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.

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.

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.

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.

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.

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.

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 (LPIPS), 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 (PSNR) and structural similarity index measure (SSIM). 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.

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.

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.

Combined Assessment of 2-D Ultrasound and Real-Time Shear Wave Elastography of Low-Intensity Pulsed Ultrasound Therapy Efficacy in Rabbits with Achilles Tendinopathy.

OBJECTIVE: Low-intensity pulsed ultrasound (LIPUS) has been gradually used to treat Achilles tendinopathy. However, there are limited non-invasive and efficient instruments for monitoring LIPUS efficacy in Achilles tendinopathy. The purpose of this study was to assess the therapeutic effectiveness of LIPUS after Achilles tendinopathy by 2-D ultrasound and real-time shear wave elastography (SWE). METHODS: Ninety New Zealand white rabbits were divided into control, sham and LIPUS groups after tendinopathy modeling. On days 1, 4, 7, 14 and 28, the Achilles tendon thickness and SWE Young's modulus on the long axis were measured. The tissues of the Achilles tendon were then evaluated histologically. RESULTS: The mean SWE values increased while the average thickness and histologic scores decreased, especially in the LIPUS group (9.5% and 80.7% on day 28, respectively). The SWE values in the LIPUS group were significantly lower than those in the control group on day 1 (121.0 kPa vs. 177.6 kPa) and peaked on day 7 (173.7 kPa, p < 0.001). By day 28, the SWE value had approached that of the control (191.2 kPa vs. 192.4 kPa), and had been significantly higher than that in the sham group since day 7. SWE values and histologic scores were correlated (r = -0.792, p < 0.01). The average thickness decreased in the three groups but did not differ significantly. CONCLUSION: Two-dimensional ultrasound is beneficial to the diagnosis of Achilles tendinopathy. SWE could quantify changes in Achilles tendon stiffness non-invasively during LIPUS treatment, enabling the study of early Achilles tendon healing after LIPUS treatment.

A super-resolution ultrasound imaging method based on active-modulated super-resolution optical fluctuation imaging.

Super resolution ultrasound imaging (SR-US) methods including super-resolution optical fluctuation imaging (SOFI) have been successfully demonstrated to improve imaging performance of ultrasound (US). However, the imaging quality of US improved by conventional SOFI depends on the probability of microbubbles (MB) appearing in imaging regions. Current SOFI-based ultrasound imaging methods usually fix the probability of MBs, ignoring the effect of probability characteristics, leading to artifacts in high-order SOFI images. Inspired by active-modulated SOFI (AR-SOFI), in this paper, we propose a new method, termed as AR-SOFI-US, for further improving the performance of SR-US, which is achieved by effectively controlling the probabilities of MBs on an appropriate range. Through a series of numerical simulations, we compare the imaging resolution at differing MB probabilities and demonstrate that by controlling the probabilities of MBs when they appear in the imaging regions, incorporating the proposed AR-SOFI-US method, we can improve the spatial resolution of SR-US to a higher degree, especially for the high-order SOFI imaging results.

Condition control training-based ConVMLP-ResU-Net for semantic segmentation of esophageal cancer in 18F-FDG PET/CT images.

The precise delineation of esophageal gross tumor volume (GTV) on medical images can promote the radiotherapy effect of esophagus cancer. This work is intended to explore effective learning-based methods to tackle the challenging auto-segmentation problem of esophageal GTV. By employing the progressive hierarchical reasoning mechanism (PHRM), we devised a simple yet effective two-stage deep framework, ConVMLP-ResU-Net. Thereinto, the front-end ConVMLP integrates convolution (ConV) and multi-layer perceptrons (MLP) to capture localized and long-range spatial information, thus making ConVMLP excel in the location and coarse shape prediction of esophageal GTV. According to the PHRM, the front-end ConVMLP should have a strong generalization ability to ensure that the back-end ResU-Net has correct and valid reasoning. Therefore, a condition control training algorithm was proposed to control the training process of ConVMLP for a robust front end. Afterward, the back-end ResU-Net benefits from the yielded mask by ConVMLP to conduct a finer expansive segmentation to output the final result. Extensive experiments were carried out on a clinical cohort, which included 1138 pairs of 18F-FDG positron emission tomography/computed tomography (PET/CT) images. We report the Dice similarity coefficient, Hausdorff distance, and Mean surface distance as 0.82 ± 0.13, 4.31 ± 7.91 mm, and 1.42 ± 3.69 mm, respectively. The predicted contours visually have good agreements with the ground truths. The devised ConVMLP is apt at locating the esophageal GTV with correct initial shape prediction and hence facilitates the finer segmentation of the back-end ResU-Net. Both the qualitative and quantitative results validate the effectiveness of the proposed method.

Low-intensity pulsed ultrasound alleviates doxorubicin-induced cardiotoxicity via inhibition of S100a8/a9-mediated cardiac recruitment of neutrophils.

