Quantitative 23Na magnetic resonance imaging in the abdomen at 3 T.

OBJECTIVES: To assess the feasibility of sodium-23 MRI for performing quantitative and non-invasive measurements of total sodium concentration (TSC) and relaxation in a variety of abdominal organs. MATERIALS AND METHODS: Proton and sodium imaging of the abdomen was performed in 19 healthy volunteers using a 3D cones sequence and a sodium-tuned 4-rung transmit/receive body coil on a clinical 3 T system. The effects of B1 non-uniformity on TSC measurements were corrected using the double-angle method. The long-component of 23Na T2* relaxation time was measured using a series of variable echo-times. RESULTS: The mean and standard deviation of TSC and long-component 23Na T2* values were calculated across the healthy volunteer group in the kidneys, cerebrospinal fluid (CSF), liver, gallbladder, spleen, aorta, and inferior vena cava. DISCUSSION: Mean TSC values in the kidneys, liver, and spleen were similar to those reported using 23Na-MRI previously in the literature. Measurements in the CSF and gallbladder were lower, potentially due to the reduced spatial resolution achievable in a clinically acceptable scan time. Mean long-component 23Na T2* values were consistent with previous reports from the kidneys and CSF. Intra-population standard error was larger in smaller, fluid-filled structures due to fluid motion and partial volume effects.

Mapping Out Phase Diagrams with Generative Classifiers.

One of the central tasks in many-body physics is the determination of phase diagrams. However, mapping out a phase diagram generally requires a great deal of human intuition and understanding. To automate this process, one can frame it as a classification task. Typically, classification problems are tackled using discriminative classifiers that explicitly model the probability of the labels for a given sample. Here we show that phase-classification problems are naturally suitable to be solved using generative classifiers based on probabilistic models of the measurement statistics underlying the physical system. Such a generative approach benefits from modeling concepts native to the realm of statistical and quantum physics, as well as recent advances in machine learning. This leads to a powerful framework for the autonomous determination of phase diagrams with little to no human supervision that we showcase in applications to classical equilibrium systems and quantum ground states.

Combining Machine Learning and Spectroscopy to Model Reactive Atom + Diatom Collisions.

The prediction of product translational, vibrational, and rotational energy distributions for arbitrary initial conditions for reactive atom + diatom collisions is of considerable practical interest in atmospheric re-entry. Because of the large number of accessible states, determination of the necessary information from explicit (quasi-classical or quantum) dynamics studies is impractical. Here, a machine-learned (ML) model based on translational energy and product vibrational states assigned from a spectroscopic, ro-vibrational coupled energy expression based on the Dunham expansion is developed and tested quantitatively. All models considered in this work reproduce final state distributions determined from quasi-classical trajectory (QCT) simulations with R2 ∼ 0.98. As a further validation, thermal rates determined from the machine-learned models agree with those from explicit QCT simulations and demonstrate that the atomistic details are retained by the machine learning which makes them suitable for applications in more coarse-grained simulations. More generally, it is found that ML is suitable for designing robust and accurate models from mixed computational/experimental data which may also be of interest in other areas of the physical sciences.

Machine learning product state distributions from initial reactant states for a reactive atom-diatom collision system.

A machine-learned model for predicting product state distributions from specific initial states (state-to-distribution or STD) for reactive atom-diatom collisions is presented and quantitatively tested for the N(4S) + O2(X3Σg -) → NO(X2Π) + O(3P) reaction. The reference dataset for training the neural network consists of final state distributions determined from quasi-classical trajectory (QCT) simulations for ∼2000 initial conditions. Overall, the prediction accuracy as quantified by the root-mean-squared difference (∼0.003) and the R2 (∼0.99) between the reference QCT and predictions of the STD model is high for the test set, for off-grid state-specific initial conditions, and for initial conditions drawn from reactant state distributions characterized by translational, rotational, and vibrational temperatures. Compared with a more coarse grained distribution-to-distribution (DTD) model evaluated on the same initial state distributions, the STD model shows comparable performance with the additional benefit of the state resolution in the reactant preparation. Starting from specific initial states also leads to a more diverse range of final state distributions, which requires a more expressive neural network compared with DTD. A direct comparison between QCT simulations, the STD model, and the widely used Larsen-Borgnakke (LB) model shows that the STD model is quantitative, whereas the LB model is qualitative at best for rotational distributions P(j') and fails for vibrational distributions P(v'). As such, the STD model can be well-suited for simulating nonequilibrium high-speed flows, e.g., using the direct simulation Monte Carlo method.

Diagnostic quality assessment of IR-prepared 3D magnetic resonance neuroimaging accelerated using compressed sensing and k-space sampling order optimization.

