Frequency importance analysis for chemical exchange saturation transfer magnetic resonance imaging using permuted random forest.

Chemical exchange saturation transfer magnetic resonance imaging (CEST MRI) is a promising molecular imaging tool that allows sensitive detection of endogenous metabolic changes. However, because the CEST spectrum does not display a clear peak like MR spectroscopy, its signal interpretation is challenging, especially under 3-T field strength or with a large saturation B1 . Herein, as an alternative to conventional Z-spectral fitting approaches, a permuted random forest (PRF) method is developed to determine featured saturation frequencies for lesion identification, so-called CEST frequency importance analysis. Briefly, voxels in the CEST dataset were labeled as lesion and control according to multicontrast MR images. Then, by considering each voxel's saturation signal series as a sample, a permutation importance algorithm was employed to rank the contribution of saturation frequency offsets in the differentiation of lesion and normal tissue. Simulations demonstrated that PRF could correctly determine the frequency offsets (3.5 or -3.5 ppm) for classifying two groups of Z-spectra, under a range of B0 , B1 conditions and sample sizes. For ischemic rat brains, PRF only displayed high feature importance around amide frequency at 2 h postischemia, reflecting that the pH changes occurred at an early stage. By contrast, the data acquired at 24 h postischemia exhibited high feature importance at multiple frequencies (amide, water, and lipids), which suggested the complex tissue changes that occur during the later stages. Finally, PRF was assessed using 3-T CEST data from four brain tumor patients. By defining the tumor region on amide proton transfer-weighted images, PRF analysis identified different CEST frequency importance for two types of tumors (glioblastoma and metastatic tumor) (p < 0.05, with each image slice as a subject). In conclusion, the PRF method was able to rank and interpret the contribution of all acquired saturation offsets to lesion identification; this may facilitate CEST analysis in clinical applications, and open up new doors for comprehensive CEST analysis tools other than model-based approaches.

Solution Processing for Lateral Transition-Metal Dichalcogenides Homojunction from Polymorphic Crystal.

Homojunctions comprised of transition-metal dichalcogenides (TMD) polymorphs are attractive building blocks for next-generation two-dimensional (2D) electronic circuitry. However, the synthesis of such homojunctions, which usually involves elaborate manipulation at the nanoscale, still remains a great challenge. Herein, we demonstrated a solution-processing strategy to successfully harvest lateral semiconductor-metal homojunctions with high yield. Specially, through precisely controlled lithiation process, precursors of polymorphic crystal arranged with 1T-2H domains were successfully achieved. A programmed exfoliation procedure was further employed to orderly laminate each phase in the polymorphic crystal, thus leading to 1T-2H TMD homojunction monolayers with sizes up to tens of micrometers. Moreover, the atomically sharp boundaries and superior band alignment improved the device on the basis of the semiconductor-metal homojunction with 50% decrease of electric field strength required in the derivation of state transition. We anticipate that solution processing based on programmed exfoliation would be a powerful tool to produce new configurations of 2D nanomaterials.

Molecule-Confined Engineering toward Superconductivity and Ferromagnetism in Two-Dimensional Superlattice.

Superconductivity is mutually exclusive with ferromagnetism, because the ferromagnetic exchange field is often destructive to superconducting pairing correlation. Well-designed chemical and physical methods have been devoted to realize their coexistence only by structural integrity of inherent superconducting and ferromagnetic ingredients. However, such coexistence in freestanding structure with nonsuperconducting and nonferromagnetic components still remains a great challenge up to now. Here, we demonstrate a molecule-confined engineering in two-dimensional organic-inorganic superlattice using a chemical building-block approach, successfully realizing first freestanding coexistence of superconductivity and ferromagnetism originated from electronic interactions of nonsuperconducting and nonferromagnetic building blocks. We unravel totally different electronic behavior of molecules depending on spatial confinement: flatly lying Co(Cp)2 molecules in strongly confined SnSe2 interlayers weaken the coordination field, leading to spin transition to form ferromagnetism; meanwhile, electron transfer from cyclopentadienyls to the Se-Sn-Se lattice induces superconducting state. This entirely new class of coexisting superconductivity and ferromagnetism generates a unique correlated state of Kondo effect between the molecular ferromagnetic layers and inorganic superconducting layers. We anticipate that confined molecular chemistry provides a newly powerful tool to trigger exotic chemical and physical properties in two-dimensional matrixes.

