Fitting interrelated datasets: metabolite diffusion and general lineshapes

Adalid, Victor; Döring, André; Kyathanahally, Sreenath Pruthviraj; Bolliger, Christine Sandra; Boesch, Chris; Kreis, Roland

doi:10.1007/s10334-017-0618-z

Cited by 17 publications

(38 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Confirming earlier results, 16,32 use of the lineshape information from the reference water signal helped to improve the fit precision in general, especially when it comes to poor overall shim performance. A simultaneous fit of the channels was evaluated as an alternative to the traditional way of fitting the weighted sum spectrum.…”

Section: Postprocessing and Fittingsupporting

confidence: 77%

“…Therefore, an arbitrary lineshape can be modeled during the fit 9,10 or a reference lineshape can be derived from the nonsuppressed water signal, which suffers from the same distortions as the metabolite signals in the water-suppressed spectrum, to improve the suitability of the fitting model. [11][12][13][14][15][16] The use of such a reference lineshape thus seems appropriate when optimizing the acquisition settings for larger ROIs and will be investigated in this study.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing acquisition and fitting conditions for ¹H MR spectroscopy investigations in global brain pathology

2019

Self Cite

View full text Add to dashboard Cite

Purpose To optimize acquisition and fitting conditions for nonfocal disease in terms of voxel size and use of individual coil element data. Increasing the voxel size yields a higher signal‐to‐noise ratio, but leads to larger linewidths and more artifacts. Several ways to improve the spectral quality for large voxels are exploited and the optimal use of individual coil signals investigated. Methods Ten human subjects were measured at 3 T using a 64‐channel receive head coil with a semi‐LASER localization sequence under optimized and deliberately mis‐set field homogeneity. Eight different voxel sizes (8 to 99 cm3) were probed. Spectra were fitted either as weighted sums of the individual coil elements or simultaneously without summation. Eighteen metabolites were included in the fit model that also included the lineshapes from all coil elements as reflected in water reference data. Fitting errors for creatine, myo‐Inositol and glutamate are reported as representative parameters to judge optimal acquisition and evaluation conditions. Results Minimal Cramér‐Rao lower bounds and thus optimal acquisition conditions were found for a voxel size of ~ 70 cm3 for the representative upfield metabolites. Spectral quality in terms of lineshape and artifact appearance was determined to differ substantially between coil elements. Simultaneous fitting of spectra from individual coil elements instead of traditional fitting of a weighted sum spectrum reduced Cramer‐Rao lower bounds by up to 17% for large voxel sizes. Conclusion The optimal voxel size for best precision in determined metabolite content is surprisingly large. Such an acquisition condition is most relevant for detection of low‐concentration metabolites, like NAD+ or phenylalanine, but also for longitudinal studies where very small alterations in metabolite content are targeted. In addition, simultaneous fitting of single channel spectra enforcing lineshape and coil sensitivity information proved to be superior to traditional signal combination with subsequent fitting.

show abstract

Section: Postprocessing and Fittingsupporting

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Optimizing acquisition and fitting conditions for ¹H MR spectroscopy investigations in global brain pathology

2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Spectra were fitted with prior‐knowledge enhanced linear combination models using FiTAID . As recently described, this tool has been extended to support simultaneous modeling of the spectral and diffusion domains, for which the diffusion effect is represented by a mono‐exponential signal attenuation:

A (b) = {normalA}_{0} e^{- b A D C},

where

b

is the b ‐value as defined above;

A (b)

the area of a specific metabolite at a specific b ‐value; and

A_{0}

is the theoretical metabolite area without diffusion weighting. For the in vivo data, the basis set comprised 19 metabolites (Asp: aspartate; Cr; Etn: ethanolamine; GABA: gamma‐aminobutyric acid; Glc: glucose; Gln: glutamine; Glu; Gly: glycine; GPC: glycerophosphorylcholine; GSH: glutathione; Lac; mI; NAA; NAAG: N‐acetylaspartylglutamate; PCho: phosphocholine; PCr: phosphocreatine; PE: phosphorylethanolamine; sI: scyllo‐inositol; Tau: taurine) with spectra simulated using versatile simulation, pulses and analysis (VeSPA), assuming ideal RF pulses.…”

Section: Methodsmentioning

confidence: 99%

“…In addition, a novel correction scheme is introduced that allows artefact correction and motion compensation (MoCom) using the co‐acquired water signal as inherent reference and also enables compensation for nonlinear motion, rendering cardiac and respiratory gating dispensable. Furthermore, simultaneous 2D spectrum‐ADC fitting using the fitting tool for arrays of interrelated datasets (FiTAID) toolbox is included to maximize enforcement of prior knowledge constraints . First, the technique is applied in a phantom with and without motion, showing that the new MoCom scheme leads to improved ADC estimation without triggering.…”

