A novel and fast time-domain quantitation algorithm--quantitation based on semi-parametric quantum estimation (QUEST)--invoking optimal prior knowledge is proposed and tested. This nonlinear least-squares algorithm fits a time-domain model function, made up from a basis set of quantum-mechanically simulated whole-metabolite signals, to low-SNR in vivo data. A basis set of in vitro measured signals can be used too. The simulated basis set was created with the software package NMR-SCOPE which can invoke various experimental protocols. Quantitation of 1H short echo-time signals is often hampered by a background signal originating mainly from macromolecules and lipids. Here, we propose and compare three novel semi-parametric approaches to handle such signals in terms of bias-variance trade-off. The performances of our methods are evaluated through extensive Monte-Carlo studies. Uncertainty caused by the background is accounted for in the Cramér-Rao lower bounds calculation. Valuable insight about quantitation precision is obtained from the correlation matrices. Quantitation with QUEST of 1H in vitro data, 1H in vivo short echo-time and 31P human brain signals at 1.5 T, as well as 1H spectroscopic imaging data of human brain at 1.5 T, is demonstrated.

Quantitation of 1H short echo-time signals is often hampered by a background signal originating mainly from macromolecules and lipids. While the model function of the metabolite signal is known, that of the macromolecules is only partially known. We present time-domain semi-parametric estimation approaches based on the QUEST quantitation algorithm (QUantitation based on QUantum ESTimation) and encompassing Cramér-Rao bounds that handle the influence of 'nuisance' parameters related to the background. Three novel methods for background accommodation are presented. They are based on the fast decay of the background signal in the time domain. After automatic estimation, the background signal can be automatically (1) subtracted from the raw data, (2) included in the basis set as multiple components, or (3) included in the basis set as a single entity. The performances of these methods combined with QUEST are evaluated through extensive Monte Carlo studies. They are compared in terms of bias-variance trade-off. Because error bars on the amplitudes are of paramount importance for diagnostic reliability, Cramér-Rao bounds accounting for the uncertainty caused by the background are proposed. Quantitation with QUEST of in vivo short echo-time (1)H human brain with estimation of the background is demonstrated.

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IMPORTANCE Predicting disease evolution is becoming essential for optimizing treatment decision making in multiple sclerosis (MS). Multiple sclerosis pathologic damage typically includes demyelination, neuro-axonal loss, and astrogliosis. OBJECTIVE To evaluate the potential of magnetic resonance markers of central nervous system injury to predict brain-volume loss and clinical disability in multiple sclerosis. DESIGN, SETTING, AND PARTICIPANTS Participants were selected from the Multiple Sclerosis Center at the University of California-San Francisco. The preliminary data set included 59 patients with MS and 43 healthy control individuals. The confirmatory data set included 220 patients from an independent, large genotype-phenotype research project. MAIN OUTCOMES AND MEASURESBaseline N-acetylaspartate (NAA) level, myo-inositol (mI) in normal-appearing white and gray matter, myelin water fraction in normal-appearing white matter, markers of axonal damage, astrogliosis, and demyelination were evaluated as predictors in a preliminary data set. Potential predictors were subsequently tested for replication in a confirmatory data set. Clinical scores and percentage of brain-volume change were obtained annually over 4 years as outcomes. Predictors of outcomes were assessed using linear models, linear mixed-effects models, and logistic regression. RESULTS N-acetylaspartate and mI both had statistically significant effects on brain volume, prompting the use of the mI:NAA ratio in normal-appearing white matter as a predictor. The ratio was a predictor of brain-volume change in both cohorts (annual slope in the percentage of brain-volume change/unit of increase in the ratio: −1.68; 95% CI, −3.05 to −0.30; P = .02 in the preliminary study cohort and −1.08; 95% CI, −1.95 to −0.20; P = .02 in the confirmatory study cohort). Furthermore, the mI:NAA ratio predicted clinical disability (Multiple Sclerosis Functional Composite evolution: −0.52 points annually, P < .001; Multiple Sclerosis Functional Composite sustained progression: odds ratio, 2.76/SD increase in the ratio; 95% CI, 1.32 to 6.47; P = .01) in the preliminary data set and predicted Multiple Sclerosis Functional Composite evolution (−0.23 points annually; P = .01), Expanded Disability Status Scale evolution (0.57 points annually; P = .04), and Expanded Disability Status Scale sustained progression (odds ratio, 1.46; 95% CI, 1.10 to 1.94; P = .009) in the confirmatory data set. Myelin water fraction did not show predictive value. CONCLUSIONS AND RELEVANCEThe mI:NAA ratio in normal-appearing white matter has consistent predictive power on brain atrophy and neurological disability evolution. The combined presence of astrogliosis and axonal damage in white matter has cardinal importance in disease severity.