Hydrogen Treatment for Superparamagnetic VO2 Nanowires with Large Room-Temperature Magnetoresistance.

One-dimensional (1D) transition metal oxide (TMO) nanostructures are actively pursued in spintronic devices owing to their nontrivial d electron magnetism and confined electron transport pathways. However, for TMOs, the realization of 1D structures with long-range magnetic order to achieve a sensitive magnetoelectric response near room temperature has been a longstanding challenge. Herein, we exploit a chemical hydric effect to regulate the spin structure of 1D V-V atomic chains in monoclinic VO2 nanowires. Hydrogen treatment introduced V(3+) (3d(2) ) ions into the 1D zigzag V-V chains, triggering the formation of ferromagnetically coupled V(3+) -V(4+) dimers to produce 1D superparamagnetic chains and achieve large room-temperature negative magnetoresistance (-23.9 %, 300 K, 0.5 T). This approach offers new opportunities to regulate the spin structure of 1D nanostructures to control the intrinsic magnetoelectric properties of spintronic materials.

Multi-target reconstruction based on subspace decision optimization for bioluminescence tomography.

BACKGROUND AND OBJECTIVE: Bioluminescence tomography (BLT) is a noninvasive optical imaging technique that provides qualitative and quantitative information on the spatial distribution of tumors in living animals. Researchers have proposed a list of algorithms and strategies for BLT reconstruction to improve its reconstruction quality. However, multi-target BLT reconstruction remains challenging in practical clinical applications due to the mutual interference of optical signals and difficulty in source separation. METHODS: To solve this problem, this study proposes the subspace decision optimization (SDO) approach based on the traditional iterative permissible region strategy. The SDO approach transforms a single permissible region into multiple subspaces by clustering analysis. These subspaces are shrunk based on subspace shrinking optimization to achieve spatial continuity of the permissible regions. In addition, these subspaces are merged to construct a new permissible region and then the next iteration of reconstruction is carried out to ensure the stability of the results. Finally, all the iterative results are optimized based on the normal distribution model and the distribution properties of the targets to ensure the sparsity of each target and the non-biasing of the overall results. RESULTS: Experimental results show that the SDO approach can automatically identify and separate different targets, ensuring the accuracy and quality of multi-target BLT reconstruction results. Meanwhile, SDO can combine various types of reconstruction algorithms and provide stable and high-quality reconstruction results independent of the algorithm parameters. CONCLUSIONS: The SDO approach provides an integrated solution to the multi-target BLT reconstruction problem, realizing the whole process including target recognition, separation, reconstruction, and result enhancement, which can extend the application domain of BLT.

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) quantification of transient ischemia using a combination method of 5-pool Lorentzian fitting and inverse Z-spectrum analysis.

Background: Chemical exchange saturation transfer (CEST) is a promising method for the detection of biochemical alterations in cancers and neurological diseases. However, the sensitivity of the currently existing quantitative method for detecting ischemia needs further improvement. Methods: To further improve the quantification of the CEST signal and enhance the CEST detection for ischemia, we used a quantitative analysis method that combines an inverse Z-spectrum analysis and a 5-pool Lorentzian fitting. Specifically, a 5-pool Lorentzian simulation was conducted with the following brain tissue parameters: water, amide (3.5 ppm), amine (2.2 ppm), magnetization transfer (MT), and nuclear Overhauser enhancement (NOE; -3.5 ppm). The parameters were first calculated offline and stored as the initial value of the Z-spectrum fitting. Then, the measured Z-spectrum with the peak value set to 0 was fitted via the stored initial value, which yielded the reference Z-spectrum. Finally, the difference between the inverse of the Z-spectrum and the inverse of the reference Z-spectrum was used as the CEST definite spectrum. Results: The simulation results demonstrated that the Z-spectra of the rat brain were well simulated by a 5-pool Lorentzian fitting. Further, the proposed method detected a larger difference than did either the saturation transfer difference or the 5-pool Lorentzian fitting, as demonstrated by simulations. According to the results of the cerebral ischemia rat model, the proposed method provided the highest contrast-to-noise ratio (CNR) between the contralateral and the ipsilateral striatum under various acquisition conditions. The results indicated that the difference of fitted amplitudes generated with a 5-pool Lorentzian fitting in amide at 3.5 ppm (6.04%±0.39%; 6.86%±0.39%) was decreased in a stroke lesion compared to the contralateral normal tissue. Moreover, the difference of the residual of inversed Z-spectra in which 5-pool Lorentzian fitting was used to calculate the reference Z-spectra ( M T R R e x 5 L ) amplitudes in amide at 3.5 ppm (13.83%±2.20%, 15.69%±1.99%) was reduced in a stroke lesion compared to the contralateral normal tissue. Conclusions: M T R R e x 5 L is predominantly pH-sensitive and is suitable for detecting tissue acidosis following an acute stroke.

