Reconstructing Cancellous Bone From Down-Sampled Optical-Resolution Photoacoustic Microscopy Images With Deep Learning.

OBJECTIVE: Bone diseases deteriorate the microstructure of bone tissue. Optical-resolution photoacoustic microscopy (OR-PAM) enables high spatial resolution of imaging bone tissues. However, the spatiotemporal trade-off limits the application of OR-PAM. The purpose of this study was to improve the quality of OR-PAM images without sacrificing temporal resolution. METHODS: In this study, we proposed the Photoacoustic Dense Attention U-Net (PADA U-Net) model, which was used for reconstructing full-scanning images from under-sampled images. Thereby, this approach breaks the trade-off between imaging speed and spatial resolution. RESULTS: The proposed method was validated on resolution test targets and bovine cancellous bone samples to demonstrate the capability of PADA U-Net in recovering full-scanning images from under-sampled OR-PAM images. With a down-sampling ratio of [4, 1], compared to bilinear interpolation, the Peak Signal-to-Noise Ratio and Structural Similarity Index Measure values (averaged over the test set of bovine cancellous bone) of the PADA U-Net were improved by 2.325 dB and 0.117, respectively. CONCLUSION: The results demonstrate that the PADA U-Net model reconstructed the OR-PAM images well with different levels of sparsity. Our proposed method can further facilitate early diagnosis and treatment of bone diseases using OR-PAM.

Stretchable Ultrasound Metalens for Biomedical Zoom Imaging and Bone Quality Assessment with Subwavelength Resolution.

Ultrasound imaging is extensively used in biomedical science and clinical practice. Imaging resolution and tunability of imaging plane are key performance indicators, but both remain challenging to be improved due to the longer wavelength compared with light and the lack of zoom lens for ultrasound. Here, the ultrasound zoom imaging based on a stretchable planar metalens that simultaneously achieves the subwavelength imaging resolution and dynamic control of the imaging plane is reported. The proposed zoom imaging ultrasonography enables precise bone fracture diagnosis and comprehensive osteoporosis assessment. Millimeter-scale microarchitectures of the cortical bones at different depths can be selectively imaged with a 0.6-wavelength resolution. The morphological features of bone fractures, including the shape, size and position, are accurately detected. Based on the extracted ultrasound information of cancellous bones with healthy matrix, osteopenia and osteoporosis, a multi-index osteoporosis evaluation method is developed. Furthermore, it provides additional biological information in aspects of bone elasticity and attenuation to access the comprehensive osteoporosis assessment. The soft metalens also features flexibility and biocompatibility for preferable applications on wearable devices. This work provides a strategy for the development of high-resolution ultrasound biomedical zoom imaging and comprehensive bone quality diagnosis system.

Removing Artifacts in Transcranial Photoacoustic Imaging With Polarized Self-Attention Dense-UNet.

OBJECTIVE: Photoacoustic imaging (PAI) is a promising transcranial imaging technique. However, the distortion of photoacoustic signals induced by the skull significantly influences its imaging quality. We aimed to use deep learning for removing artifacts in PAI. METHODS: In this study, we propose a polarized self-attention dense U-Net, termed PSAD-UNet, to correct the distortion and accurately recover imaged objects beneath bone plates. To evaluate the performance of the proposed method, a series of experiments was performed using a custom-built PAI system. RESULTS: The experimental results showed that the proposed PSAD-UNet method could effectively implement transcranial PAI through a one- or two-layer bone plate. Compared with the conventional delay-and-sum and classical U-Net methods, PSAD-UNet can diminish the influence of bone plates and provide high-quality PAI results in terms of structural similarity and peak signal-to-noise ratio. The 3-D experimental results further confirm the feasibility of PSAD-UNet in 3-D transcranial imaging. CONCLUSION: PSAD-UNet paves the way for implementing transcranial PAI with high imaging accuracy, which reveals broad application prospects in preclinical and clinical fields.

DF-GAM: Cross-Domain Ultrasound Image High-Quality Reconstruction Using a Dual Frequency-Domain Guided Adaptation Model.

