Simultaneous grading diagnosis of liver fibrosis, inflammation, and steatosis using multimodal quantitative ultrasound and artificial intelligence framework.

Noninvasive, accurate, and simultaneous grading of liver fibrosis, inflammation, and steatosis is valuable for reversing the progression and improving the prognosis quality of chronic liver diseases (CLDs). In this study, we established an artificial intelligence framework for simultaneous grading diagnosis of these three pathological types through fusing multimodal tissue characterization parameters dug by quantitative ultrasound methods derived from ultrasound radiofrequency signals, B-mode images, shear wave elastography images, and clinical ultrasound systems, using the liver biopsy results as the classification criteria. One hundred forty-two patients diagnosed with CLD were enrolled in this study. The results show that for the classification of fibrosis grade ≥ F1, ≥ F2, ≥ F3, and F4, the highest AUCs were respectively 0.69, 0.82, 0.84, and 0.88 with single clinical indicator alone, and were 0.81, 0.83, 0.89, and 0.91 with the proposed method. For the classification of inflammation grade ≥ A2 and A3, the highest AUCs were respectively 0.66 and 0.76 with single clinical indicator alone and were 0.80 and 0.93 with the proposed method. For the classification of steatosis grade ≥ S1 and ≥ S2, the highest AUCs were respectively 0.71 and 0.90 with single clinical indicator alone and were 0.75 and 0.92 with the proposed method. The proposed method can effectively improve the grading diagnosis performance compared with the present clinical indicators and has potential applications for noninvasive, accurate, and simultaneous diagnosis of CLDs.

Ultrafine CuO/graphene oxide cellulose nanocomposites with complementary framework for polycyclic aromatic hydrocarbon pollutants removal.

Efficient and sustainable methods for eliminating polycyclic aromatic hydrocarbon pollutants (PAHPs) are in highly desired. Proven technologies involve physical and chemical reactions that absorb PAHPs, however they encounter formidable challenges. Here, a bottom-up refining-grain strategy is proposed to rationally design ultrafine CuO/graphene oxide-cellulose nanocomposites (LCelCCu) with a mirror-like for tetracycline (TC) to substantially improve the efficient of the purification process by active integrated-sorption. The LCelCCu captures TC with a higher affinity and lower energy demand, as determined by sorption kinetic, isotherms, thermodynamics, and infrared and X-ray Photoelectron Spectroscopy. The resulting material could achieve ultra-high sorption capacity (2775.23 mg/g), kinetic (1.2499 L g-1 h-1) and high selectivity (up to 99.9 %) for TC, nearly surpassing all recent adsorbents. This study simultaneously unveils the pioneering role of simultaneous multi-site match sorption and subsequent advanced oxidation synergistically, fundamentally enhancing understanding of the structure-activity-selectivity relationship and inspires more sustainable water purification applications and broader material design considerations.

[Reconstruction of elasticity modulus distribution base on semi-supervised neural network].

Accurate reconstruction of tissue elasticity modulus distribution has always been an important challenge in ultrasound elastography. Considering that existing deep learning-based supervised reconstruction methods only use simulated displacement data with random noise in training, which cannot fully provide the complexity and diversity brought by in-vivo ultrasound data, this study introduces the use of displacement data obtained by tracking in-vivo ultrasound radio frequency signals (i.e., real displacement data) during training, employing a semi-supervised approach to enhance the prediction accuracy of the model. Experimental results indicate that in phantom experiments, the semi-supervised model augmented with real displacement data provides more accurate predictions, with mean absolute errors and mean relative errors both around 3%, while the corresponding data for the fully supervised model are around 5%. When processing real displacement data, the area of prediction error of semi-supervised model was less than that of fully supervised model. The findings of this study confirm the effectiveness and practicality of the proposed approach, providing new insights for the application of deep learning methods in the reconstruction of elastic distribution from in-vivo ultrasound data.

Improved Ultrafast Power Doppler Imaging Using United Spatial-Angular Adaptive Scaling Wiener Postfilter.