Ultralow In-Plane Thermal Conductivity in 2D Magnetic Mosaic Superlattices for Enhanced Thermoelectric Performance.

Lowering thermal conductivity via introducing heterointerfaces of heterophase fillings (HPFs) is a common strategy for optimizing thermoelectric performance, but it is always accompanied by deterioration of electrical conductivity. Here we report an ordered magnetic HPF system in a CoSe2-SnSe mosaic heterostructure superlattice synthesized by van der Waals confined epitaxial growth (vdWCEG), which realizes a maximized filling amount to decrease in-plane thermal conductivity of SnSe layers and maintain the intact in-plane carrier transport path. The in-plane thermal conductivity of CoSe2-SnSe superlattice reaches the lowest range among SnSe-based materials with a value of 0.27 W m-1 K-1 at 850 K, which can be attributed to abundant interfaces between CoSe2 nanocrystals and SnSe layers. Moreover, the CoSe2 nanocrystals show superparamagnetic behavior, by which the rotation of magnetic domains provides additional phonon scattering to further decrease in-plane thermal conductivity. By combination with the preserved in-plane electrical conductivity of SnSe layers, an enhanced in-plane ZT value of 0.62 is achieved at 850 K. This vdWCEG approach can also be generally applied to fabricate various other two-dimensional (2D) mosaic heterostructures, providing an avenue for artificial 2D heterostructures with desired functionalities.

Application of a Markov chain Monte Carlo method for robust quantification in chemical exchange saturation transfer magnetic resonance imaging.

Background: Chemical exchange saturation transfer (CEST) magnetic resonance imaging can provide surrogate biomarkers for disease diagnosis. However, endogenous CEST effects are always diluted and contaminated by competing effects, which results in unwanted signal contributions that lessen the specificity of CEST to underlying biochemical exchange processes. The aim of this study was to examine a method for the accurate quantification of CEST effects. Methods: A Markov chain Monte Carlo (MCMC)-based Bayesian inference approach was proposed to estimate the exchange parameters, and CEST effects could be fitted using these estimations. This approach was tested in Bloch simulation and ischemic stroke rat experiments, and its performance was evaluated using quantification maps and numerical metrics. Results: With 12 groups of simulations, the MCMC method achieved satisfactory fittings on both 2-pool and 5-pool models. The sum of squares error values and the root mean square error of the fitted Z-spectra were smaller than 10-3, and the coefficient of determination (R-squared) values were close to 1. The corresponding CEST quantification M T R R e x C E S T spectra were also well fitted and successfully separated the mixed CEST effects. The estimated parameters showed little bias relative to the ground truth, with errors between the true and estimated values of each parameter of less than 0.5%. In the animal experiments, M T R R e x A m i d e fitted using the MCMC method showed obvious contrast between ischemic and contralateral regions at the early stage. Compared with other quantification methods, it displayed the highest contrast-to-noise ratios (3.9, 2.73, and 3.93) and the lowest coefficient of variation values (0.181, 0.2224, and 0.2897) in all three stroke periods. Conclusions: The MCMC method provided an efficient approach for parameter estimation and CEST effect quantification. The method may therefore be useful in achieving an accurate pathological diagnosis.

A fully convolutional network (FCN) based automated ischemic stroke segment method using chemical exchange saturation transfer imaging.