OBJECTIVE: To enhance the quality of low-resolution (LR) ultrasound images and mitigate artifacts and speckle noise, which can impede accurate medical diagnosis, a novel method called the dual frequency-domain guided adaptation model (DF-GAM) is proposed. The method aims to achieve high-quality image reconstruction across diverse domains, including different ultrasound machines, diseases and phantom images. METHODS: DF-GAM utilizes a dual-branch network architecture combined with frequency-domain self-adaptation and self-supervised edge regression. This approach enables cross-domain enhancement by focusing on the reconstruction of clear tissue structures and speckle patterns. The model is designed to adapt to various ultrasound imaging (USI) scenarios, ensuring its applicability in real-world clinical settings. RESULTS: Experimental evaluations of DF-GAM were conducted using five different datasets. The results demonstrated the method's effectiveness, with DF-GAM outperforming existing enhancement techniques. The average peak signal-to-noise ratio (PSNR) achieved was 34.62, and the structural similarity index (SSIM) was 0.91, indicating a significant improvement in image quality compared to other methods. CONCLUSION: DF-GAM shows great potential in improving medical image diagnosis and interpretation. Its ability to enhance LR ultrasound images across various domains without the need for extensive training data makes it a valuable tool for clinical use. The high PSNR and SSIM scores validate the method's effectiveness, suggesting that DF-GAM could significantly contribute to the field of USI diagnostics.

Unsupervised Learning-Based Measurement of Ultrasonic Axial Transmission Velocity in Neonatal Bone.

OBJECTIVES: To develop a robust algorithm for estimating ultrasonic axial transmission velocity from neonatal tibial bone, and to investigate the relationships between ultrasound velocity and neonatal anthropometric measurements as well as clinical biochemical markers of skeletal health. METHODS: This study presents an unsupervised learning approach for the automatic detection of first arrival time and estimation of ultrasonic velocity from axial transmission waveforms, which potentially indicates bone quality. The proposed method combines the ReliefF algorithm and fuzzy C-means clustering. It was first validated using an in vitro dataset measured from a Sawbones phantom. It was subsequently applied on in vivo signals collected from 40 infants, comprising 21 males and 19 females. The extracted neonatal ultrasonic velocity was subjected to statistical analysis to explore correlations with the infants' anthropometric features and biochemical indicators. RESULTS: The results of in vivo data analysis revealed significant correlations between the extracted ultrasonic velocity and the neonatal anthropometric measurements and biochemical markers. The velocity of first arrival signals showed good associations with body weight (ρ = 0.583, P value <.001), body length (ρ = 0.583, P value <.001), and gestational age (ρ = 0.557, P value <.001). CONCLUSION: These findings suggest that fuzzy C-means clustering is highly effective in extracting ultrasonic propagating velocity in bone and reliably applicable in in vivo measurement. This work is a preliminary study that holds promise in advancing the development of a standardized ultrasonic tool for assessing neonatal bone health. Such advancements are crucial in the accurate diagnosis of bone growth disorders.

Weakly-Supervised Segmentation-Based Quantitative Characterization of Pulmonary Cavity Lesions in CT Scans.

OBJECTIVE: Pulmonary cavity lesion is one of the commonly seen lesions in lung caused by a variety of malignant and non-malignant diseases. Diagnosis of a cavity lesion is commonly based on accurate recognition of the typical morphological characteristics. A deep learning-based model to automatically detect, segment, and quantify the region of cavity lesion on CT scans has potential in clinical diagnosis, monitoring, and treatment efficacy assessment. METHODS: A weakly-supervised deep learning-based method named CSA2-ResNet was proposed to quantitatively characterize cavity lesions in this paper. The lung parenchyma was firstly segmented using a pretrained 2D segmentation model, and then the output with or without cavity lesions was fed into the developed deep neural network containing hybrid attention modules. Next, the visualized lesion was generated from the activation region of the classification network using gradient-weighted class activation mapping, and image processing was applied for post-processing to obtain the expected segmentation results of cavity lesions. Finally, the automatic characteristic measurement of cavity lesions (e.g., area and thickness) was developed and verified. RESULTS: the proposed weakly-supervised segmentation method achieved an accuracy, precision, specificity, recall, and F1-score of 98.48%, 96.80%, 97.20%, 100%, and 98.36%, respectively. There is a significant improvement (P < 0.05) compared to other methods. Quantitative characterization of morphology also obtained good analysis effects. CONCLUSIONS: The proposed easily-trained and high-performance deep learning model provides a fast and effective way for the diagnosis and dynamic monitoring of pulmonary cavity lesions in clinic. Clinical and Translational Impact Statement: This model used artificial intelligence to achieve the detection and quantitative analysis of pulmonary cavity lesions in CT scans. The morphological features revealed in experiments can be utilized as potential indicators for diagnosis and dynamic monitoring of patients with cavity lesions.