Ultrafast power Doppler imaging (uPDI) using high-frame-rate plane-wave transmission is a new microvascular imaging modality that offers high Doppler sensitivity. However, due to the unfocused transmission of plane waves, the echo signal is subject to interference from noise and clutter, resulting in a low signal-to-noise ratio (SNR) and poor image quality. Adaptive beamforming techniques are effective in suppressing noise and clutter for improved image quality. In this study, an adaptive beamformer based on a united spatial-angular adaptive scaling Wiener (uSA-ASW) postfilter is proposed to improve the resolution and contrast of uPDI. In the proposed method, the signal power and noise power of the Wiener postfilter are estimated by uniting spatial and angular signals, and a united generalized coherence factor (uGCF) is introduced to dynamically adjust the noise power estimation and enhance the robustness of the method. Simulation and in vivo data were used to verify the effectiveness of the proposed method. The results show that the uSA-ASW can achieve higher resolution and significant improvements in image contrast and background noise suppression compared with conventional delay-and-sum (DAS), coherence factor (CF), spatial-angular CF (SACF), and adaptive scaling Wiener (ASW) postfilter methods. In the simulations, uSA-ASW improves contrast-to-noise ratio (CNR) by 34.7 dB (117.3%) compared with DAS, while reducing background noise power (BNP) by 52 dB (221.4%). The uSA-ASW method provides full-width at half-maximum (FWHM) reductions of [Formula: see text] (59.5%) and [Formula: see text] (56.9%), CNR improvements of 25.6 dB (199.9%) and 42 dB (253%), and BNP reductions of 46.1 dB (319.3%) and 12.9 dB (289.1%) over DAS in the experiments of contrast-free human neonatal brain and contrast-free human liver, respectively. In the contrast-free experiments, uSA-ASW effectively balances the performance of noise and clutter suppression and enhanced microvascular visualization. Overall, the proposed method has the potential to become a reliable microvascular imaging technique for aiding in more accurate diagnosis and detection of vascular-related diseases in clinical contexts.

High-Quality Ultrafast Power Doppler Imaging Based on Spatial Angular Coherence Factor.

The morphological and hemodynamic changes of microvessels are demonstrated to be related to the diseased conditions in tissues. Ultrafast power Doppler imaging (uPDI) is a novel modality with a significantly increased Doppler sensitivity, benefiting from the ultrahigh frame rate plane-wave imaging (PWI) and advanced clutter filtering. However, unfocused plane-wave transmission often leads to a low imaging quality, which degrades the subsequent microvascular visualization in power Doppler imaging. Coherence factor (CF)-based adaptive beamformers have been widely studied in conventional B-mode imaging. In this study, we propose a spatial and angular coherence factor (SACF) beamformer for improved uPDI (SACF-uPDI) by calculating the spatial CF across apertures and the angular CF across transmit angles, respectively. To identify the superiority of SACF-uPDI, simulations, in vivo contrast-enhanced rat kidney, and in vivo contrast-free human neonatal brain studies were conducted. Results demonstrate that SACF-uPDI can effectively enhance contrast and resolution and suppress background noise simultaneously, compared with conventional uPDI methods based on delay-and-sum (DAS) (DAS-uPDI) and CF (CF-uPDI). In the simulations, SACF-uPDI can improve the lateral and axial resolutions compared with those of DAS-uPDI, from 176 to [Formula: see text] of lateral resolution, and from 111 to [Formula: see text] of axial resolution. In the in vivo contrast-enhanced experiments, SACF achieves 15.14- and 5.6-dB higher contrast-to-noise ratio (CNR), 15.25- and 3.68-dB lower noise power, and 240- and 15- [Formula: see text] narrower full-width at half-maximum (FWHM) than DAS-uPDI and CF-uPDI, respectively. In the in vivo contrast-free experiments, SACF achieves 6.11- and 1.09-dB higher CNR, 11.93- and 4.01-dB lower noise power, and 528- and 160- [Formula: see text] narrower FWHM than DAS-uPDI and CF-uPDI, respectively. In conclusion, the proposed SACF-uPDI method can efficiently improve the microvascular imaging quality and has the potential to facilitate clinical applications.