Section: Introductionmentioning

confidence: 99%

Diffusion‐weighted magnetic resonance spectroscopy boosted by simultaneously acquired water reference signals

Döring

Adalid

Boesch

et al. 2018

Magnetic Resonance in Med

Self Cite

View full text Add to dashboard Cite

Using the simultaneously acquired water signal as internal reference allows not only for compensation of phase and frequency fluctuations but also for signal amplitude restoration, and thus improved metabolite ADC estimation. Combination with 2D simultaneous fitting promises access to the diffusion properties even for low signal-to-noise metabolites. The combination of both techniques increases the specificity and sensitivity of estimated metabolite ADC values in the cohort.

show abstract

“…In such cases, phase correction based on water may not fully restore metabolite phase. To circumvent this problem, it has been proposed to use the water signal after adding an inversion-recovery CSF-nulling block (64). Other kinds of bulk motion, such as rotational motion, result in overall signal attenuation on individual scans, and are therefore less trivial to correct.…”

Section: Bulk Translational Motion In the Presence Of Diffusion Gradimentioning

confidence: 99%

Insights into brain microstructure from in vivo DW-MRS

et al. 2018

View full text Add to dashboard Cite

Many developmental processes, such as plasticity and aging, or pathological processes such as neurological diseases are characterized by modulations of specific cellular types and their microstructures. Diffusion-weighted Magnetic Resonance Imaging (DW-MRI) is a powerful technique for probing microstructure, yet its information arises from the ubiquitous, non-specific water signal. By contrast, diffusion-weighted Magnetic Resonance Spectroscopy (DW-MRS) allows specific characterizations of tissues such as brain and muscle in vivo by quantifying the diffusion properties of MR-observable metabolites. Many brain metabolites are predominantly intracellular, and some of them are preferentially localized in specific brain cell populations, e.g., neurons and glia. Given the microstructural sensitivity of diffusion-encoding filters, investigation of metabolite diffusion properties using DW-MRS can thus provide exclusive cell and compartment-specific information. Furthermore, since many models and assumptions are used for quantification of water diffusion, metabolite diffusion may serve to generate a-priori information for model selection in DW-MRI. However, DW-MRS measurements are extremely challenging, from the acquisition to the accurate and correct analysis and quantification stages. In this review, we survey the state-of-the-art methods that have been developed for the robust acquisition, quantification and analysis of DW-MRS data and discuss the potential relevance of DW-MRS for elucidating brain microstructure in vivo. The review highlights that when accurate data on the diffusion of multiple metabolites is combined with accurate computational and geometrical modeling, DW-MRS can provide unique cell-specific information on the intracellular structure of brain tissue, in health and disease, which could serve as incentives for further application in vivo in human research and clinical MRI.

show abstract

Fitting interrelated datasets: metabolite diffusion and general lineshapes

Abstract: It is shown that inclusion of a measured lineshape into modeling of interrelated MR spectra is beneficial and can be combined also with simultaneous spectral and diffusion modeling.

Cited by 17 publications

References 36 publications

Optimizing acquisition and fitting conditions for ¹H MR spectroscopy investigations in global brain pathology

Optimizing acquisition and fitting conditions for ¹H MR spectroscopy investigations in global brain pathology

Diffusion‐weighted magnetic resonance spectroscopy boosted by simultaneously acquired water reference signals

Insights into brain microstructure from in vivo DW-MRS

Contact Info

Product

Resources

About

Fitting interrelated datasets: metabolite diffusion and general lineshapes

Abstract: It is shown that inclusion of a measured lineshape into modeling of interrelated MR spectra is beneficial and can be combined also with simultaneous spectral and diffusion modeling.

Cited by 17 publications

References 36 publications

Optimizing acquisition and fitting conditions for 1H MR spectroscopy investigations in global brain pathology

Optimizing acquisition and fitting conditions for 1H MR spectroscopy investigations in global brain pathology

Diffusion‐weighted magnetic resonance spectroscopy boosted by simultaneously acquired water reference signals

Insights into brain microstructure from in vivo DW-MRS

Contact Info

Product

Resources

About

Optimizing acquisition and fitting conditions for ¹H MR spectroscopy investigations in global brain pathology

Optimizing acquisition and fitting conditions for ¹H MR spectroscopy investigations in global brain pathology