Neoadjuvant treatment response in lymph nodes predicts patient outcome, but existing methods do not track response during therapy accurately. In this study, specialized hardware was used to adapt ...high-field (7T) (31) P magnetic resonance spectroscopy (MRS), which has been shown to track treatment response in small breast tumors, to monitor axillary lymph nodes.
A dual-tuned quadrature coil that is a (31) P (120 MHz) transceiver and a (1) H (300 MHz) receiver was designed using a novel detune circuit. The transceiver/receiver coil in the axilla is used with a fractionated dipole antenna on the back of the subject and the conventional breast coil for transmit.
The novel circuit detuned the (1) H resonance without disturbing the (31) P resonance. In vivo demonstrations included: >80% homogeneous B1 (+) for (1) H over the axilla, identification of a small (3-mm diameter) lymph node, and (31) P MR spectra from a single healthy lymph node. The setup can detect <2 millimolar concentrations of metabolites from a 2-mL voxel.
The first (31) P MR spectrum from an in vivo lymph node indicates that the presented design may be sufficiently sensitive to detect metabolic response to neoadjuvant therapy. Multinuclei MRS of the lymph nodes at 7T is possible through combining lightweight antenna elements with dual-tuned transceiver/receive-only coils.
Proton magnetic resonance spectroscopy (
H-MRS) can be used to quantify in vivo metabolite levels, such as lactate, γ-aminobutyric acid (GABA) and glutamate (Glu). However, there are considerable ...analysis choices which can alter the accuracy or precision of
H-MRS metabolite quantification. It is currently unknown to what extent variations in the analysis pipeline used to quantify
H-MRS data affect outcomes. The purpose of this study was to evaluate whether the quantification of identical
H-MRS scans across independent and experienced research groups would yield comparable results. We investigated the influence of model parameters and spectral quantification software on fitted metabolite concentration values. Sixty spectra in 30 individuals (repeated measures) were acquired using a 7-T MRI scanner. Data were processed by four independent research groups with the freedom to choose their own individualized and optimal parameter settings using LCModel software. Data were processed a second time in one group using an independent software package (NMRWizard) for an additional comparison with a different post-processing platform. Correlations across research groups of the ratio between the highest and, arguably, the most relevant resonances for neurotransmission N-acetyl aspartate (NAA), N-acetyl aspartyl glutamate (NAAG) and Glu over the total creatine creatine (Cr) + phosphocreatine (PCr) concentration, using Pearson's product-moment correlation coefficient (r), were calculated. Mean inter-group correlations using LCModel software were 0.87, 0.88 and 0.77 for NAA/Cr + PCr, NAA + NAAG/Cr + PCr and Glu/Cr + PCr, respectively. The mean correlations when comparing NMRWizard results with LCModel fitting results at University Medical Center Utrecht (UMCU) were 0.87, 0.89 and 0.71 for NAA/Cr + PCr, NAA + NAAG/Cr + PCr and Glu/Cr + PCr, respectively. Metabolite quantification using identical
H-MRS data was influenced by processing parameters, basis sets and software choice. Locally preferred processing choices affected metabolite quantification, even when using identical software. Our results reinforce the notion that standard practices should be established to regularize outcomes of
H-MRS studies, and that basis sets used for processing should be made available to the scientific community.