Convolutional Neural Network-Based Speckle Tracking for Ultrasound Strain Elastography: An Unsupervised Learning Approach.

Accurate and computationally efficient motion estimation is a critical component of real-time ultrasound strain elastography (USE). With the advent of deep-learning neural network models, a growing body of work has explored supervised convolutional neural network (CNN)-based optical flow in the framework of USE. However, the above-said supervised learning was often done using simulated ultrasound data. The research community has questioned whether simulated ultrasound data containing simple motion can train deep-learning CNN models that can reliably track complex in vivo speckle motion. In parallel with other research groups' efforts, this study developed an unsupervised motion estimation neural network (UMEN-Net) for USE by adapting a well-established CNN model named PWC-Net. Our network's input is a pair of predeformation and postdeformation radio frequency (RF) echo signals. The proposed network outputs both axial and lateral displacement fields. The loss function consists of a correlation between the predeformation signal and the motion-compensated postcompression signal, smoothness of the displacement fields, and tissue incompressibility. Notably, an innovative correlation method known as the globally optimized correspondence (GOCor) volumes module developed by Truong et al. was used to replace the original Corr module to enhance our evaluation of signal correlation. The proposed CNN model was tested using simulated, phantom, and in vivo ultrasound data containing biologically confirmed breast lesions. Its performance was compared against other state-of-the-art methods, including two deep-learning-based tracking methods (MPWC-Net++ and ReUSENet) and two conventional tracking methods (GLUE and BRGMT-LPF). In summary, compared with the four known methods mentioned above, our unsupervised CNN model not only obtained higher signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) for axial strain estimates but also improved the quality of the lateral strain estimates.

[The developments and applications of functional ultrasound imaging].

In recent years, due to the emergence of ultrafast ultrasound imaging technology, the sensitivity of detecting slow and micro blood flow with ultrasound has been dramatically improved, and functional ultrasound imaging (fUSI) has been developed. fUSI is a novel technology for neurological imaging that utilizes neurovascular coupling to detect the functional activity of the central nervous system (CNS) with high spatiotemporal resolution and high sensitivity, which is dynamic, non-invasive or minimally invasive. fUSI fills the gap between functional magnetic resonance imaging (fMRI) and optical imaging with its high accessibility and portability. Moreover, it is compatible with electrophysiological recording and optogenetics. In this paper, we review the developments of fUSI and its applications in neuroimaging. To date, fUSI has been used in various animals ranging from mice to non-human primates, as well as in clinical surgeries and bedside functional brain imaging of neonates. In conclusion, fUSI has great potential in neuroscience research and is expected to become an important tool for neuroscientists, pathologists and pharmacologists.

Influence of key parameters on motion artifacts in lateral strain estimation with spatial angular compounding.

Strain imaging can reveal the changes in tissue mechanical properties related to pathological alterations by estimating tissue strains in the lateral and axial directions of ultrasound imaging. The estimation performance in the lateral direction is usually worse than that in the axial direction. Spatial angular compounding (SAC) has been demonstrated to improve the quality of lateral estimation by deriving the lateral displacements using axial displacements obtained from multi-angle transmissions. However, motion and deformation of tissues during multiple transmissions may cause motion artifacts, and thus deteriorate the quality of strain estimation. These artifacts can be reduced by choosing appropriate imaging parameters. However, few studies have been conducted to evaluate the influences of key parameters in strain estimation, such as the pulse repetition frequency (PRF), the number of steering angles (NSA), and the maximum steering angles (MSA), in terms of performance optimization. Therefore, this study aims to investigate the effects of these parameters through simulations and phantom experiments. The performance of strain estimation is evaluated by measuring the root-mean-square error (RMSE) and the standard deviation (SD) in the simulations and phantom experiments, respectively. The contrast-to-noise ratio (CNR) of strain images is calculated in both the simulations and phantom experiments. The results show that motion artifacts in strain estimation can be reduced by increasing the PRF to 1 kHz. When the PRF reaches 1 kHz, further increase of the PRF shows little obvious improvement in strain estimation. An increase in the NSA can cause larger motion artifacts and deteriorate the quality of strain images, and the improvement of strain estimation is limited when the NSA is increased from 3 to 7. An NSA of 3 is thus recommended to balance the influences of motion artifacts and the improvement for strain estimation. The MSA has little influence on the motion artifacts, while increased MSA can achieve improved lateral estimation performance at the cost of a smaller imaging region. In light of the lateral strain estimation performance and imaging region, an MSA of 15° is recommended. The influences of these key parameters obtained from this study may provide insights for parameter optimization in strain estimation with SAC to minimize the effects of motion artifacts.