A transformer-guided cross-modality adaptive feature fusion framework for esophageal gross tumor volume segmentation.

BACKGROUND AND OBJECTIVE: Accurate segmentation of esophageal gross tumor volume (GTV) indirectly enhances the efficacy of radiotherapy for patients with esophagus cancer. In this domain, learning-based methods have been employed to fuse cross-modality positron emission tomography (PET) and computed tomography (CT) images, aiming to improve segmentation accuracy. This fusion is essential as it combines functional metabolic information from PET with anatomical information from CT, providing complementary information. While the existing three-dimensional (3D) segmentation method has achieved state-of-the-art (SOTA) performance, it typically relies on pure-convolution architectures, limiting its ability to capture long-range spatial dependencies due to convolution's confinement to a local receptive field. To address this limitation and further enhance esophageal GTV segmentation performance, this work proposes a transformer-guided cross-modality adaptive feature fusion network, referred to as TransAttPSNN, which is based on cross-modality PET/CT scans. METHODS: Specifically, we establish an attention progressive semantically-nested network (AttPSNN) by incorporating the convolutional attention mechanism into the progressive semantically-nested network (PSNN). Subsequently, we devise a plug-and-play transformer-guided cross-modality adaptive feature fusion model, which is inserted between the multi-scale feature counterparts of a two-stream AttPSNN backbone (one for the PET modality flow and another for the CT modality flow), resulting in the proposed TransAttPSNN architecture. RESULTS: Through extensive four-fold cross-validation experiments on the clinical PET/CT cohort. The proposed approach acquires a Dice similarity coefficient (DSC) of 0.76 ± 0.13, a Hausdorff distance (HD) of 9.38 ± 8.76 mm, and a Mean surface distance (MSD) of 1.13 ± 0.94 mm, outperforming the SOTA competing methods. The qualitative results show a satisfying consistency with the lesion areas. CONCLUSIONS: The devised transformer-guided cross-modality adaptive feature fusion module integrates the strengths of PET and CT, effectively enhancing the segmentation performance of esophageal GTV. The proposed TransAttPSNN has further advanced the research of esophageal GTV segmentation.

Super-resolution reconstruction of ultrasound image using a modified diffusion model.

Objective. This study aims to perform super-resolution (SR) reconstruction of ultrasound images using a modified diffusion model, designated as the diffusion model for ultrasound image super-resolution (DMUISR). SR involves converting low-resolution images to high-resolution ones, and the proposed model is designed to enhance the suitability of diffusion models for this task in the context of ultrasound imaging.Approach. DMUISR incorporates a multi-layer self-attention (MLSA) mechanism and a wavelet-transform based low-resolution image (WTLR) encoder to enhance its suitability for ultrasound image SR tasks. The model takes interpolated and magnified images as input and outputs high-quality, detailed SR images. The study utilized 1,334 ultrasound images from the public fetal head-circumference dataset (HC18) for evaluation.Main results. Experiments were conducted at 2× , 4× , and 8×  magnification factors. DMUISR outperformed mainstream ultrasound SR methods (Bicubic, VDSR, DECUSR, DRCN, REDNet, SRGAN) across all scales, providing high-quality images with clear structures and rich detailed textures in both hard and soft tissue regions. DMUISR successfully accomplished multiscale SR reconstruction while suppressing over-smoothing and mode collapse problems. Quantitative results showed that DMUISR achieved the best performance in terms of learned perceptual image patch similarity, with a significant decrease of over 50% at all three magnification factors (2× , 4× , and 8× ), as well as improvements in peak signal-to-noise ratio and structural similarity index measure. Ablation experiments validated the effectiveness of the MLSA mechanism and WTLR encoder in improving DMUISR's SR performance. Furthermore, by reducing the number of diffusion steps, the computational time of DMUISR was shortened to nearly one-tenth of its original while maintaining image quality without significant degradation.Significance. This study demonstrates that the modified diffusion model, DMUISR, provides superior performance for SR reconstruction of ultrasound images and has potential in improving imaging quality in the medical ultrasound field.

Revealing the Incidence-Angle-Independent Frequency Shift in the Acoustic Rotational Doppler Effect.

