tailored saturation and inversion pulses. The joint design of both waveforms is charac...
Article
Full-text available
Purpose GRAPPA is a popular reconstruction method for Cartesian parallel imaging, but is not easily extended to non‐Cartesian sampling. We introduce a general and practical GRAPPA algorithm for arbitrary non‐Cartesian imaging. Methods We formulate a general GRAPPA reconstruction by associating a unique kernel with each unsampled k‐space location w...

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