Unsupervised Convolutional Neural Network for Motion Estimation in Ultrasound Elastography.

High-quality motion estimation is essential for ultrasound elastography (USE). Traditional motion estimation algorithms based on speckle tracking such as normalized cross correlation (NCC) or regularization such as global ultrasound elastography (GLUE) are time-consuming. In order to reduce the computational cost and ensure the accuracy of motion estimation, many convolutional neural networks have been introduced into USE. Most of these networks such as radio-frequency modified pyramid, warping and cost volume network (RFMPWC-Net) are supervised and need many ground truths as labels in network training. However, the ground truths are laborious to collect for USE. In this study, we proposed a MaskFlownet-based unsupervised convolutional neural network (MF-UCNN) for fast and high-quality motion estimation in USE. The inputs to MF-UCNN are the concatenation of RF, envelope, and B-mode images before and after deformation, while the outputs are the axial and lateral displacement fields. The similarity between the predeformed image and the warped image (i.e., the postdeformed image compensated by the estimated displacement fields) and the smoothness of the estimated displacement fields were incorporated in the loss function. The network was compared with modified pyramid, warping and cost volume network (MPWC-Net)++, RFMPWC-Net, GLUE, and NCC. Results of simulations, breast phantom, and in vivo experiments show that MF-UCNN obtains higher signal-to-noise ratio (SNR) and higher contrast-to-noise ratio (CNR). MF-UCNN achieves high-quality motion estimation with significantly reduced computation time. It is unsupervised and does not need any ground truths as labels in the training, and, thus, has great potential for motion estimation in USE.

Unsupervised Histological Image Registration Using Structural Feature Guided Convolutional Neural Network.

Registration of multiple stained images is a fundamental task in histological image analysis. In supervised methods, obtaining ground-truth data with known correspondences is laborious and time-consuming. Thus, unsupervised methods are expected. Unsupervised methods ease the burden of manual annotation but often at the cost of inferior results. In addition, registration of histological images suffers from appearance variance due to multiple staining, repetitive texture, and section missing during making tissue sections. To deal with these challenges, we propose an unsupervised structural feature guided convolutional neural network (SFG). Structural features are robust to multiple staining. The combination of low-resolution rough structural features and high-resolution fine structural features can overcome repetitive texture and section missing, respectively. SFG consists of two components of structural consistency constraints according to the formations of structural features, i.e., dense structural component and sparse structural component. The dense structural component uses structural feature maps of the whole image as structural consistency constraints, which represent local contextual information. The sparse structural component utilizes the distance of automatically obtained matched key points as structural consistency constraints because the matched key points in an image pair emphasize the matching of significant structures, which imply global information. In addition, a multi-scale strategy is used in both dense and sparse structural components to make full use of the structural information at low resolution and high resolution to overcome repetitive texture and section missing. The proposed method was evaluated on a public histological dataset (ANHIR) and ranked first as of Jan 18th, 2022.

Improved Ultrafast Power Doppler Imaging by Using Spatiotemporal Non-Local Means Filtering.