The Doppler effect is a universal wave phenomenon that has inspired various applications due to the induced frequency shift. In the case of the linear Doppler effect, the frequency shift depends on the incident frequency and angle. Here, we unveil the frequency shift dependence induced by the acoustic rotational Doppler effect in the wave-object scattering process. We experimentally demonstrate that this frequency shift is exclusively determined by the angular speed and rotational symmetry of the spinning scatterer while remaining independent of the incident angular momentum and angle. We derive the analytical relationship between the frequency shift and the scatterer's helicity, presenting a novel approach for helical feature recognition. The angle-independent nature of the frequency shift inherently prevents spectrum broadening and offers a solution for precise motion measurement through the rotational Doppler effect. This work provides a rigorous and comprehensive understanding of the acoustic Doppler effect, enriching its applications in helicity and motion detection.

Backscatter measurement of cancellous bone using the ultrasound transit time spectroscopy.

Recently, ultrasound transit time spectroscopy (UTTS) was proposed as a promising method for bone quantitative ultrasound measurement. Studies have showed that UTTS could estimate the bone volume fraction and other trabecular bone structure in ultrasonic through-transmission measurements. The goal of this study was to explore the feasibility of UTTS to be adapted in ultrasonic backscatter measurement and further evaluate the performance of backscattered ultrasound transit time spectrum (BS-UTTS) in the measurement of cancellous bone density and structure. First, taking ultrasonic attenuation into account, the concept of BS-UTTS was verified on ultrasonic backscatter signals simulated from a set of scatterers with different positions and intensities. Then, in vitro backscatter measurements were performed on 26 bovine cancellous bone specimens. After a logarithmic compression of the BS-UTTS, a linear fitting of the log-compressed BS-UTTS versus ultrasonic propagated distance was performed and the slope and intercept of the fitted line for BS-UTTS were determined. The associations between BS-UTTS parameters and cancellous bone features were analyzed using simple linear regression. The results showed that the BS-UTTS could make an accurate deconvolution of the backscatter signal and predict the position and intensity of the simulated scatterers eliminating phase interference, even the simulated backscatter signal was with a relatively low signal-to-noise ratio. With varied positions and intensities of the scatterers, the slope of the fitted line for the log-compressed BS-UTTS versus ultrasonic propagated distance (i.e., slope of BS-UTTS for short) yield a high agreement (r2 = 99.84%-99.96%) with ultrasonic attenuation in simulated backscatter signal. Compared with the high-density cancellous bone, the low-density specimen showed more abundant backscatter impulse response in the BS-UTTS. The slope of BS-UTTS yield a significant correlation with bone mineral density (r = 0.87; p < 0.001), BV/TV (r = 0.87; p < 0.001), and cancellous bone microstructures (r up to 0.87; p < 0.05). The intercept of BS-UTTS was also significantly correlated with bone densities (r = -0.87; p < 0.001) and trabecular structures (|r|=0.43-0.80; p < 0.05). However, the slope of the BS-UTTS underestimated attenuation when measurements were performed experimentally. In addition, a significant non-linear relationship was observed between the measured attenuation and the attenuation estimated by the slope of the BS-UTTS. This study demonstrated that the UTTS method could be adapted to ultrasonic backscatter measurement of cancellous bone. The derived slope and intercept of BS-UTTS could be used in the measurement of bone density and microstructure. The backscattered ultrasound transit time spectroscopy might have potential in the diagnosis of osteoporosis in the clinic.

The Protocol of Ultrasonic Backscatter Measurements of Musculoskeletal Properties.

This study aims to introduce the protocol for ultrasonic backscatter measurements of musculoskeletal properties based on a novel ultrasonic backscatter bone diagnostic (UBBD) instrument. Dual-energy X-ray absorptiometry (DXA) can be adopted to measure bone mineral density (BMD) in the hip, spine, legs and the whole body. The muscle and fat mass in the legs and the whole body can be also calculated by DXA body composition analysis. Based on the proposed protocol for backscatter measurements by UBBD, ultrasonic backscatter signals can be measured in vivo, deriving three backscatter parameters [apparent integral backscatter (AIB), backscatter signal peak amplitude (BSPA) and the corresponding arrival time (BSPT)]. AIB may provide important diagnostic information about bone properties. BSPA and BSPT may be important indicators of muscle and fat properties. The standardized backscatter measurement protocol of the UBBD instrument may have the potential to evaluate musculoskeletal characteristics, providing help for promoting the application of the backscatter technique in the clinical diagnosis of musculoskeletal disorders (MSDs), such as osteoporosis and muscular atrophy.