BACKGROUND: Chemical exchange saturation transfer (CEST) MRI is a promising imaging modality in ischemic stroke detection due to its sensitivity in sensing postischemic pH alteration. However, the accurate segmentation of pH-altered regions remains difficult due to the complicated sources in water signal changes of CEST MRI. Meanwhile, manual localization and quantification of stroke lesions are laborious and time-consuming, which cannot meet the urgent need for timely therapeutic interventions. PURPOSE: The goal of this study was to develop an automatic lesion segmentation approach of the ischemic region based on CEST MR images. A novel segmentation framework based on the fully convolutional neural network was investigated in our task. METHODS: Z-spectra from 10 rats were manually labeled as ground truth and split into two datasets, where the training dataset including 3 rats was used to generate a segmentation model, and the remaining rats were used as test datasets to evaluate the model's performance. Then a 1D fully convolutional neural network equipped with bottleneck structures was set up, and a Grad-CAM approach was used to produce a coarse localization map, which can reflect the relevancy to the "ischemia" class of each pixel. RESULTS: As compared with the ground truth, the proposed network model achieved satisfying segmentation results with high values of evaluation metrics including specificity (SPE), sensitivity (SEN), accuracy (ACC), and Dice similarity coefficient (DSC), especially in some intractable situations where conventional MRI modalities and CEST quantitative method failed to distinguish between ischemic and normal tissues; also the model with augmentation was robust to input perturbations. The Grad-CAM maps performed clear tissue change distributions and interpreted the segmentations, showed a strong correlation with the quantitative method, and gave extended thinking to the function of networks. CONCLUSIONS: The proposed method can segment ischemia region from CEST images, with the Grad-CAM maps giving access to interpretative information about the segmentations, which demonstrates great potential in clinical routines.

Voxel-wise Optimization of Pseudo Voigt Profile (VOPVP) for Z-spectra fitting in chemical exchange saturation transfer (CEST) MRI.

BACKGROUND: Chemical exchange saturation transfer (CEST) MRI is a promising approach for detecting biochemical alterations in cancers and neurological diseases, but the quantification can be challenging. Among numerous quantification methods, Lorentzian difference (LD) is relatively simple and widely used, which employs Lorentzian line-shape as a reference to describe the direct saturation (DS) of water and takes account of difference against experimental CEST spectra data. However, LD often overestimates CEST and nuclear overhauser enhancement (NOE) effects. Specifically, for fast-exchanging CEST species require higher saturation power (B1_sat) or in the presence of strong magnetization transfer (MT) contrast, Z-spectrum appears more like a Gaussian line-shape rather than a Lorentzian line-shape. METHODS: To improve the conventional LD analysis, the present study developed and validated a novel fitting algorithm through a linear combination of Gaussian and Lorentzian function as the reference spectra, namely, Voxel-wise Optimization of Pseudo Voigt Profile (VOPVP). The experimental Z-spectra were pre-fitted with Gaussian and Lorentzian method independently, in order to determine Lorentzian proportionality coefficient (a). To further compensate for the line-shape changes under different B1_sat's, a B1-dependent adjustment was applied to the experimental Z-spectra (Z_exp) according to the prior knowledge learned from 5-pool Bloch equation-based simulations at a range of B1_sat's. Then, the obtained Z-spectra (Z_B1adj) was fitted by the previously defined VOPVP function. Considering the asymmetric component of MT, the positive- and negative-side of Z-spectra were fitted separately, while the middle part (-0.6 to 0.6 ppm, consisted primarily of DS) was fitted using Lorentzian function. Finally, the difference between Z_VOPVP and Z_exp was defined as the CEST and NOE contrast. To validate our VOPVP method, an extensive simulation of CEST Z-spectra was performed using 5-pool model and 6-pool model with greater MT component. RESULTS: In comparison with LD approach, VOPVP exhibited lower sum of squares due to error (SSE) and higher goodness of fit (R-square) for the experimental Z-spectra at all B1_sat. Moreover, the results indicated that VOPVP fitting improved the overestimated contributions from amide proton transfer (APT) and NOE through LD at all B1_sat. Despite that the relationship for B1-dependent adjustment was pre-determined using a single 5-pool model, the VOPVP fittings obtained accurate quantification for multiple 6-pool models with a range of T1w's and T2w's. The robustness of VOPVP fitting was also proved by simulations using 3T parameters. Furthermore, we assessed VOPVP in vivo in a glioblastoma-bearing mouse. Compared to LD maps, VOPVP quantification maps displayed higher contrast-to-noise ratio between tumor and normal contralateral tissue for APT, glutamate and nuclear overhauser effect (NOE), when B1_sat >1 µT. CONCLUSIONS: As an improvement of LD method, VOPVP fitting can serve as a simple, robust and more accurate approach for quantifying CEST and NOE contrast.

Motion correction of chemical exchange saturation transfer MRI series using robust principal component analysis (RPCA) and PCA.