Doxorubicin (DOX)-induced cardiotoxicity limits its broad use as a chemotherapy agent. The development of effective and non-invasive strategies to prevent DOX-associated adverse cardiac events is urgently needed. We aimed to examine whether and how low-intensity pulsed ultrasound (LIPUS) plays a protective role in DOX-induced cardiotoxicity. Male C57BL/6J mice were used to establish models of both acute and chronic DOX-induced cardiomyopathy. Non-invasive LIPUS therapy was conducted for four consecutive days after DOX administration. Cardiac contractile function was evaluated by echocardiography. Myocardial apoptosis, oxidative stress, and fibrosis were analyzed using terminal deoxynucleotidyl transferase-mediated dUTP nick end labelling (TUNEL) staining, dihydroethidium (DHE) staining, and picrosirius red staining assays. RNA-seq analysis was performed to unbiasedly explore the possible downstream regulatory mechanisms. Neutrophil recruitment and infiltration in the heart were analyzed by flow cytometry. The S100a8/a9 inhibitor ABR-238901 was utilized to identify the effect of S100a8/a9 signaling. We found that LIPUS therapy elicited a great benefit on DOX-induced heart contractile dysfunction in both acute and chronic DOX models. Chronic DOX administration increased serum creatine kinase and lactate dehydrogenase levels, as well as myocardial apoptosis, all of which were significantly mitigated by LIPUS. In addition, LIPUS treatment prevented chronic DOX-induced cardiac oxidative stress and fibrosis. RNA-seq analysis revealed that LIPUS treatment partially reversed alterations of gene expression induced by DOX. Gene ontology (GO) analysis of the downregulated genes between DOX-LIPUS and DOX-Sham groups indicated that inhibition of neutrophil chemotaxis might be involved in the protective effects of LIPUS therapy. Flow cytometry analysis illustrated the inhibitory effects of LIPUS on DOX-induced neutrophil recruitment and infiltration in the heart. Moreover, S100 calcium binding protein A8/A9 (S100a8/a9) was identified as a potential key target of LIPUS therapy. S100a8/a9 inhibition by ABR-238901 showed a similar heart protective effect against DOX-induced cardiomyopathy to LIPUS treatment. LIPUS therapy prevents DOX-induced cardiotoxicity through inhibition of S100a8/a9-mediated neutrophil recruitment to the heart, suggesting its potential application in cancer patients undergoing chemotherapy with DOX.

Distinct thermal effect on biological tissues using subwavelength ultrasound metalens at megahertz.

Ultrasound focusing plays an important role in biomedical therapy and diagnosis. Acoustic metalens has showcased remarkable focusing performance but yet to be implemented to the practical ultrasound therapeutic applications. We design a planar metalens operating at megahertz and experimentally demonstrate the distinct thermal effect on biological tissues induced by the high-resolution focusing. A prominent temperature rise of 50°C is experimentally observed in the biological phantom, with a much lower input ultrasound power of 4 W compared with the traditional methods. We further study the thermal effect on fresh porcine liver and investigate the morphological changes under different physical parameters. Visible lesions are observed in in vitro tissues at the lowest input ultrasound power of 2.6 W within 10 s. This study facilitates the practical biomedical application of acoustic metalens, providing a feasible approach for the precise, safe, and reliable therapeutic ultrasound with the simple and compact metalens.

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.

Virtual Fluorescence Translation for Biological Tissue by Conditional Generative Adversarial Network.

Fluorescence labeling and imaging provide an opportunity to observe the structure of biological tissues, playing a crucial role in the field of histopathology. However, when labeling and imaging biological tissues, there are still some challenges, e.g., time-consuming tissue preparation steps, expensive reagents, and signal bias due to photobleaching. To overcome these limitations, we present a deep-learning-based method for fluorescence translation of tissue sections, which is achieved by conditional generative adversarial network (cGAN). Experimental results from mouse kidney tissues demonstrate that the proposed method can predict the other types of fluorescence images from one raw fluorescence image, and implement the virtual multi-label fluorescent staining by merging the generated different fluorescence images as well. Moreover, this proposed method can also effectively reduce the time-consuming and laborious preparation in imaging processes, and further saves the cost and time. Supplementary Information: The online version contains supplementary material available at 10.1007/s43657-023-00094-1.

Ray theory-based compounded plane wave ultrasound imaging for aberration corrected transcranial imaging: Phantom experiments and simulations.

Compounded plane wave imaging (CPWI) allows high-frame-rate measurement and has been one of the most promising modalities for real-time brain imaging. However, ultrasonic brain imaging using the CPWI modality is usually performed with a worn thin or removal of the skull layer. Otherwise, the skull layer is expected to distort the ultrasonic wavefronts and significantly decrease intracranial imaging quality. The motivation of this study is to investigate a CPWI method for transcranial brain imaging with the skull layer. A coordinate transformation ray-tracing (CTRT) approach was proposed to track the distorted ultrasonic wavefronts and calculate the time delays for the ultrasound plane wave passing through the skull layer. With an accurate correction for the time delays in beamforming, the CTRT-based CPWI could achieve high-quality intracranial images with the presence of skulls. The proposed CTRT-based CPWI method was verified using a simplified three-layer transcranial model. The full-wave simulation demonstrated that CTRT could accurately (i.e., relative percentage error less than0.18%) track the distorted transmitting wavefront through skull. Compared with the CPWI without aberration correction, the CTRT-based CPWI provided high-quality intracranial imaging and could accurately localize intracranial point scatterers; specifically, positioning error decreases from 0.5 mm to 0.1 mm on average in the axial direction and from 0.7 mm to 0.1 mm on average in the lateral direction. As the compounded angles increased in the CTRT-based CPWI, the contrast improved by 16.2 dB on average for the region of interest, and the array performance indicator (representing resolution) decreased by 4.0 on average for the intracranial point scatterers. The CTRT is of low computational cost compared with full wave simulation. This study suggested that the proposed CTRT-based CPWI might have the potential for real-time and non-invasive transcranial aberration-corrected imaging.