Data-driven method of super-resolution image recovery for speckle-illumination photoacoustic computed tomography.

Methods have been proposed in recent years aimed at pushing photoacoustic imaging resolution beyond the acoustic diffraction limit, among which those based on random speckle illumination show particular promise. In this Letter, we propose a data-driven deep learning approach to processing the added spatiotemporal information resulting from speckle illumination, where the neural network learns the distribution of absorbers from a series of different samplings of the imaged area. In ex-vivo experiments based on the tomography configuration with prominent artifacts, our method successfully breaks the acoustic diffraction limit and delivers better results in identifying individual targets when compared against a selection of other leading methods.

Light on Alzheimer's disease: from basic insights to preclinical studies.

Alzheimer's disease (AD), referring to a gradual deterioration in cognitive function, including memory loss and impaired thinking skills, has emerged as a substantial worldwide challenge with profound social and economic implications. As the prevalence of AD continues to rise and the population ages, there is an imperative demand for innovative imaging techniques to help improve our understanding of these complex conditions. Photoacoustic (PA) imaging forms a hybrid imaging modality by integrating the high-contrast of optical imaging and deep-penetration of ultrasound imaging. PA imaging enables the visualization and characterization of tissue structures and multifunctional information at high resolution and, has demonstrated promising preliminary results in the study and diagnosis of AD. This review endeavors to offer a thorough overview of the current applications and potential of PA imaging on AD diagnosis and treatment. Firstly, the structural, functional, molecular parameter changes associated with AD-related brain imaging captured by PA imaging will be summarized, shaping the diagnostic standpoint of this review. Then, the therapeutic methods aimed at AD is discussed further. Lastly, the potential solutions and clinical applications to expand the extent of PA imaging into deeper AD scenarios is proposed. While certain aspects might not be fully covered, this mini-review provides valuable insights into AD diagnosis and treatment through the utilization of innovative tissue photothermal effects. We hope that it will spark further exploration in this field, fostering improved and earlier theranostics for AD.

ULM-MbCNRT: In vivo Ultrafast Ultrasound Localization Microscopy by Combining Multi-branch CNN and Recursive Transformer.

Ultrasound localization microscopy (ULM) overcomes the acoustic diffraction limit by localizing tiny microbubbles (MBs), thus enabling the microvascular to be rendered at sub-wavelength resolution. Nevertheless, to obtain such superior spatial resolution, it is necessary to spend tens of seconds gathering numerous ultrasound (US) frames to accumulate MB events required, resulting in ULM imaging still suffering from trade-offs between imaging quality, data acquisition time and data processing speed. In this paper, we present a new deep learning (DL) framework combining multi-branch CNN and recursive Transformer, termed as ULM-MbCNRT, that is capable of reconstructing a super-resolution image directly from a temporal mean low-resolution image generated by averaging much fewer raw US frames, i.e., implement an ultrafast ULM imaging. To evaluate the performance of ULM-MbCNRT, a series of numerical simulations and in vivo experiments are carried out. Numerical simulation results indicate that ULM-MbCNRT achieves high-quality ULM imaging with ~10-fold reduction in data acquisition time and ~130-fold reduction in computation time compared to the previous DL method (e.g., the modified sub-pixel convolutional neural network, ULM-mSPCN). For the in vivo experiments, when comparing to the ULM-mSPCN, ULM-MbCNRT allows ~37-fold reduction in data acquisition time (~0.8 s) and ~2134-fold reduction in computation time (~0.87 s) without sacrificing spatial resolution. It implies that ultrafast ULM imaging holds promise for observing rapid biological activity in vivo, potentially improving the diagnosis and monitoring of clinical conditions.

Combining quantitative and qualitative analysis for scoring pleural line in lung ultrasound.

