Constrained reconstruction applied to 2-D chemical shift imaging

Wear, Keith A.; Myers, Kyle J.; Rajan, Sunder S.; Grossman, Laurence W.

doi:10.1109/42.640749

Cited by 8 publications

(6 citation statements)

References 27 publications

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“…It has been shown that a flexible arrangement of spatial boxcar functions may serve as a reduced representation of the object in order to improve resolution beyond the sampling diffraction limit in MRI [16, 17] and in MRS [18]. Another example is the a priori constraint of total variation , effectively imposing a piecewise constancy of the object, which has been applied in MRI and MRS [19, 20].…”

Section: Imaging-based Mrs Localizationmentioning

confidence: 99%

Imaging based magnetic resonance spectroscopy (MRS) localization for quantitative neurochemical analysis and cerebral metabolism studies

Lee

Adany

Choi

2017

Analytical Biochemistry

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Accurate quantitative metabolic imaging of the brain presents significant challenges due to the complexity and heterogeneity of its structures and compositions with distinct compartmentations of brain tissue types (e.g., gray and white matter). The brain is compartmentalized into various regions based on their unique functions and locations. In vivo magnetic resonance spectroscopy (MRS) techniques allow non-invasive measurements of neurochemicals in either single voxel or multiple voxels, yet the spatial resolution and detection sensitivity of MRS are significantly lower compared with MRI. A fundamentally different approach, namely spectral localization by imaging (SLIM) provides a new framework that overcomes major limitations of conventional MRS techniques. Conventional MRS allows only rectangular voxel shapes that do not conform the shapes of brain structures or lesions, while SLIM allows compartments with arbitrary shapes. However, the restrictive assumption proposed in the original concept of SLIM, i.e., compartmental homogeneity, led to spectral localization errors, which have limited its broad applications. This review focuses on the recent technical frontiers of image-based MRS localization techniques that overcome the limitations of SLIM through the development and implementation of various new strategies, including incorporation of magnetic field inhomogeneity corrections, the use of multiple receiver coils, and prospective optimization of data acquisition.

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Section: Imaging-based Mrs Localizationmentioning

confidence: 99%

Imaging based magnetic resonance spectroscopy (MRS) localization for quantitative neurochemical analysis and cerebral metabolism studies

Lee

Adany

Choi

2017

Analytical Biochemistry

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“…The original SLIM concept was impelled by the postulate that the distribution of metabolites of interest is often linked with the distribution of water protons in biological samples. Therefore, a priori knowledge of anatomical features derived from structural H MRI could be used to inform the reconstruction of spectroscopic images, an idea which can be considered within a broader framework of "constrained imaging" [23]- [25]. With SLIM, high resolution anatomical MR images are used to define spectrally homogeneous compartments, which can be considered as generalized "voxels," and can assume arbitrary shape to match anatomical structure.…”

Section: A the Slim Framework Revisitedmentioning

confidence: 99%

Data-Driven MRSI Spectral Localization Via Low-Rank Component Analysis

Kasten

Lazeyras

Ville

2013

IEEE Trans. Med. Imaging

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Abstract-Magnetic resonance spectroscopic imaging (MRSI) is a powerful tool capable of providing spatially localized maps of metabolite concentrations. Its utility, however, is often depreciated by spectral leakage artifacts resulting from low spatial resolution measurements through an effort to reduce acquisition times. Though model-based techniques can help circumvent these drawbacks, they require strong prior knowledge, and can introduce additional artifacts when the underlying models are inaccurate. We introduce a novel scheme in which a generative model is estimated from the raw MRSI data via a regularized variational framework that minimizes the model approximation error within a measurement-prescribed subspace. As additional a priori information, our approach relies only upon a measured field inhomogeneity map at high spatial resolution. We demonstrate the feasibility of our approach on both synthetic and experimental data.

