Quality management in in vivo proton MRS

Barros, Nuno Pedrosa de; Slotboom, Johannes

doi:10.1016/j.ab.2017.01.017

Cited by 24 publications

(35 citation statements)

References 51 publications

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“…The relative CRLB derived from fitting each spectrum to a model function is misleading as a numeric estimate of data quality if there is a real absence of specific metabolites; however, good CRLB values may also arise from fitting bad‐quality spectra if the noise is underestimated, artifacts are present, or the fitting method has converged to an incorrect solution (local minimum). Alternative quality measures include CRLB values from a fit to a co‐located water signal, using confidence limits and linewidths from spectral fitting of metabolites or water, detection of outlying values in the spectrum, and use of pattern recognition to classify poor‐quality spectra . A quality map enables easy interpretation at the time of the clinical read, such as implemented in the MIDAS software (Figure ).…”

Section: Standard Mrs Methodologymentioning

confidence: 99%

“…The first 6 can be calculated automatically by spectral analysis software; however, the evaluation of artifacts currently necessitates inspection by an experienced reader of MRS. 43 Current automatic quality control suffers from a lack of evidence-based quality thresholds, although general recommendations are available. 1,44,45 An accepted numerical quality estimate in relation to model fitting of MRS data is the CRLB: a lower estimate of the error of the concentration measurement as influenced by SNR, linewidth, and mutual signal overlap. A relative CRLB greater than 50% indicates that there is insufficient information to claim that the estimated value is significantly different from zero; therefore, it is often considered to be unreliable.…”

Section: Data Qualitymentioning

confidence: 99%

“…Alternative quality measures include CRLB values from a fit to a co-located water signal, using confidence limits 67 and linewidths from spectral fitting of metabolites or water, detection of outlying values in the spectrum, 68 and use of pattern recognition to classify poor-quality spectra. 44,69,70 A quality map enables easy interpretation at the time of the clinical read, such as implemented in the MIDAS software 71 (Figure 6). Nevertheless, a visual assessment of spectra in key diagnostic locations is still advisable, and a zoomed-in grid of the raw spectra overlaid on the MRI of the abnormal region adds confidence to any interpretation of metabolite maps.…”

Section: Cautions and Quality Controlmentioning

confidence: 99%

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Methodological consensus on clinical proton MRS of the brain: Review and recommendations

Wilson

Andronesi

Barker

et al. 2019

Magnetic Resonance in Med

328

450

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Proton MRS (1H MRS) provides noninvasive, quantitative metabolite profiles of tissue and has been shown to aid the clinical management of several brain diseases. Although most modern clinical MR scanners support MRS capabilities, routine use is largely restricted to specialized centers with good access to MR research support. Widespread adoption has been slow for several reasons, and technical challenges toward obtaining reliable good‐quality results have been identified as a contributing factor. Considerable progress has been made by the research community to address many of these challenges, and in this paper a consensus is presented on deficiencies in widely available MRS methodology and validated improvements that are currently in routine use at several clinical research institutions. In particular, the localization error for the PRESS localization sequence was found to be unacceptably high at 3 T, and use of the semi‐adiabatic localization by adiabatic selective refocusing sequence is a recommended solution. Incorporation of simulated metabolite basis sets into analysis routines is recommended for reliably capturing the full spectral detail available from short TE acquisitions. In addition, the importance of achieving a highly homogenous static magnetic field (B0) in the acquisition region is emphasized, and the limitations of current methods and hardware are discussed. Most recommendations require only software improvements, greatly enhancing the capabilities of clinical MRS on existing hardware. Implementation of these recommendations should strengthen current clinical applications and advance progress toward developing and validating new MRS biomarkers for clinical use.

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Section: Standard Mrs Methodologymentioning

confidence: 99%

Section: Data Qualitymentioning

confidence: 99%

Section: Cautions and Quality Controlmentioning

confidence: 99%

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Methodological consensus on clinical proton MRS of the brain: Review and recommendations

Wilson

Andronesi

Barker

et al. 2019

Magnetic Resonance in Med

328

450

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“… Residual water peak removal using HLSVD . HLSVD was applied considering eight components between 4.0 and 9.0 ppm. Automatic quality control . Quality control was performed using a random forest model that was trained to reproduce the judgment of an expert.…”

Section: Methodsmentioning

confidence: 99%

Analysis of metabolic abnormalities in high‐grade glioma using MRSI and convex NMF