Objective.Accurate assessment of pleural line is crucial for the application of lung ultrasound (LUS) in monitoring lung diseases, thereby aim of this study is to develop a quantitative and qualitative analysis method for pleural line.Approach.The novel cascaded deep learning model based on convolution and multilayer perceptron was proposed to locate and segment the pleural line in LUS images, whose results were applied for quantitative analysis of textural and morphological features, respectively. By using gray-level co-occurrence matrix and self-designed statistical methods, eight textural and three morphological features were generated to characterize the pleural lines. Furthermore, the machine learning-based classifiers were employed to qualitatively evaluate the lesion degree of pleural line in LUS images.Main results.We prospectively evaluated 3770 LUS images acquired from 31 pneumonia patients. Experimental results demonstrated that the proposed pleural line extraction and evaluation methods all have good performance, with dice and accuracy of 0.87 and 94.47%, respectively, and the comparison with previous methods found statistical significance (P< 0.001 for all). Meanwhile, the generalization verification proved the feasibility of the proposed method in multiple data scenarios.Significance.The proposed method has great application potential for assessment of pleural line in LUS images and aiding lung disease diagnosis and treatment.

B-mode ultrasound to elastography synthesis using multiscale learning.

Elastography is a promising diagnostic tool that measures the hardness of tissues, and it has been used in clinics for detecting lesion progress, such as benign and malignant tumors. However, due to the high cost of examination and limited availability of elastic ultrasound devices, elastography is not widely used in primary medical facilities in rural areas. To address this issue, a deep learning approach called the multiscale elastic image synthesis network (MEIS-Net) was proposed, which utilized the multiscale learning to synthesize elastic images from ultrasound data instead of traditional ultrasound elastography in virtue of elastic deformation. The method integrates multi-scale features of the prostate in an innovative way and enhances the elastic synthesis effect through a fusion module. The module obtains B-mode ultrasound and elastography feature maps, which are used to generate local and global elastic ultrasound images through their correspondence. Finally, the two-channel images are synthesized into output elastic images. To evaluate the approach, quantitative assessments and diagnostic tests were conducted, comparing the results of MEIS-Net with several deep learning-based methods. The experiments showed that MEIS-Net was effective in synthesizing elastic images from B-mode ultrasound data acquired from two different devices, with a structural similarity index of 0.74 ± 0.04. This outperformed other methods such as Pix2Pix (0.69 ± 0.09), CycleGAN (0.11 ± 0.27), and StarGANv2 (0.02 ± 0.01). Furthermore, the diagnostic tests demonstrated that the classification performance of the synthetic elastic image was comparable to that of real elastic images, with only a 3 % decrease in the area under the curve (AUC), indicating the clinical effectiveness of the proposed method.

Study on Synergistic Effects of Nanohydroxyapatite/High-Viscosity Carboxymethyl Cellulose Scaffolds Stimulated by LIPUS for Bone Defect Repair of Rats.

Despite the self-healing capacity of bone, the regeneration of critical-size bone defects remains a major clinical challenge. In this study, nanohydroxyapatite (nHAP)/high-viscosity carboxymethyl cellulose (hvCMC, 6500 mPa·s) scaffolds and low-intensity pulsed ultrasound (HA-LIPUS) were employed to repair bone defects. First, hvCMC was prepared from ramie fiber, and the degree of substitution (DS), purity, and content of NaCl of hvCMC samples were 0.91, 99.93, and 0.017%, respectively. Besides, toxic metal contents were below the permissible limits for pharmaceutically used materials. Our results demonstrated that the hvCMC is suitable for pharmaceutical use. Second, nHAP and hvCMC were employed to prepare scaffolds by freeze-drying. The results indicated that the scaffolds were porous, and the porosity was 35.63 ± 3.52%. Subsequently, the rats were divided into four groups (n = 8) randomly: normal control (NC), bone defect (BD), bone defect treated with nHAP/hvCMC scaffolds (HA), and bone defect treated with nHAP/hvCMC scaffolds and stimulated by LIPUS (HA-LIPUS). After drilling surgery, nHAP/hvCMC scaffolds were implanted in the defect region of HA and HA-LIPUS rats. Meanwhile, HA-LIPUS rats were treated by LIPUS (1.5 MHz, 80 mW cm-2) irradiation for 2 weeks. Compared with BD rats, the maximum load and bone mineral density of HA-LIPUS rats were increased by 20.85 and 51.97%, respectively. The gene and protein results indicated that nHAP/hvCMC scaffolds and LIPUS promoted the bone defect repair and regeneration of rats significantly by activating Wnt/ß-catenin and inhibiting OPG/RANKL signaling pathways. Overall, compared with BD rats, nHAP/hvCMC scaffolds and LIPUS promoted bone defect repair significantly. Furthermore, the research results also indicated that there are synergistic effects for bone defect repair between the nHAP/hvCMC scaffolds and LIPUS.