Proton magnetic resonance spectroscopy (1H–MRS) can be used to quantify in vivo metabolite levels, such as lactate, γ‐aminobutyric acid (GABA) and glutamate (Glu). However, there are considerable ...analysis choices which can alter the accuracy or precision of 1H–MRS metabolite quantification. It is currently unknown to what extent variations in the analysis pipeline used to quantify 1H–MRS data affect outcomes. The purpose of this study was to evaluate whether the quantification of identical 1H–MRS scans across independent and experienced research groups would yield comparable results. We investigated the influence of model parameters and spectral quantification software on fitted metabolite concentration values. Sixty spectra in 30 individuals (repeated measures) were acquired using a 7‐T MRI scanner. Data were processed by four independent research groups with the freedom to choose their own individualized and optimal parameter settings using LCModel software. Data were processed a second time in one group using an independent software package (NMRWizard) for an additional comparison with a different post‐processing platform. Correlations across research groups of the ratio between the highest and, arguably, the most relevant resonances for neurotransmission N‐acetyl aspartate (NAA), N‐acetyl aspartyl glutamate (NAAG) and Glu over the total creatine creatine (Cr) + phosphocreatine (PCr) concentration, using Pearson's product–moment correlation coefficient (r), were calculated. Mean inter‐group correlations using LCModel software were 0.87, 0.88 and 0.77 for NAA/Cr + PCr, NAA + NAAG/Cr + PCr and Glu/Cr + PCr, respectively. The mean correlations when comparing NMRWizard results with LCModel fitting results at University Medical Center Utrecht (UMCU) were 0.87, 0.89 and 0.71 for NAA/Cr + PCr, NAA + NAAG/Cr + PCr and Glu/Cr + PCr, respectively. Metabolite quantification using identical 1H–MRS data was influenced by processing parameters, basis sets and software choice. Locally preferred processing choices affected metabolite quantification, even when using identical software. Our results reinforce the notion that standard practices should be established to regularize outcomes of 1H–MRS studies, and that basis sets used for processing should be made available to the scientific community.
This work investigates how choices made within magnetic resonance spectroscopy (MRS) data processing pipelines affect final metabolite concentration values in 60 single‐voxel MRS datasets. Metabolite quantification was influenced by processing parameters, basis sets and software choice. Standard practices must be established to regularize 1H–MRS studies. Software parameter choices and basis sets should be made available to the scientific community.
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 signal-to-noise ratios data, signal variations due to partial-volume effects, acquisition and extracranial lipid artifacts, along with clinically relevant aspects such as scan time constraints, are among the challenges associated with in vivo MRSI.
The aim of this work was to address some of these factors and to develop an acquisition, reconstruction, and postprocessing pipeline to derive lipid-suppressed metabolite values of central brain structures based on free-induction decay measurements made using a 7 T MR scanner. Anatomical images were used to perform high-resolution (1 mm
) partial-volume correction to account for gray matter, white matter (WM), and cerebral-spinal fluid signal contributions. Implementation of automatic quality control thresholds and normalization of metabolic maps from 23 subjects to the Montreal Neurological Institute (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. Partial-volume correction improved the delineation of deep brain nuclei. We report average metabolite values including glutamate + glutamine (Glx), glycerophosphocholine, choline and phosphocholine (tCho), (phospo)creatine, myo-inositol and glycine (mI-Gly), glutathione, N-acetyl-aspartyl glutamate(and glutamine), and N-acetyl-aspartate in the basal ganglia, central WM (thalamic radiation, corpus callosum) as well as insular cortex and intracalcarine sulcus.
MNI-registered average metabolite maps facilitate group-based analysis, thus offering the possibility to mitigate uncertainty in variable MRSI data.
Introduction
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 signal‐to‐noise ratios data, signal variations due to partial‐volume effects, acquisition and extracranial lipid artifacts, along with clinically relevant aspects such as scan time constraints, are among the challenges associated with in vivo MRSI.
Methods
The aim of this work was to address some of these factors and to develop an acquisition, reconstruction, and postprocessing pipeline to derive lipid‐suppressed metabolite values of central brain structures based on free‐induction decay measurements made using a 7 T MR scanner. Anatomical images were used to perform high‐resolution (1 mm3) partial‐volume correction to account for gray matter, white matter (WM), and cerebral‐spinal fluid signal contributions. Implementation of automatic quality control thresholds and normalization of metabolic maps from 23 subjects to the Montreal Neurological Institute (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.
Partial‐volume correction improved the delineation of deep brain nuclei. We report average metabolite values including glutamate + glutamine (Glx), glycerophosphocholine, choline and phosphocholine (tCho), (phospo)creatine, myo‐inositol and glycine (mI‐Gly), glutathione, N‐acetyl‐aspartyl glutamate(and glutamine), and N‐acetyl‐aspartate in the basal ganglia, central WM (thalamic radiation, corpus callosum) as well as insular cortex and intracalcarine sulcus.
Conclusion
MNI‐registered average metabolite maps facilitate group‐based analysis, thus offering the possibility to mitigate uncertainty in variable MRSI data.
We use a 7 T MRI scanner along with a combination of advanced acquisition, reconstruction, and postprocessing techniques to derive metabolic distribution in deep brain structures.
PDF