et al. 2019

Self Cite

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Clinical use of MRSI is limited by the level of experience required to properly translate MRSI examinations into relevant clinical information. To solve this, several methods have been proposed to automatically recognize a predefined set of reference metabolic patterns. Given the variety of metabolic patterns seen in glioma patients, the decision on the optimal number of patterns that need to be used to describe the data is not trivial. In this paper, we propose a novel framework to (1) separate healthy from abnormal metabolic patterns and (2) retrieve an optimal number of reference patterns describing the most important types of abnormality. Using 41 MRSI examinations (1.5 T, PRESS, T E 135 ms) from 22 glioma patients, four different patterns describing different types of abnormality were detected: edema, healthy without Glx, active tumor and necrosis. The identified patterns were then evaluated on 17 MRSI examinations from nine different glioma patients. The results were compared against BraTumIA, an automatic segmentation method trained to identify different tumor compartments on structural MRI data. Finally, the ability to predict future contrast enhancement using the proposed approach was also evaluated. KEYWORDS applications, cancer, head and neck cancer methods and engineering, MRS and MRSI methods, post-acquisition processing, spectroscopic imaging, visualization methods and engineering 1 | INTRODUCTION MRS provides relevant metabolic information for the assessment of brain tumors, allowing us to distinguish different tumor types and grades, 1-3 distinguish radiation effects (pseudoprogression) from true progression 4,5 and identify regions with high tumor cellularity that are not visible in structural MRI. 6-8 Many publications 9 focus on the translation of one or two MRS features, such as choline (Cho)/NAA (N-acetyl aspartate) andCho/Cr (creatine), into clinically meaningful information for the tasks described above. Regardless of what can already be achieved with the analysis of individual metabolite ratios, the use of metabolic patterns for the identification of tissue types and diseases has the potential to allow more precise characterization of brain tumors.Several approaches 10-21 have been suggested for the analysis of metabolic patterns present in brain tumor MRS data. Before such methods can be used to interpret new data, a library of metabolic patterns has to be defined. The decision on the order of the model used to interpret .3 + Lip0.9; Lip0.9, 0.9 ppm signals originating from the -CH3 moiety of lipid molecules (triglycerides); Lip1.3, 1.3 ppm signals originating from the -CH2 moiety of lipid molecules (triglycerides); LOH, loss of heterozygosity; LOOCV, leave-one-out cross-validation; NAA, N-acetyl aspartate; NNLS, non-negative least squares

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“…Water suppression was achieved using a shortened chemical shift selective (CHESS) sequence with subject specific spectral-spatial (tailored) B 1 -insensitive suppression pulses [14]. Advanced reconstruction and post-processing techniques were used in conjunction with stringent data filtering based on several quality assurance (QA) metrics [24] including linewidth, SNR, Cramér-Rao Lower Bounds (CRLB) and the Lipid-to-total-Creatine ratio. High-resolution anatomical images facilitated a point-spread function adjusted [11,25] partial volume correction (PVC) method that accounted for grey matter (GM) and white matter (WM) signal contributions, along with the more commonly applied correction for cerebral-spinal fluid (CSF).…”

Section: Introductionmentioning

confidence: 99%

Lipid suppressed and tissue-fraction corrected metabolic distributions in human central brain structures using 2D¹H Magnetic Resonance Spectroscopic Imaging at 7 tesla

Bhogal

Broeders

Morsinkhof

et al. 2020

Preprint

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Magnetic resonance spectroscopic imaging (MRSI) has the potential to add a layer of understanding of the neurobiological mechanisms underlying brain diseases, disease progression, and treatment efficacy.Limitations related to metabolite fitting of low SNR data, signal variations due to partial volume effects, acquisition and extra-cranial lipid artefacts, along with clinically relevant aspects such as scan-time constraints, are among the factors that hinder the widespread implementation of in vivo MRSI. The aim of this work was to address these factors and to develop an acquisition, reconstruction and postprocessing pipeline to derive lipid suppressed metabolite values based on Free Induction Decay (FID-MRSI) measurements made using a 7 tesla MR scanner. Anatomical images were used to perform highresolution (1mm 3 ) partial-volume correction to account for grey matter, white matter and cerebralspinal fluid signal contributions. Implementation of automatic quality control thresholds and normalization of metabolic maps from 23 subjects to the MNI standard atlas facilitated the creation of high-resolution average metabolite maps of several clinically relevant metabolites in central brain regions, while accounting for macromolecular distributions. Reported metabolite values include glutamate, choline, (phospo)creatine, myo-inositol, glutathione, N-acetyl aspartyl glutamate(and glutamine) and N-acetyl aspartate. MNI-registered average metabolite maps facilitate group-based analysis; thus offering the possibility to mitigate uncertainty in variable MRSI.

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Quality management in in vivo proton MRS

Cited by 24 publications

References 51 publications

Methodological consensus on clinical proton MRS of the brain: Review and recommendations

Methodological consensus on clinical proton MRS of the brain: Review and recommendations

Analysis of metabolic abnormalities in high‐grade glioma using MRSI and convex NMF

Lipid suppressed and tissue-fraction corrected metabolic distributions in human central brain structures using 2D¹H Magnetic Resonance Spectroscopic Imaging at 7 tesla

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Quality management in in vivo proton MRS

Cited by 24 publications

References 51 publications

Methodological consensus on clinical proton MRS of the brain: Review and recommendations

Methodological consensus on clinical proton MRS of the brain: Review and recommendations

Analysis of metabolic abnormalities in high‐grade glioma using MRSI and convex NMF

Lipid suppressed and tissue-fraction corrected metabolic distributions in human central brain structures using 2D1H Magnetic Resonance Spectroscopic Imaging at 7 tesla

Contact Info

Product

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Lipid suppressed and tissue-fraction corrected metabolic distributions in human central brain structures using 2D¹H Magnetic Resonance Spectroscopic Imaging at 7 tesla