Accelerating multidimensional NMR and MRI experiments using iterated maps

[Display omitted] •An iterated maps algorithm is applied to sparsely-sampled time domain data.•Used to reconstruct spectra from noisy 2D NMR and 3D MRI of solids data.•High quality results achieved with sparse sampling approaching theoretical minimum.•We use the QUEST sampling schedule and discuss i...

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Veröffentlicht in:	Journal of magnetic resonance (1997) Jg. 237; S. 100 - 109
Hauptverfasser:	Frey, Merideth A., Sethna, Zachary M., Manley, Gregory A., Sengupta, Suvrajit, Zilm, Kurt W., Loria, J. Patrick, Barrett, Sean E.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Inc 01.12.2013
Schlagworte:	Algorithms Amino Acids - chemistry Aminohydrolases - chemistry Image Processing, Computer-Assisted Iterative maps Magnetic resonance Magnetic resonance imaging Magnetic Resonance Imaging - methods Magnetic Resonance Imaging - statistics & numerical data Magnetic Resonance Spectroscopy - methods Magnetic Resonance Spectroscopy - statistics & numerical data Multi-dimensional nuclear magnetic resonance Noise Nuclear magnetic resonance Nuclear Magnetic Resonance, Biomolecular Projection Sampling Sparse sampling Three dimensional Iterative maps Magnetic resonance imaging Sparse sampling Multi-dimensional nuclear magnetic resonance
ISSN:	1090-7807, 1096-0856, 1096-0856
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Abstract	[Display omitted] •An iterated maps algorithm is applied to sparsely-sampled time domain data.•Used to reconstruct spectra from noisy 2D NMR and 3D MRI of solids data.•High quality results achieved with sparse sampling approaching theoretical minimum.•We use the QUEST sampling schedule and discuss its benefits for 2D NMR data.•FFT-based method is computationally fast, simple to implement, and robust. Techniques that accelerate data acquisition without sacrificing the advantages of fast Fourier transform (FFT) reconstruction could benefit a wide variety of magnetic resonance experiments. Here we discuss an approach for reconstructing multidimensional nuclear magnetic resonance (NMR) spectra and MR images from sparsely-sampled time domain data, by way of iterated maps. This method exploits the computational speed of the FFT algorithm and is done in a deterministic way, by reformulating any a priori knowledge or constraints into projections, and then iterating. In this paper we explain the motivation behind this approach, the formulation of the specific projections, the benefits of using a ‘QUasi-Even Sampling, plus jiTter’ (QUEST) sampling schedule, and various methods for handling noise. Applying the iterated maps method to real 2D NMR and 3D MRI of solids data, we show that it is flexible and robust enough to handle large data sets with significant noise and artifacts.
AbstractList	Techniques that accelerate data acquisition without sacrificing the advantages of fast Fourier transform (FFT) reconstruction could benefit a wide variety of magnetic resonance experiments. Here we discuss an approach for reconstructing multidimensional nuclear magnetic resonance (NMR) spectra and MR images from sparsely-sampled time domain data, by way of iterated maps. This method exploits the computational speed of the FFT algorithm and is done in a deterministic way, by reformulating any a priori knowledge or constraints into projections, and then iterating. In this paper we explain the motivation behind this approach, the formulation of the specific projections, the benefits of using a ‘QUasi-Even Sampling, plus jiTter’ (QUEST) sampling schedule, and various methods for handling noise. Applying the iterated maps method to real 2D NMR and 3D MRI of solids data, we show that it is flexible and robust enough to handle large data sets with significant noise and artifacts. Techniques that accelerate data acquisition without sacrificing the advantages of fast Fourier transform (FFT) reconstruction could benefit a wide variety of magnetic resonance experiments. Here we discuss an approach for reconstructing multidimensional nuclear magnetic resonance (NMR) spectra and MR images from sparsely-sampled time domain data, by way of iterated maps. This method exploits the computational speed of the FFT algorithm and is done in a deterministic way, by reformulating any a priori knowledge or constraints into projections, and then iterating. In this paper we explain the motivation behind this approach, the formulation of the specific projections, the benefits of using a aQUasi-Even Sampling, plus jiTtera (QUEST) sampling schedule, and various methods for handling noise. Applying the iterated maps method to real 2D NMR and 3D MRI of solids data, we show that it is flexible and robust enough to handle large data sets with significant noise and artifacts. Techniques that accelerate data acquisition without sacrificing the advantages of fast Fourier transform (FFT) reconstruction