SiMix: A domain generalization method for cross-site brain MRI harmonization via site mixing.

Brain magnetic resonance imaging (MRI) is widely used in clinical practice for disease diagnosis. However, MRI scans acquired at different sites can have different appearances due to the difference in the hardware, pulse sequence, and imaging parameter. It is important to reduce or eliminate such cross-site variations with brain MRI harmonization so that downstream image processing and analysis is performed consistently. Previous works on the harmonization problem require the data acquired from the sites of interest for model training. But in real-world scenarios there can be test data from a new site of interest after the model is trained, and training data from the new site is unavailable when the model is trained. In this case, previous methods cannot optimally handle the test data from the new unseen site. To address the problem, in this work we explore domain generalization for brain MRI harmonization and propose Site Mix (SiMix). We assume that images of travelling subjects are acquired at a few existing sites for model training. To allow the training data to better represent the test data from unseen sites, we first propose to mix the training images belonging to different sites stochastically, which substantially increases the diversity of the training data while preserving the authenticity of the mixed training images. Second, at test time, when a test image from an unseen site is given, we propose a multiview strategy that perturbs the test image with preserved authenticity and ensembles the harmonization results of the perturbed images for improved harmonization quality. To validate SiMix, we performed experiments on the publicly available SRPBS dataset and MUSHAC dataset that comprised brain MRI acquired at nine and two different sites, respectively. The results indicate that SiMix improves brain MRI harmonization for unseen sites, and it is also beneficial to the harmonization of existing sites.

Study on the epoxidation of chain olefins using biquaternary ammonium phosphotungstic acid phase transfer catalysts under no-solvent condition.

In this study, we have developed a novel catalyst synthesized by phosphotungstic acid and a gemini quaternary ammonium cation salt. This quaternary ammonium salt not only reduces the interfacial tension between olefins and hydrogen peroxide but also forms a notably stable structure with phosphotungstic acid. Dodecene was successfully epoxidized to epoxy dodecane with a selectivity of 82.9 %. The impact of initial conditions was systematically investigated such as molar ratio, temperature, reaction time, and catalyst dosage on the catalytic performance. Characterization of the catalyst morphology was performed by SEM, TEM and SAXS. Raman spectra, FT-IR and XPS spectra were employed to perform the catalyst transformation during the epoxidation reaction. This catalytic mechanism study could provide the industrial application in the epoxidation of long-chain olefins.

Interaction, Surface Activity, and Application of Mixed Systems of Alcohol Ether Sulfate Anionic Surfactants with Multiple Ethylene Oxide Groups and Gemini Quaternary Ammonium Surfactant.

The surface activities and application properties for the mixtures of cationic surfactants tetramethylene-1,4-bis[N,N-bis(hydroxypropyl)-hexa/decyloxypropylammonium] bromide (GC10-P) and tetramethylene-1,4-bis[N,N-bis(hydroxyethyl)-hexa/decyloxypropylammonium] bromide (GC10-E) and anionic surfactant isomeric sodium fatty alcohol ether sulfates (iso-AE9S) were investigated using both the tensiometry and the conductometry. The interaction parameters and thermodynamic micellization parameters of GC10-P/iso-AE9S and GC10-E/iso-AE9S mixtures were evaluated by Clint-Rubingh and Motomura theoretical models. When the mole fraction of α1 for GC10-P/iso-AE9S mixed system was 0.2, the critical micelle concentration (CMC) reached a minimum of 1.61 × 10-4 mol/L, and the minimum critical micelle concentration of the GC10-E/iso-AE9S mixed system is 2.67 × 10-5 mol/L at α1 = 0.6. The CMC value of the mixed system is 1-2 orders of magnitude lower than that of any single component. The results indicate that the synergistic effects of the investigated mixed systems (evaluated by ßm) are in order of GC10-P/iso-AE9S < GC10-E/iso-AE9S, with maximum ßm values of -17.98 and -9.78, respectively. The change in zeta potential indicates that the poly(ethylene oxide) chain has weakened the charge density of the hydrophilic headgroup of the anionic surfactant. The interfacial tension at the oil-water interface in the mixed system of anionic/cationic surfactants is lower than that of any single component, exhibiting a higher interfacial activity. The mixed system exhibits a decreased contact angle and superior wetting ability over any single component, and it also enhances foam performance, emulsification performance, and degreasing performance.

Evaluation of the consistency of the MRI- based AI segmentation cartilage model using the natural tibial plateau cartilage.