Purpose: To measure T 1 and T 2 relaxation times of metabolites in glioma patients at 3T and to investigate how these values influence the observed metabolite levels. Materials and Methods:A total of 23 patients with gliomas and 10 volunteers were studied with single-voxel two-dimensional (2D) J-resolved point-resolved spectral selection (PRESS) using a 3T MR scanner. Voxels were chosen in normal appearing white matter (WM) and in regions of tumor. The T 1 and T 2 of choline containing compounds (Cho), creatine (Cr), and N-acetyl aspartate (NAA) were estimated.Results: Metabolite T 1 relaxation values in gliomas were not significantly different from values in normal WM. The T 2 of Cho and Cr were statistically significantly longer for grade 4 gliomas than for normal WM but the T 2 of NAA was similar. These differences were large enough to impact the corrections of metabolite levels for relaxation times with tumor grade in terms of metabolite ratios (P Ͻ 0.001). Conclusion:The differential increase in T 2 for Cho and Cr relative to NAA means that the ratios of Cho/NAA and Cr/ NAA are higher in tumor at longer echo times (TEs) relative to values in normal appearing brain. Having this information may be useful in defining the acquisition parameters for optimizing contrast between tumor and normal tissue in MR spectroscopic imaging (MRSI) data, in which limited time is available and only one TE can be used. GLIOMAS ACCOUNT FOR THE MAJORITY of primary brain tumors and vary from benign to malignant. Among all the gliomas, glioblastoma multiforme (GBM) is both the most common and the most malignant, with a relatively poor prognosis. Proton magnetic resonance spectroscopy ( 1 H-MRS) is a powerful noninvasive tool that has been used for the assessment of metabolites in gliomas and the biochemical profiles of brain tumors have been widely studied (1-4). A general marker of brain tumors is the elevation of choline-containing compounds (Cho), which is thought to be due to increased cell density and membrane turnover in neoplasms, and the reduction of the neural marker Nacetyl aspartate (NAA). The availability of higher field strength MR scanners and multichannel radio frequency coils offer the potential of higher signal-to-noise ratio (SNR) and better spectral resolution (5) that can be used to either shorten acquisition time, decrease spatial resolution, or improve detection of other brain metabolites; such as, glutamate (Glu), a main excitatory neurotransmitter; glutamine (Gln), which acts as a detoxifier; and myo-inositol (mI), which is predominantly located within astrocytes.In planning the data acquisition parameters for using MR spectroscopic imaging (MRSI) to determine the spatial extent of tumor vs. normal brain tissue, it is important to consider how to select values that will emphasize the contrast between metabolites in the different regions. Because of the restrictions on clinical scan time, the repetition time (TR) for acquiring MRSI data is often set at one to two seconds and it is usually only possible to acq...

Magnetic resonance spectroscopy (MRS) is an important technique in biomedical research and it has the unique capability to give a non-invasive access to the biochemical content (metabolites) of scanned organs. In the literature, the quantification (the extraction of the potential biomarkers from the MRS signals) involves the resolution of an inverse problem based on a parametric model of the metabolite signal. However, poor signal-to-noise ratio (SNR), presence of the macromolecule signal or high correlation between metabolite spectral patterns can cause high uncertainties for most of the metabolites, which is one of the main reasons that prevents use of MRS in clinical routine. In this paper, quantification of metabolites in MR Spectroscopic imaging using deep learning is proposed. A regression framework based on the Convolutional Neural Networks (CNN) is introduced for an accurate estimation of spectral parameters. The proposed model learns the spectral features from a large-scale simulated data set with different variations of human brain spectra and SNRs. Experimental results demonstrate the accuracy of the proposed method, compared to state of the art standard quantification method (QUEST), on concentration of 20 metabolites and the macromolecule.

Background Overweight and obesity are major worldwide health concerns characterized by an abnormal accumulation of fat in adipose tissue (AT) and liver. Purpose To evaluate the volume and the fatty acid (FA) composition of the subcutaneous adipose tissue (SAT) and the visceral adipose tissue (VAT) and the fat content in the liver from 3D chemical‐shift‐encoded (CSE)‐MRI acquisition, before and after a 31‐day overfeeding protocol. Study Type Prospective and longitudinal study. Subjects Twenty‐one nonobese healthy male volunteers. Field Strength/Sequence A 3D spoiled‐gradient multiple echo sequence and STEAM sequence were performed at 3T. Assessment AT volume was automatically segmented on CSE‐MRI between L2 to L4 lumbar vertebrae and compared to the dual‐energy X‐ray absorptiometry (DEXA) measurement. CSE‐MRI and MR spectroscopy (MRS) data were analyzed to assess the proton density fat fraction (PDFF) in the liver and the FA composition in SAT and VAT. Gas chromatography‐mass spectrometry (GC‐MS) analyses were performed on 13 SAT samples as a FA composition countermeasure. Statistical Tests Paired t‐test, Pearson's correlation coefficient, and Bland–Altman plots were used to compare measurements. Results SAT and VAT volumes significantly increased (P < 0.001). CSE‐MRI and DEXA measurements were strongly correlated (r = 0.98, P < 0.001). PDFF significantly increased in the liver (+1.35, P = 0.002 for CSE‐MRI, + 1.74, P = 0.002 for MRS). FA composition of SAT and VAT appeared to be consistent between localized‐MRS and CSE‐MRI (on whole segmented volume) measurements. A significant difference between SAT and VAT FA composition was found (P < 0.001 for CSE‐MRI, P = 0.001 for MRS). MRS and CSE‐MRI measurements of the FA composition were correlated with the GC‐MS results (for ndb: rMRS/GC‐MS = 0.83 P < 0.001, rCSE‐MRI/GC‐MS = 0.84, P = 0.001; for nmidb: rMRS/GC‐MS = 0.74, P = 0.006, rCSE‐MRI/GC‐MS = 0.66, P = 0.020) Data Conclusion The follow‐up of liver PDFF, volume, and FA composition of AT during an overfeeding diet was demonstrated through different methods. The CSE‐MRI sequence associated with a dedicated postprocessing was found reliable for such quantification. Level of Evidence: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1587–1599.

• Non-invasive techniques to diagnose non-alcoholic fatty liver diseases (NAFLD) are important. • Liver fat volume fraction quantified using MRI correlates well with histology. • Fat volume fraction could be a relevant marker for NAFLD clinical follow-up. • Disjointed relaxation time estimation could potentially identify factors contributing to NAFLD.