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“…The reconstruction process can be formulated as: [7] where A H is the NUFFT (NUFFT-1) operation that maps the density compensated raw k-space data (S) onto image space, W is the density compensation matrix, and Î is the reconstructed image. The NUFFT-1 operation takes the raw non-Cartesian data, convolves it with a special kernel and finds the corresponding coefficients on a Cartesian grid, performs the fast Fourier transform (FFT), then scales the image by the inverse of the accuracy factors used for constructing the convolution kernel (11,18).…”

Section: Fig 3 Amentioning

confidence: 99%

“…To follow rapidly evolving changes in metabolite concentrations, as cell metabolism transforms an injected compound into its downstream metabolites the use of fast, multispectral MR data acquisition techniques is required. Chemical shift imaging (CSI) techniques have been used to encode spectral and spatial information in MRSI exams (6,7). These techniques, however, have not been optimized for acquisition of hyperpolarized signals, and need to be improved to fully exploit the benefits offered by the large, slowly decaying, nonrenewable hyperpolarized signals.…”

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confidence: 99%

Multispectral imaging with three‐dimensional rosette trajectories

Bucholz

Song

Johnson

et al. 2008

Magnetic Resonance in Med

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Two-dimensional intersecting k-space trajectories have previously been demonstrated to allow fast multispectral imaging. Repeated sampling of k-space points leads to destructive interference of the signal coming from the off-resonance spectral peaks; on-resonance data reconstruction yields images of the on-resonance peak, with some of the off-resonance energy being spread as noise in the image. A shift of the k-space data by a given off-resonance frequency brings a second frequency of interest on resonance, allowing the reconstruction of a second spectral peak from the same k-space data. Given the higher signal-to-noise per unit time characteristic of a 3D acquisition, we extended the concept of intersecting trajectories to three dimensions. A 3D, rosette-like pulse sequence was designed and implemented on a clinical 1.5T scanner. An iterative density compensation function was developed to weight the 3D intersecting trajectories before Fourier transformation. Three volunteers were scanned using this sequence and separate fat and water images were reconstructed from the same imaging dataset. MRI is exquisitely sensitive in identifying anatomical changes associated with pathological conditions. It is becoming increasingly obvious, however, that early detection of disease, prior to anatomical transformations, is paramount for the treatment of disease. Changes in tissue biochemistry usually precede changes in anatomy and have been identified and visualized through MR spectroscopic imaging (MRSI) (1,2). Given the low concentration of endogenous compounds, MRSI exams generally require a long scan time and produce images with a low signal-tonoise ratio (SNR) and low spatial resolution.The recent development of hyperpolarization techniques promises to fundamentally expand the capabilities of MRI, increasing SNR of MRSI exams and allowing realtime, in vivo imaging of metabolism (3-5). To follow rapidly evolving changes in metabolite concentrations, as cell metabolism transforms an injected compound into its downstream metabolites the use of fast, multispectral MR data acquisition techniques is required. Chemical shift imaging (CSI) techniques have been used to encode spectral and spatial information in MRSI exams (6,7). These techniques, however, have not been optimized for acquisition of hyperpolarized signals, and need to be improved to fully exploit the benefits offered by the large, slowly decaying, nonrenewable hyperpolarized signals.Intersecting trajectories in k-space are a promising alternative for fast multispectral imaging and have been previously demonstrated in 2D (8,9). These two previously presented techniques are both a variation of chemical shift imaging, in which the spectral information is acquired through multiple crossings of the same points in k-space. In the first approach (8), random intersecting trajectories were used to reconstruct on-and off-resonant frequency information. The random stochastic trajectories proposed (8) were taxing on the gradient system and the quality of the reconstructed imag...

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Constrained reconstruction applied to 2-D chemical shift imaging

Cited by 8 publications

References 27 publications

Imaging based magnetic resonance spectroscopy (MRS) localization for quantitative neurochemical analysis and cerebral metabolism studies

Imaging based magnetic resonance spectroscopy (MRS) localization for quantitative neurochemical analysis and cerebral metabolism studies

Data-Driven MRSI Spectral Localization Via Low-Rank Component Analysis

Multispectral imaging with three‐dimensional rosette trajectories

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