OBJECTIVE: The study aims to evaluate the accuracy of an MRI-based artificial intelligence (AI) segmentation cartilage model by comparing it to the natural tibial plateau cartilage. METHODS: This study included 33 patients (41 knees) with severe knee osteoarthritis scheduled to undergo total knee arthroplasty (TKA). All patients had a thin-section MRI before TKA. Our study is mainly divided into two parts: (i) In order to evaluate the MRI-based AI segmentation cartilage model's 2D accuracy, the natural tibial plateau was used as gold standard. The MRI-based AI segmentation cartilage model and the natural tibial plateau were represented in binary visualization (black and white) simulated photographed images by the application of Simulation Photography Technology. Both simulated photographed images were compared to evaluate the 2D Dice similarity coefficients (DSC). (ii) In order to evaluate the MRI-based AI segmentation cartilage model's 3D accuracy. Hand-crafted cartilage model based on knee CT was established. We used these hand-crafted CT-based knee cartilage model as gold standard to evaluate 2D and 3D consistency of between the MRI-based AI segmentation cartilage model and hand-crafted CT-based cartilage model. 3D registration technology was used for both models. Correlations between the MRI-based AI knee cartilage model and CT-based knee cartilage model were also assessed with the Pearson correlation coefficient. RESULTS: The AI segmentation cartilage model produced reasonably high two-dimensional DSC. The average 2D DSC between MRI-based AI cartilage model and the tibial plateau cartilage is 0.83. The average 2D DSC between the AI segmentation cartilage model and the CT-based cartilage model is 0.82. As for 3D consistency, the average 3D DSC between MRI-based AI cartilage model and CT-based cartilage model is 0.52. However, the quantification of cartilage segmentation with the AI and CT-based models showed excellent correlation (r = 0.725; P values < 0.05). CONCLUSION: Our study demonstrated that our MRI-based AI cartilage model can reliably extract morphologic features such as cartilage shape and defect location of the tibial plateau cartilage. This approach could potentially benefit clinical practices such as diagnosing osteoarthritis. However, in terms of cartilage thickness and three-dimensional accuracy, MRI-based AI cartilage model underestimate the actual cartilage volume. The previous AI verification methods may not be completely accurate and should be verified with natural cartilage images. Combining multiple verification methods will improve the accuracy of the AI model.

The influence of hydrogen bonding on the structure of organic-inorganic hybrid catalysts and its application in the solvent-free epoxidation of α-olefins.

In this study, two types of catalysts were prepared by the combination of gemini quaternary ammonium salt with two distinct species of phosphotungstic acid. Catalysts prepared by the Wells-Dawson type of phosphotungstic acid and Keggin-type phosphotungstic acid both exhibited dual-phase catalytic behavior, demonstrating both heterogeneous and homogeneous catalytic activities. In comparison to the catalyst prepared by the Keggin-type phosphotungstic acid, due to the higher size of Wells-Dawson type of phosphotungstic acid, hydrogen bonding could not effectively affect the catalyst prepared by H6P2W18O62. Subsequently, the influential factors on the catalytic reaction were investigated. Through the utilization of techniques such as XPS, FT-IR, Raman spectra and other characterization methods, two distinct structure and reaction mechanisms for these catalysts were elucidated under the influence of hydrogen bonding.

Adaptive Cylindrical Wireless Metasurfaces in Clinical Magnetic Resonance Imaging.

The signal-to-noise ratio (SNR) is one of the most important criteria for evaluating the image quality in magnetic resonance imaging (MRI), and metasurfaces with unique electromagnetic properties provide a novel method for SNR improvement. However, their applications in clinical MRI are highly restricted by the inhomogeneous enhancement of the magnetic field and interference in the radio frequency (RF) transmitting field. In this study, an adaptive cylindrical wireless metasurface (ACWM) with homogeneous field enhancement and adaptive resonant modes is reported. The ACWM automatically switches its resonant modes between the partial (transmitting period) and whole (receiving period) resonance, which enables it to not only eliminate the interference in RF transmitting field, but also greatly enhance the SNR. Its adaptability also makes the ACWM applicable to all common clinical sequences without any modifications in the scan parameters. The SNR of MRI images of the human wrist, acquired with ACWM, is two to four times compared with the conventional coil. This work offers a practical control method to fill the scientific knowledge gaps between the preclinical research and medical applications for metasurfaces, and suggests a novel and powerful tool for diagnosing and evaluating human diseases.