Diagnosis of cognitive and motor disorders levels in stroke patients through explainable machine learning based on MRI.

BACKGROUND: Globally, stroke is the third most significant cause of disability. A stroke may produce motor, sensory, perceptual, or cognitive disorders that result in disability and affect the likelihood of recovery, affecting a person's ability to function. Evaluation post-stroke is critical for optimal stroke care. PURPOSE: Traditional methods for classifying the clinical disorders of cognitive and motor in stroke patients use assessment and interrogative measures, which are time-consuming, complex, and labor-intensive. In response to the current situation, this study develops an algorithm to automatically classify motor and cognitive disorders in stroke patients by 3D brain MRI to assist physicians in diagnosis. METHODS: First, radiomics and fusion features are extracted from the OAx T2 Propeller of 3D brain MRI. Then, we use 14 machine learning models and one model ensemble method to predict Fugl-Meyer and MMSE levels of stroke patients. Next, we evaluate the models using accuracy, recall, f1-score, and area under the curve (AUC). Finally, we employ SHAP to explain the output of the model. RESULTS: The best predictive models come from Random Forest (RF) Classifier with fusion features in cognitive classification and Linear Discriminant Analysis (LDA) with radiomics features in motor classification. The highest accuracies are 92.0 and 82.5% for cognitive and motor disorders. CONCLUSIONS: MRI brain maps can classify the cognitive and motor disorders of stroke patients. Radiomics features demonstrate its merits. The proposed algorithms with MRI images can efficiently assist physicians in diagnosing the cognitive and motor disorders of stroke patients in clinical practice. Additionally, this lessens labor costs, improves diagnostic effectiveness, and avoids the subjective difference that comes with manual assessment.

Low-Intensity Pulsed Ultrasound Promotes the Repair of Achilles Tendinopathy by Downregulating the JAK/STAT Signaling Pathway in Rabbits.

Tendinopathy is a complex tendon injury or pathology outcome, potentially leading to permanent impairment. Low-intensity pulsed ultrasound (LIPUS) is emerging as a treatment modality for tendon disorders. However, the optimal treatment duration and its effect on tendons remain unclear. This study aims to investigate the efficacy of LIPUS in treating injured tendons, delineate the appropriate treatment duration, and elucidate the underlying treatment mechanisms through animal experiments. Ninety-six three-month-old New Zealand white rabbits were divided into normal control (NC) and model groups. The model group received Prostaglandin E2 (PGE2) injections to induce Achilles tendinopathy. They were then divided into model control (MC) and LIPUS treatment (LT) groups. LT received LIPUS intervention with a 1-MHz frequency, a pulse repetition frequency (PRF) of 1 kHz, and spatial average temporal average sound intensity ( [Formula: see text]) of 100 mW/cm2. MC underwent a sham ultrasound, and NC received no treatment. Assessments on 1, 4, 7, 14, and 28 days after LT included shear wave elastography (SWE), mechanical testing, histologic evaluation, ribonucleic acid sequencing (RNA-seq), polymerase chain reaction (PCR), and western blot (WB) analysis. SWE results showed that the shear modulus in the LT group was significantly higher than that in the MC group after LT for seven days. Histological results demonstrated improved tendon tissue alignment and fibroblast distribution after LT. Molecular analyses suggested that LIPUS may downregulate the Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling pathway and regulate inflammatory and matrix-related factors. We concluded that LT enhanced injured tendon elasticity and accelerated Achilles tendon healing. The study highlighted the JAK/STAT signaling pathway as a potential therapeutic target for LT of Achilles tendinopathy, guiding future research.

Chinese Ultrasound Doctors Association Guideline on Operational Standards for 2-D Shear Wave Elastography Examination of Musculoskeletal Tissues.

The Ultrasound Physician Branch of the Chinese Medical Doctor Association sought to develop evidence-based recommendations on the operational standards for 2-D shear wave elastography examination of musculoskeletal tissues. A consensus panel of 22 Chinese musculoskeletal ultrasound experts reviewed current scientific evidence and proposed a set of 12 recommendations for 13 key issues, including instruments, operating methods, influencing factors and image interpretation. A final consensus was reached through discussion and voting. On the basis of research evidence and expert opinions, the strength of recommendation for each proposition was assessed using a visual analog scale, while further emphasizing the best available evidence during the question-and-answer session. These expert consensus guidelines encourage facilitation of the standardization of clinical practices for collecting and reporting shear wave elastography data.