Bayesian $k$-Space–Time Reconstruction of MR Spectroscopic Imaging for Enhanced Resolution

Liang

2014

120

179

Purpose To accelerate spectroscopic imaging using sparse sampling of (k, t)-space and subspace (or low-rank) modeling to enable high-resolution metabolic imaging with good signal-to-noise ratio (SNR). Methods The proposed method, called SPICE (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 SNR. More specifically, a hybrid CSI/EPSI pulse sequence is proposed for sparse sampling of (k, t)-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. Results The performance of SPICE has been evaluated using both computer simulations and phantom studies, which produced very encouraging results. For 2D spectroscopic imaging experiments on a metabolite phantom, a factor of 10 acceleration was achieved with a minimal loss in SNR compared to the long CSI experiments and with a significant gain in SNR compared to the accelerated EPSI experiments. Conclusion SPICE is able to significantly accelerate spectroscopic imaging experiments, making high-resolution metabolic imaging possible.

“…Then, the spatial coefficients can be determined as

\overset{truê}{boldC} = \arg \min_{boldC} | | {bolds}_{2} - ⊓ {scriptF}_{B} {C Φ} | |_{2}^{2} + λ Ψ true(C, Φ true),

where the

| | \cdot | |_{2}^{2}

term measures the data consistency of a reconstruction, and

Ψ (\cdot)

is a regularization function with regularization parameter λ . There are many choices for

Ψ (\cdot)

to incorporate prior information about

\tilde{ρ} true(r, t true)

ρ (r, f)

[including both quadratic and sparsity‐promoting penalties ]. In this article, we focus on demonstrating the concept and potential of SPICE and use

Ψ true(C, Φ true) = true| | W D C Φ true| |_{F}^{2}

, where D is a finite difference operator and W contains edge weights derived from a high‐resolution anatomical image .…”

Section: Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A subspace approach to high‐resolution spectroscopic imaging

Liang

2014

120

179

“…Significant efforts have been made in the last several decades to achieve fast, high-resolution MR spectroscopic imaging (MRSI), through the development of fast sequences (1)(2)(3)(4)(5)(6)(7)(8)(9)(10) and advanced image reconstruction methods (11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25). SPICE (SPectroscopic Imaging by exploiting spatiospectral CorrElation) is a relatively new approach that we recently proposed to achieve high-resolution MRSI with good signal-to-noise ratio (SNR) and speed (26).…”

Section: Introductionmentioning

confidence: 99%

High‐resolution ¹H‐MRSI of the brain using SPICE: Data acquisition and image reconstruction

Clifford

et al. 2015

144

Purpose To develop data acquisition and image reconstruction methods to enable high-resolution 1H MR spectroscopic imaging (MRSI) of the brain, using the recently proposed subspace-based spectroscopic imaging framework called SPICE (SPectroscopic Imaging by exploiting spatiospectral CorrElation). Theory and Methods SPICE is characterized by the use of a subspace model for both data acquisition and image reconstruction. For data acquisition, we propose a novel spatiospectral encoding scheme that provides hybrid data sets for determining the subspace structure and for image reconstruction using the subspace model. More specifically, we use a hybrid chemical shift imaging (CSI)/echo-planar spectroscopic imaging (EPSI) sequence for two-dimensional (2D) MRSI and a dual-density, dual-speed EPSI sequence for three-dimensional (3D) MRSI. For image reconstruction, we propose a method that can determine the subspace structure and the high-resolution spatiospectral reconstruction from the hybrid data sets generated by the proposed sequences, incorporating field inhomogeneity correction and edge-preserving regularization. Results Phantom and in vivo brain experiments were performed to evaluate the performance of the proposed method. For 2D MRSI experiments, SPICE is able to produce high SNR spatiospectral distributions with an approximately 3 mm nominal in-plane resolution from a 10-min acquisition. For 3D MRSI experiments, SPICE is able to achieve an approximately 3 mm in-plane and 4 mm through-plane resolution in about 25 min. Conclusion Special data acquisition and reconstruction methods have been developed for high-resolution 1H-MRSI of the brain using SPICE. Using these methods, SPICE is able to produce spatiospectral distributions of 1H metabolites in the brain with high spatial resolution, while maintaining a good SNR. These capabilities should prove useful for practical applications of SPICE.

Section: Introductionmentioning

confidence: 99%

High‐resolution ¹H‐MRSI of the brain using SPICE: Data acquisition and image reconstruction

Clifford

et al. 2016

Purpose-To develop data acquisition and image reconstruction methods to enable highresolution 1 H MR spectroscopic imaging (MRSI) of the brain, using the recently proposed subspace-based spectroscopic imaging framework called SPICE (SPectroscopic Imaging by exploiting spatiospectral CorrElation).Theory and Methods-SPICE is characterized by the use of a subspace model for both data acquisition and image reconstruction. For data acquisition, we propose a novel spatiospectral encoding scheme that provides hybrid data sets for determining the subspace structure and for image reconstruction using the subspace model. More specifically, we use a hybrid chemical shift imaging (CSI)/echo-planar spectroscopic imaging (EPSI) sequence for two-dimensional (2D) MRSI and a dual-density, dual-speed EPSI sequence for three-dimensional (3D) MRSI. For image reconstruction, we propose a method that can determine the subspace structure and the highresolution spatiospectral reconstruction from the hybrid data sets generated by the proposed sequences, incorporating field inhomogeneity correction and edge-preserving regularization.Results-Phantom and in vivo brain experiments were performed to evaluate the performance of the proposed method. For 2D MRSI experiments, SPICE is able to produce high SNR spatiospectral distributions with an approximately 3 mm nominal in-plane resolution from a 10-min acquisition. For 3D MRSI experiments, SPICE is able to achieve an approximately 3 mm inplane and 4 mm through-plane resolution in about 25 min.Conclusion-Special data acquisition and reconstruction methods have been developed for highresolution 1 H-MRSI of the brain using SPICE. Using these methods, SPICE is able to produce spatiospectral distributions of 1 H metabolites in the brain with high spatial resolution, while maintaining a good SNR. These capabilities should prove useful for practical applications of SPICE.