Lactate quantitation in a gerbil brain stroke model by GSLIM of multiple-quantum-filtered signals

Kmiecik, Joseph A.; Gregory, Carl D.; Liang, Zhi Pei; Lauterbur, Paul C.; Dawson, M. Joan

doi:10.1002/(sici)1522-2586(199904)9:4<539::aid-jmri5>3.0.co;2-a

Cited by 10 publications

(8 citation statements)

References 22 publications

(31 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Because the CSI gradient set is discretized, this means that the only a priori information needed to run the sequence is the number C , which is generally fixed for a given study protocol. Compared to pro-active implementation of SLOOP [5], this has the advantage of avoiding image-guided gradient optimization, prescription, and implementation at the scanner-side prior to acquisition, whereas SLIM and GSLIM utilize standard CSI sequences [3, 4, 6, 11, 12]. …”

Section: Discussionmentioning

confidence: 99%

“…Thus, SLIM was applied retroactively to 1 H CSI data sets acquired from the human calf [3, 11] and brain [9], and both GSLIM and SLIM were used in 1 H MRS CSI acquisitions from a gerbil brain [12]. Although SLOOP 1 H MRS was initially performed with proactively optimized gradients on an excised rabbit kidney [5], all subsequent applications to human heart applied SLOOP retroactively to phosphorus ( 31 P) MRS data acquired with regular CSI gradients [13–16].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Magnetic resonance Spectroscopy with Linear Algebraic Modeling (SLAM) for higher speed and sensitivity

Zhang

Gabr

Schär

et al. 2012

Journal of Magnetic Resonance

View full text Add to dashboard Cite

Speed and signal-to-noise ratio (SNR) are critical for localized magnetic resonance spectroscopy (MRS) of low-concentration metabolites. Matching voxels to anatomical compartments a priori yields better SNR than the spectra created by summing signals from constituent chemical-shift-imaging (CSI) voxels post-acquisition. Here, a new method of localized Spectroscopy using Linear Algebraic Modeling (SLAM) is presented, that can realize this additional SNR gain. Unlike prior methods, SLAM generates spectra from C signal-generating anatomic compartments utilizing a CSI sequence wherein essentially only the C central k-space phase-encoding gradient steps with highest SNR are retained. After MRI-based compartment segmentation, the spectra are reconstructed by solving a sub-set of linear simultaneous equations from the standard CSI algorithm. SLAM is demonstrated with one-dimensional CSI surface coil phosphorus MRS in phantoms, the human leg and the heart on a 3T clinical scanner. Its SNR performance, accuracy, sensitivity to registration errors and inhomogeneity, are evaluated. Compared to one-dimensional CSI, SLAM yielded quantitatively the same results 4-times faster in 24 cardiac patients and healthy subjects. SLAM is further extended with fractional phase-encoding gradients that optimize SNR and/or minimize both inter- and intra-compartmental contamination. In proactive cardiac phosphorus MRS of 6 healthy subjects, both SLAM and fractional-SLAM (fSLAM) produced results indistinguishable from CSI while preserving SNR gains of 36–45% in the same scan-time. Both SLAM and fSLAM are simple to implement and reduce the minimum scan-time for CSI, which otherwise limits the translation of higher SNR achievable at higher field strengths to faster scanning.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Magnetic resonance Spectroscopy with Linear Algebraic Modeling (SLAM) for higher speed and sensitivity

Zhang

Gabr

Schär

et al. 2012

Journal of Magnetic Resonance

View full text Add to dashboard Cite

show abstract

“…With the advantages mentioned, BSLIM has potential for applications that have successfully deployed SLIM or GSLIM before; e.g., in cardiac imaging [33], brain imaging [34]- [36], and drug monitoring [37]. The promising results on the 5 1 measurement grid suggest the possible use of BSLIM in fast, spatially localized metabolite tracking applications.…”

Section: Discussionmentioning

confidence: 99%

BSLIM: Spectral Localization by Imaging With Explicit $B_{0}$ Field Inhomogeneity Compensation

Khalidov

Ville

Jacob

et al. 2007

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

Abstract-Magnetic resonance spectroscopy imaging (MRSI) is an attractive tool for medical imaging. However, its practical use is often limited by the intrinsic low spatial resolution and long acquisition time. Spectral localization by imaging (SLIM) has been proposed as a non-Fourier reconstruction algorithm that incorporates spatial a priori information about spectroscopically uniform compartments. Unfortunately, the influence of the magnetic field inhomogeneity-in particular, the susceptibility effects at tissues' boundaries-undermines the validity of the compartmental model. Therefore, we propose BSLIM as an extension of SLIM with field inhomogeneity compensation. A 0 -field inhomogeneity map, which can be acquired rapidly and at high resolution, is used by the new algorithm as additional a priori information. We show that the proposed method is distinct from the generalized SLIM (GSLIM) framework. Experimental results of a two-compartment phantom demonstrate the feasibility of the method and the importance of inhomogeneity compensation.Index Terms-Chemical shift imaging, constrained reconstruction, magnetic field inhomogeneity, magnetic resonance spectroscopy imaging (MRSI).

show abstract

“…GSLIM applies a data consistency constraint as the optimization parameter to find a(f) , through which the homogeneous part (partial SLIM reconstruction) and the inhomogeneous part (partial Fourier series reconstruction) can be isolated [47]. GSLIM reconstruction has shown less quantitation error compared to the Fourier transform in a phantom, to reveal unexpected heterogeneity of the frog skeletal muscle [61] and ischemic regions of the brain in the a gerbil model of stroke [62]. …”

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

View full text Add to dashboard Cite

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.

show abstract

Lactate quantitation in a gerbil brain stroke model by GSLIM of multiple-quantum-filtered signals

Cited by 10 publications

References 22 publications

Magnetic resonance Spectroscopy with Linear Algebraic Modeling (SLAM) for higher speed and sensitivity

Magnetic resonance Spectroscopy with Linear Algebraic Modeling (SLAM) for higher speed and sensitivity

BSLIM: Spectral Localization by Imaging With Explicit $B_{0}$ Field Inhomogeneity Compensation

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

Contact Info

Product

Resources

About