Paper
20 March 2015 Multi-contrast magnetic resonance image reconstruction
Meng Liu, Yunmei Chen, Hao Zhang, Feng Huang
Author Affiliations +
Abstract
In clinical exams, multi-contrast images from conventional MRI are scanned with the same field of view (FOV) for complementary diagnostic information, such as proton density- (PD-), T1- and T2-weighted images. Their sharable information can be utilized for more robust and accurate image reconstruction. In this work, we propose a novel model and an efficient algorithm for joint image reconstruction and coil sensitivity estimation in multi-contrast partially parallel imaging (PPI) in MRI. Our algorithm restores the multi-contrast images by minimizing an energy function consisting of an L2-norm fidelity term to reduce construction errors caused by motion, a regularization term of underlying images to preserve common anatomical features by using vectorial total variation (VTV) regularizer, and updating sensitivity maps by Tikhonov smoothness based on their physical property. We present the numerical results including T1- and T2-weighted MR images recovered from partially scanned k-space data and provide the comparisons between our results and those obtained from the related existing works. Our numerical results indicate that the proposed method using vectorial TV and penalties on sensitivities can be made promising and widely used for multi-contrast multi-channel MR image reconstruction.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Meng Liu, Yunmei Chen, Hao Zhang, and Feng Huang "Multi-contrast magnetic resonance image reconstruction", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94130C (20 March 2015); https://doi.org/10.1117/12.2082136
Lens.org Logo
CITATIONS
Cited by 2 scholarly publications.
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Image restoration

Magnetic resonance imaging

Reconstruction algorithms

Image quality

Data acquisition

Image analysis

Magnetism

Back to Top