PURPOSE: To evaluate the clinical diagnostic efficacy of accelerated 3D magnetic resonance (MR) neuroimaging by radiological assessment for image quality and artefacts. STUDY TYPE: Prospective healthy volunteer study. SUBJECTS: Eight healthy subjects. FIELD STRENGTH/SEQUENCE: Inversion Recovery (IR) prepared 3D Gradient Echo (GRE) sequence on a 1.5 T GE Signa HDx scanner. ASSESSMENT: Independent radiological diagnostic quality assessments of accelerated 3D MR brain datasets were carried out by four experienced neuro-radiologists who were blinded to the acceleration factor and to the subject. The radiological grading was based on a previously reported radiological scoring key that was used for image quality assessment of human brains. STATISTICAL TESTS: Bland-Altman analysis. RESULTS: Optimization of the k-space sampling order was important for preserving contrast in accelerated scans. Despite having lower scores than fully sampled datasets, the majority of the compressed sensing (CS) accelerated brain datasets with k-space sampling order optimization (19/24 datasets by Radiologist 1, 24/24 datasets by Radiologist 2 and 16/24 datasets by Radiologist 3) were graded to be fully diagnostic indicating that there was adequate confidence for performing gross structural assessment of the brain. CONCLUSION: Optimization of k-space acquisition order improves the clinical utility of CS accelerated 3D neuroimaging. This method may be appropriate for routine radiological assessment of the brain.

Machine Learning for Observables: Reactant to Product State Distributions for Atom-Diatom Collisions.

Machine learning based models to predict product state distributions from a distribution of reactant conditions for atom-diatom collisions are presented and quantitatively tested. The models are based on function-, kernel-, and grid-based representations of the reactant and product state distributions. All three methods predict final state distributions from explicit quasi-classical trajectory simulations with R2 > 0.998. Although a function-based approach is found to be more than two times better in computational performance, the grid-based approach is preferred in terms of prediction accuracy, practicability, and generality. For the function-based approach, the choice of parametrized functions is crucial and this aspect is explicitly probed for final vibrational state distributions. Applications of the grid-based approach to nonequilibrium, multitemperature initial state distributions are presented, a situation common to energy and state distributions in hypersonic flows. The role of such models in direct simulation Monte Carlo and computational fluid dynamics simulations is also discussed.

Multi-shot Echo Planar Imaging for accelerated Cartesian MR Fingerprinting: An alternative to conventional spiral MR Fingerprinting.

PURPOSE: To develop an accelerated Cartesian MRF implementation using a multi-shot EPI sequence for rapid simultaneous quantification of T1 and T2 parameters. METHODS: The proposed Cartesian MRF method involved the acquisition of highly subsampled MR images using a 16-shot EPI readout. A linearly varying flip angle train was used for rapid, simultaneous T1 and T2 quantification. The results were compared to a conventional spiral MRF implementation. The acquisition time per slice was 8s and this method was validated on two different phantoms and three healthy volunteer brains in vivo. RESULTS: Joint T1 and T2 estimations using the 16-shot EPI readout are in good agreement with the spiral implementation using the same acquisition parameters (<4% deviation for T1 and <6% deviation for T2). The T1 and T2 values also agree with the conventional values previously reported in the literature. The visual qualities of fine brain structures in the multi-parametric maps generated by multi-shot EPI-MRF and Spiral-MRF implementations were comparable. CONCLUSION: The multi-shot EPI-MRF method generated accurate quantitative multi-parametric maps similar to conventional Spiral-MRF. This multi-shot approach achieved considerable k-space subsampling and comparatively short TRs in a similar manner to spirals and therefore provides an alternative for performing MRF using an accelerated Cartesian readout; thereby increasing the potential usability of MRF.

[Rapid identification of potato cultivars using NIR-excited fluorescence and Raman spectroscopy].

Potato is one of the most important food in the world. Rapid and noninvasive identification of potato cultivars plays a important role in the better use of varieties. In this study, The identification ability of optical spectroscopy techniques, including near-infrared (NIR) Raman spectroscopy and NIR fluorescence spectroscopy, for invasive detection of potato cultivars was evaluated. A rapid NIR Raman spectroscopy system was applied to measure the composite Raman and NIR fluorescence spectroscopy of 3 different species of potatoes (98 samples in total) under 785 nm laser light excitation. Then pure Raman and NIR fluorescence spectroscopy were abstracted from the composite spectroscopy, respectively. At last, the partial least squares-discriminant analysis (PLS-DA) was utilized to analyze and classify Raman spectra of 3 different types of potatoes. All the samples were divided into two sets at random: the calibration set (74samples) and prediction set (24 samples), the model was validated using a leave-one-out, cross-validation method. The results showed that both the NIR-excited fluorescence spectra and pure Raman spectra could be used to identify three cultivars of potatoes. The fluorescence spectrum could distinguish the Favorita variety well (sensitivity: 1, specificity: 0.86 and accuracy: 0.92), but the result for Diamant (sensitivity: 0.75, specificity: 0.75 and accuracy: 0. 75) and Granola (sensitivity: 0.16, specificity: 0.89 and accuracy: 0.71) cultivars identification were a bit poorer. We demonstrated that Raman spectroscopy uncovered the main biochemical compositions contained in potato species, and provided a better classification sensitivity, specificity and accuracy (sensitivity: 1, specificity: 1 and accuracy: 1 for all 3 potato cultivars identification) among the three types of potatoes as compared to fluorescence spectroscopy.