Paper
1 July 2003 MR spectroscopic image reconstruction using structural information from anatomical MR images
Author Affiliations +
Proceedings Volume 5016, Computational Imaging; (2003) https://doi.org/10.1117/12.479701
Event: Electronic Imaging 2003, 2003, Santa Clara, CA, United States
Abstract
Spectroscopic imaging (SI) techniques combine the ability of NMR spectroscopy to identify and measure biochemical constituents with the ability of MR imaging to localize NMR signals. The basic imaging technique acquires a set of spatial-frequency-domain samples on a regular grid and takes an inverse Fourier transform of the acquired data to obtain the spatial-domain image. Unfortunately, the time required to gather the data while maintaining an adequate signal-to-noise ratio (SNR) limits the number of spatial-frequency-domain samples that can be acquired. In this paper, we use a high-resolution MR scout image to obtain edge locations in the sample imaged with MRSI. MRI discontinuities represent boundaries between different tissue types, and these discontinuities are likely to appear in the spectroscopic image as well. We propose a new model that encourages edge formation in the MRSI image reconstruction wherever MR image edges occur. A major difference between our model and previous methods is that an edge found in the MR image need not be confirmed by the data; smoothing is reduced across the edge if either the MR image or the MRSI data suggests an edge. Simulations and results on in vivo MRSI data are presented that demonstrate the effectiveness of the method.
© (2003) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Thomas S. Denney Jr. and Stanley J. Reeves "MR spectroscopic image reconstruction using structural information from anatomical MR images", Proc. SPIE 5016, Computational Imaging, (1 July 2003); https://doi.org/10.1117/12.479701
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KEYWORDS
Magnetic resonance imaging

Data modeling

Imaging spectroscopy

Spectroscopy

Reconstruction algorithms

Image restoration

Signal to noise ratio

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