The change of microvasculature is associated with the occurrence and development of many diseases. Ultrafast power Doppler imaging (uPDI) is an emerging technology for the visualization of microvessels due to the development of ultrafast plane wave (PW) imaging and advanced clutter filters. However, the low signal-to-noise ratio (SNR) caused by unfocused transmit of PW imaging deteriorates the subsequent imaging of microvasculature. Nonlocal means (NLM) filtering has been demonstrated to be effective in the denoising of both natural and medical images, including ultrasound power Doppler images. However, the feasibility and performance of applying an NLM filter on the ultrasound radio frequency (RF) data have not been investigated so far. In this study, we propose to apply an NLM filter on the spatiotemporal domain of clutter filtered blood flow RF data (St-NLM) to improve the quality of uPDI. Experiments were conducted to compare the proposed method with three different methods (under various similarity window sizes), including conventional uPDI without NLM filtering (Non-NLM), NLM filtering on the obtained power Doppler images (PD-NLM), and NLM filtering on the spatial domain of clutter filtered blood flow RF data (S-NLM). Phantom experiments, in vivo contrast-enhanced human spinal cord tumor experiments, and in vivo contrast-free human liver experiments were performed to demonstrate the superiority of the proposed St-NLM method over the other three methods. Qualitative and quantitative results show that the proposed St-NLM method can effectively suppress the background noise, improve the contrast between vessels and background, and preserve the details of small vessels at the same time. In the human liver study, the proposed St-NLM method achieves 31.05-, 24.49-, and 11.15-dB higher contrast-to-noise ratios (CNRs) and 36.86-, 36.86-, and 15.22-dB lower noise powers than Non-NLM, PD-NLM, and S-NLM, respectively. In the human spinal cord tumor, the full-width at half-maximums (FWHMs) of vessel cross Section are 76, 201, and [Formula: see text] for St-NLM, Non-NLM, and S-NLM, respectively. The proposed St-NLM method can enhance the microvascular visualization in uPDI and has the potential for the diagnosis of many microvessel-change-related diseases.

Comparative preservation effect of water-soluble and insoluble chitosan from Tenebrio molitor waste.

Edible films and coatings have been developed based on numerous natural biopolymers, which have been used to increase fresh-cut fruit shelf life. Here, we present the preparation, characteristics and preservation effect of water-soluble chitosan (WSC) and water-insoluble chitosan (WIC) from Tenebrio molitor waste (TMW) on fresh-cut apple slices. WIC was isolated from TMW in four steps and WSC was obtained from the WIC solution by 8% H2O2 treatment at 40 °C for 3 h. WIC and WSC were characterized by molecular weight, Fourier transform infrared spectroscopy (FTIR), morphology analysis, etc. The preservation effects of WIC and WSC for the fresh-cut apple slices were evaluated by the indexes of browning, weight loss, firmness and titratable acidity. The results showed that WSC was soluble in water and that the chemical structures of WIC and WSC were similar. However, their crystallinity, morphology and thermal properties were different. Both WSC and WIC had a good preservation effect on fresh-cut fruits. Compared with WIC, WSC might be more suitable for use in the food industry owing to its water solubility.

Synthesis and characterization of a novel polyamidoamine-cyclodextrin crosslinked copolymer.

A novel insoluble crosslinked copolymer containing ß-cyclodextrin (ß-CD) structural units has been synthesized with polyamidoamine (PAMAM, generation 2) as comonomer. The polymer was characterized using Fourier-transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), elemental analysis, scanning electron microscopy (SEM), pores and surface area analysis, X-ray diffraction analysis (XRD), and thermal analysis (thermogravimetric and differential scanning calorimetric measurement, TG/DSC). The results reveal that PAMAM-CD copolymer has been synthesized successfully and two ß-CD molecules were cross-linked by one PAMAM (G2.0) molecule (on average). The copolymer has a reef-like surface with many irregular nanocavities, and its thermal stability is > 180°C in an argon atmosphere. The synthesis strategy presented in this work provides an innovative route for the synthesis of a PAMAM-CD-based copolymer. In preliminary sorption experiments, the PAMAM-CD copolymer exhibits high sorption capacities and high removal efficiencies toward both the heavy-metal ions (Cu(2+) and Pb(2+)) and organic compounds (2,4-dichlorophenol, 2,4,6-trichlorophenol, and ponceau 4R (C.I. 16255)). The polymer may provide many possibilities for applications in biomedical sensing, flocculation, sorption, and therapeutics.