could benefit a wide variety of magnetic resonance experiments. Here we discuss an approach for reconstructing multidimensional nuclear magnetic resonance (NMR) spectra and MR images from sparsely-sampled time domain data, by way of iterated maps. This method exploits the computational speed of the FFT algorithm and is done in a deterministic way, by reformulating any a priori knowledge or constraints into projections, and then iterating. In this paper we explain the motivation behind this approach, the formulation of the specific projections, the benefits of using a 'QUasi-Even Sampling, plus jiTter' (QUEST) sampling schedule, and various methods for handling noise. Applying the iterated maps method to real 2D NMR and 3D MRI of solids data, we show that it is flexible and robust enough to handle large data sets with significant noise and artifacts.Techniques that accelerate data acquisition without sacrificing the advantages of fast Fourier transform (FFT) reconstruction could benefit a wide variety of magnetic resonance experiments. Here we discuss an approach for reconstructing multidimensional nuclear magnetic resonance (NMR) spectra and MR images from sparsely-sampled time domain data, by way of iterated maps. This method exploits the computational speed of the FFT algorithm and is done in a deterministic way, by reformulating any a priori knowledge or constraints into projections, and then iterating. In this paper we explain the motivation behind this approach, the formulation of the specific projections, the benefits of using a 'QUasi-Even Sampling, plus jiTter' (QUEST) sampling schedule, and various methods for handling noise. Applying the iterated maps method to real 2D NMR and 3D MRI of solids data, we show that it is flexible and robust enough to handle large data sets with significant noise and artifacts. [Display omitted] •An iterated maps algorithm is applied to sparsely-sampled time domain data.•Used to reconstruct spectra from noisy 2D NMR and 3D MRI of solids data.•High quality results achieved with sparse sampling approaching theoretical minimum.•We use the QUEST sampling schedule and discuss its benefits for 2D NMR data.•FFT-based method is computationally fast, simple to implement, and robust. Techniques that accelerate data acquisition without sacrificing the advantages of fast Fourier transform (FFT) reconstruction could benefit a wide variety of magnetic resonance experiments. Here we discuss an approach for reconstructing multidimensional nuclear magnetic resonance (NMR) spectra and MR images from sparsely-sampled time domain data, by way of iterated maps. This method exploits the computational speed of the FFT algorithm and is done in a deterministic way, by reformulating any a priori knowledge or constraints into projections, and then iterating. In this paper we explain the motivation behind this approach, the formulation of the specific projections, the benefits of using a ‘QUasi-Even Sampling, plus jiTter’ (QUEST) sampling schedule, and various methods for handling noise. Applying the iterated maps method to real 2D NMR and 3D MRI of solids data, we show that it is flexible and robust enough to handle large data sets with significant noise and artifacts.
Author	Loria, J. Patrick Barrett, Sean E. Zilm, Kurt W. Frey, Merideth A. Sengupta, Suvrajit Sethna, Zachary M. Manley, Gregory A.
AuthorAffiliation	c Department of Chemistry, Yale University, New Haven, CT 06511 a Department of Physics, Yale University, New Haven, CT 06511 b Department of Physics, Princeton University, Princeton, NJ 08540 d Department of Chemistry and Department of Molecular Biophysics and Biochemistry, Yale University, New Haven CT 06511
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Keywords	Iterative maps Magnetic resonance imaging Sparse sampling Multi-dimensional nuclear magnetic resonance
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Snippet	[Display omitted] •An iterated maps algorithm is applied to sparsely-sampled time domain data.•Used to reconstruct spectra from noisy 2D NMR and 3D MRI of... Techniques that accelerate data acquisition without sacrificing the advantages of fast Fourier transform (FFT) reconstruction could benefit a wide variety of...
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SubjectTerms	Algorithms Amino Acids - chemistry Aminohydrolases - chemistry Image Processing, Computer-Assisted Iterative maps Magnetic resonance Magnetic resonance imaging Magnetic Resonance Imaging - methods Magnetic Resonance Imaging - statistics & numerical data Magnetic Resonance Spectroscopy - methods Magnetic Resonance Spectroscopy - statistics & numerical data Multi-dimensional nuclear magnetic resonance Noise Nuclear magnetic resonance Nuclear Magnetic Resonance, Biomolecular Projection Sampling Sparse sampling Three dimensional
Title	Accelerating multidimensional NMR and MRI experiments using iterated maps
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