A subspace approach to high‐resolution spectroscopic imaging. Issue 4 (4th February 2014)

• Record Type: Journal Article
• Authors: Lam, Fan; Liang, Zhi‐Pei
• Is Part Of: Magnetic resonance in medicine. Volume 71:Issue 4(2014:Apr.)
• Page Start: 1349
• Page End: 1357
• Publication Date: 2014-02-04
• Subjects: Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.25168
• Languages: English
• ISSNs: 0740-3194

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <sec id="mrm25168-sec-0001" sec-type="section"> <title>Purpose</title> <p>To accelerate spectroscopic imaging using sparse sampling of <alternatives><inline-graphic mimetype="image" xlink:href="ark:/27927/pgg4t0ftwwp" xlink:type="simple" xmlns:xlink="http://www.w3.org/1999/xlink" /><mml:math display="inline" altimg="urn:x-wiley:07403194:media:mrm25168:mrm25168-math-0001" overflow="scroll" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>(</mml:mo><mml:mi mathvariant="normal">k</mml:mi><mml:mo>, </mml:mo><mml:mi mathvariant="normal">t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives>‐space and subspace (or low‐rank) modeling to enable high‐resolution metabolic imaging with good signal‐to‐noise ratio.</p> </sec> <sec id="mrm25168-sec-0002" sec-type="section"> <title>Methods</title> <p>The proposed method, called SPectroscopic Imaging by exploiting spatiospectral CorrElation, exploits a unique property known as partial separability of spectroscopic signals. This property indicates that high‐dimensional spectroscopic signals reside in a very low‐dimensional subspace and enables special data acquisition and image reconstruction strategies to be used to obtain high‐resolution spatiospectral distributions with good signal‐to‐noise ratio. More specifically, a hybrid chemical shift imaging/echo‐planar spectroscopic imaging pulse sequence is proposed for sparse sampling of <alternatives><inline-graphic mimetype="image" xlink:href="ark:/27927/pgg4t0ftwx7" xlink:type="simple" xmlns:xlink="http://www.w3.org/1999/xlink" /><mml:math display="inline" altimg="urn:x-wiley:07403194:media:mrm25168:mrm25168-math-0002" overflow="scroll" xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>(</mml:mo><mml:mi mathvariant="normal">k</mml:mi><mml:mo>, </mml:mo><mml:mi mathvariant="normal">t</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives>‐space, and a low‐rank model‐based algorithm is proposed for subspace estimation and image reconstruction from sparse data with the capability to incorporate prior information and field inhomogeneity correction.</p> </sec> <sec id="mrm25168-sec-0003" sec-type="section"> <title>Results</title> <p>The performance of the proposed method has been evaluated using both computer simulations and phantom studies, which produced very encouraging results. For two‐dimensional spectroscopic imaging experiments on a metabolite phantom, a factor of 10 acceleration was achieved with a minimal loss in signal‐to‐noise ratio compared to the long chemical shift imaging experiments and with a significant gain in signal‐to‐noise ratio compared to the accelerated echo‐planar spectroscopic imaging experiments.</p> </sec> <sec id="mrm25168-sec-0004" sec-type="section"> <title>Conclusion</title> <p>The proposed method, SPectroscopic Imaging by exploiting spatiospectral CorrElation, is able to significantly accelerate spectroscopic imaging experiments, making high‐resolution metabolic imaging possible. <bold>Magn Reson Med 71:1349–1357, 2014. © 2014 Wiley Periodicals, Inc</bold>.</p> </sec> </abstract>