BACKGROUND: Chemical exchange saturation transfer (CEST) MRI requires the acquisition of multiple saturation-weighted images and can last several minutes. Misalignments among these images, which are often due to the inevitable motion of the subject, will corrupt CEST contrast maps and result in large quantification errors. Therefore, the registration of the CEST series is critical. However, registration is challenging since common intensity-based registration algorithms may fail to differentiate CEST signals from motion artifacts. Herein, we studied how different patterns of motion affect CEST quantification and proposed a cascaded two-step registration scheme by utilizing features extracted from the entire Z-spectral image series instead of direct registration to a single image. METHODS: The proposed approach is conducted in two stages: during the first coarse registration, the Z-spectral image series is decomposed by robust principal component analysis (RPCA) to separate CEST contrast from motion. The recomposed image series using only the low-rank component, which contains minimized motion, are averaged to generate a reference for the alignment of the image series. To further remove residual misalignments, the coarse registration is followed by a refinement stage, which uses PCA iteratively to generate motionless synthetic reference series with the first few principal components (PCs) that correspond to CEST contrast. In the end, the quality check is performed to exclude the images with unsuccessful registration. RESULTS: The proposed registration scheme (RPCA + PCA_R) was assessed by both phantom experiments and in vivo data of tumor-bearing mouse brain, with simulated random rigid motion in different patterns applied to the acquired static Z-spectral image series. For comparison, previous correction schemes using an explicit image [either S0 or Ssat(∆ω)] as registration reference were also performed, named as S0_R and Ssat_R respectively. To illustrate the advantage of combination of RPCA and PCA, registration was also exploited using either only the RPCA-based method (RPCA_R) or only the PCA-based one (PCA_R). Compared with the above four methods, RPCA + PCA_R allowed for more accurate correction of the corrupted Z-spectral images, exhibiting smaller MTRasym(∆ω) error maps and lower residual Z-spectra referring to the static data. Among all the five correction methods, the corrected Z-spectral image series by RPCA + PCA_R and the resulting MTRasym(∆ω) maps achieved the highest correlation coefficients (CC) with respect to the static ones. CONCLUSIONS: The registration scheme of RPCA + PCA_R provides robust motion correction between two specific Z-spectral images and among an entire image series, through extraction of the static component from the entire Z-spectra set and inclusion of a PCA-based refinement step. Therefore, this method can help improve CEST acquisition and quantification.

Giant Negative Electrocaloric Effect in Anti-Ferroelectric (Pb0.97La0.02)(Zr0.95Ti0.05)O3 Ceramics.

A giant electrocaloric effect is reported in (Pb0.97La0.02)(Zr0.95Ti0.05)O3 anti-ferroelectric ceramics. These samples were fabricated by a solid-state mixed oxide technique. Dielectric analyses were employed to investigate the anti-ferroelectric (AFE) and ferroelectric (FE) phase transitions of the sample. During the heating process, the phase transition from the orthorhombic anti-ferroelectric phase (AFEO) to the tetragonal anti-ferroelectric phase (AFET) occurs at 155 °C, and the phase transition from AFET to PE occurs at 225 °C. Using the Maxwell relationship, the entropy change ΔS and adiabatic temperature change ΔT were obtained at different electric fields ranging from 40 to 65 kV/cm. The maximum adiabatic temperature change (ΔT max = -7.47 K) was obtained at 50 kV/cm, which was attributed to the field-induced phase transformation between the anti-ferroelectric and ferroelectric phases. These results showed that PLZT2/95/5 ceramics possess a large negative electrocaloric effect value, which could be applied in achieving cooling power as refrigerants.

Imaging metal-like monoclinic phase stabilized by surface coordination effect in vanadium dioxide nanobeam.

In correlated systems, intermediate states usually appear transiently across phase transitions even at the femtosecond scale. It therefore remains an open question how to determine these intermediate states-a critical issue for understanding the origin of their correlated behaviour. Here we report a surface coordination route to successfully stabilize and directly image an intermediate state in the metal-insulator transition of vanadium dioxide. As a prototype metal-insulator transition material, we capture an unusual metal-like monoclinic phase at room temperature that has long been predicted. Coordinate bonding of L-ascorbic acid molecules with vanadium dioxide nanobeams induces charge-carrier density reorganization and stabilizes metallic monoclinic vanadium dioxide, unravelling orbital-selective Mott correlation for gap opening of the vanadium dioxide metal-insulator transition. Our study contributes to completing phase-evolution pathways in the metal-insulator transition process, and we anticipate that coordination chemistry may be a powerful tool for engineering properties of low-dimensional correlated solids.