Open Access
MR spectroscopic image reconstruction using structural information from anatomical MR images
Thomas S. Denney,Stanley J. Reeves +1 more
- 01 Jan 2003
- pp 131-140
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TL;DR: In this article, the authors use a high-resolution MR scout image to obtain edge locations in the sample imaged with MRSI, where MR discontinuities represent boundaries between different tissue types.
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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.
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Citations
Bayesian image reconstruction from Fourier-domain samples using prior edge information: convergence and parameter sensitivity
TL;DR: Two techniques for updating the image based on fixed edge variables one based on iterated conditional modes (ICM) and the other based on Jacobi iteration are proposed, which are more computationally efficient but does not always converge.
1
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