NMR in biomedicine
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The central vein sign (CVS) is an efficient imaging biomarker for multiple sclerosis (MS) diagnosis, but its application in clinical routine is limited by inter-rater variability and the expenditure of time associated with manual assessment. We describe a deep learning-based prototype for automated assessment of the CVS in white matter MS lesions using data from three different imaging centers. We retrospectively analyzed data from 3 T magnetic resonance images acquired on four scanners from two different vendors, including adults with MS (n = 42), MS mimics (n = 33, encompassing 12 distinct neurological diseases mimicking MS) and uncertain diagnosis (n = 5). ⋯ On the validation and test sets, the lesion-wise performance outperformed the vesselness filter method (P < 0.001). The proposed deep learning prototype shows promising performance in differentiating MS from its mimics. Our approach was evaluated using data from different hospitals, enabling larger multicenter trials to evaluate the benefit of introducing the CVS marker into MS diagnostic criteria.
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Clinical Trial
Simultaneous edited MRS of GABA, glutathione, and ethanol.
The aim of this work was to develop simultaneous edited MRS of γ-aminobutyric acid (GABA), glutathione (GSH), and ethanol (EtOH) using Hadamard encoding and reconstruction of MEGA-edited spectroscopy (HERMES) at 3T. Density-matrix simulations of HERMES were carried out and compared with phantom experiments. In vivo experiments were performed in six healthy volunteers about 30 min after alcohol consumption. ⋯ Measured integral ratios were 0.082 ± 0.012 for GABA/Cr, 0.037 ± 0.006 for GSH/Cr, and 0.305 ± 0.129 for EtOH/Cr. Simulated, phantom, and in vivo measurements of HERMES show excellent separation of GABA-, GSH-, and EtOH-edited signals with negligible levels of crosstalk. HERMES allows a threefold acceleration of editing while maintaining spectral quality compared with sequentially acquired MEGA-PRESS measurements.
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Pulsed arterial spin labelling remains a non-invasive and highly used method for the study of rodent cerebral blood flow (CBF). Flow-sensitive alternating inversion recovery (FAIR) is one of the most commonly used MR-sequences for this purpose and exists with many different strategies to record the images. This study investigates Look-Locker (LL) TrueFISP readout for FAIR as an alternative to the standard EPI readout, which is provided by the manufacturer. ⋯ Both methods have the same measurement time. TrueFISP has the advantage to EPI of producing undistorted images over larger areas of the mouse brain. It is advisable to check the value of the blood relaxation rate by measurement or to estimate it as a fitting parameter.
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Acute respiratory distress syndrome (ARDS), manifested by intricate etiology and pathophysiology, demands careful clinical surveillance due to its high mortality and imminent life support measures. NMR based metabolomics provides an approach for ARDS which culminates from a wide spectrum of illness thereby confounding early manifestation and prognosis predictors. 1 H NMR with its manifold applications in critical disease settings can unravel the biomarker of ARDS thus holding potent implications by providing surrogate endpoints of clinical utility. NMR metabolomics which is the current apogee platform of omics trilogy is contributing towards the possible panacea of ARDS by subsequent validation of biomarker credential on larger datasets. ⋯ The review has been stepwise illustrated with potent biometrics employed to selectively pool out differential metabolites as diagnostic markers and outcome predictors. The following sections have been drafted with an objective to better understand ARDS mechanisms with predictive and precise biomarkers detected so far on the basis of underlying physiological parameters having close proximity to diseased phenotype. The aim of this review is to stimulate interest in conducting more studies to help resolve the complex heterogeneity of ARDS with biomarkers of clinical utility and relevance.
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Knee degeneration involves all the major tissues in the joint. However, conventional MRI sequences can only detect signals from long T2 tissues such as the superficial cartilage, with little signal from the deep cartilage, menisci, ligaments, tendons and bone. It is highly desirable to develop new sequences that can detect signal from all major tissues in the knee. ⋯ Much improved curve fitting was achieved for all UTE-Cones biomarkers with greatly reduced root mean square errors. The averaged T1 , T2 *, AdiabT1ρ , MTR and f for knee joint tissues of 15 healthy volunteers were reported. The 3D UTE-Cones quantitative imaging techniques (ie, T1 , T2 *, AdiabT1ρ , MTR and MT modeling) together with elastix motion correction provide robust volumetric measurement of relaxation times, MTR and f of both short and long T2 tissues in the knee joint.