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Abstract Interventional embolization has been widely used as a clinical cancer therapy, which deactivates the tumors by occluding their blood supply vessels. However, conventional methods lack active control over the embolic particles, thus having a limited selectivity of millimeter-scale vessels and the issue of missing embolization. Here, we propose an ultrasound-based method for embolic particle control in submillimeter vessels. The biocompatible ultrasound generated from an extrasomatic source can transmit through biological tissues, and exert forces on the intravital embolic particles. We show that the particles, influenced by these forces, are steerable to the target branch at vascular bifurcations. By modulating the ultrasound to adapt the vascular bifurcation distribution, the particles flowing in the micro-vessel networks are steered to the target branch and embolize it. The acoustic steering within ex vivo and in vivo models both verify the potential of this non-invasive particle control for precise and safe interventional therapy.

High-intensity focused ultrasound (HIFU) is considered as an important non-invasive way for tumor ablation in deep organs. However, accurate real-time monitoring of the temperature field within HIFU focal area remains a challenge. Although ultrasound technology, compared with other approaches, is a good choice for noninvasive and real-time monitoring on the temperature distribution, traditional ultrasonic thermometry mainly relies on the backscattered signal, which is difficult for high temperature (>50 °C) measurement. Given that artificial intelligence (AI) shows significant potential for biomedical applications, we propose an AI-powered ultrasonic thermometry using an end-to-end deep neural network termed Breath-guided Multimodal Teacher-Student (BMTS), which possesses the capability to elucidate the interaction between HIFU and complex heterogeneous biological media. It has been demonstrated experimentally that two-dimension temperature distribution within HIFU focal area in deep organ can be accurately reconstructed with an average error and a frame speed of 0.8 °C and 0.37 s, respectively. Most importantly, the maximum measurable temperature for ultrasonic technology has been successfully expanded to a record value of 67 °C. This breakthrough indicates that the development of AI-powered ultrasonic thermometry is beneficial for precise HIFU therapy planning in the future.

Abstract Due to its non‐invasive nature, ultrasound has been widely used for neuromodulation in biological systems, where its application influences the synaptic weights and the process of neurotransmitter delivery. However, such modulation has not been emulated in physical devices. Memristors are ideal electrical components for artificial synapses, but up till now they are hardly reported to respond to ultrasound signals. Here we design and fabricate a HfOx‐based memristor on 64°Y‐X LiNbO3 single crystal substrate, and successfully realize artificial synapses modulation by shear‐horizontal surface acoustic wave (SH‐SAW). It is a prominent short‐term resistance modulation, where ultrasound has been shown to cause resistance drop for various resistance states, which could fully recover after the ultrasound is shut off. The physical mechanism illustrates that ultrasound induced polarization potential in the HfOx dielectric layer acts on the Schottky barrier, leading to the resistance drop. The emulation of neuron firing frequency modulation through ultrasound signals is demonstrated. Moreover, the joint application of ultrasound and electric voltage yields fruitful functionalities, such as the enhancement of resistance window and synaptic plasticity through ultrasound application. All these promising results provide a new strategy for artificial synapses modulation, and also further advance neuromorphic devices toward system applications.

Highlights Based on Fe-Hpdca-PDMS and carbon nanotube composite, an optoacoustic patch is developed, which can recover from the damage induced by cutting or laser irradiation at room temperature. The patch has high laser damage threshold (183.44 mJ cm−2) and optoacoustic energy conversion efficiency (10.66×10−3). The patch has been successfully examined in acoustic flow, thrombolysis, and wireless energy harvesting, which may provide new insights into the field of the design and fabrication of novel ultrasound devices for biomedical applications.

IntroductionYoung cervical cancer patients who require ovarian transposition usually have their ovaries moved away from the pelvic radiotherapy (RT) field before radiotherapy. The dose of ovaries during radiotherapy is closely related to the location of the ovaries. To protect ovarian function and avoid ovarian dose exceeding the limits, a safe location of transposed ovary must be determined prior to surgery.MethodsFor this purpose, we input the patient's preoperative CT into a neural network model to predict the dose distribution. Surgeons were able to quickly locate low-dose regions based on the dose distribution before surgery, thus determining the safe location of the transposed ovary. In this work, we proposed a new progressive refinement transformer model PRT-Net that can generate dose prediction at multiple scale resolutions in one forward propagation, and refine the dose prediction using prediction details from low to high resolution based on a deep supervision strategy. A multi-loss function fusion algorithm was also built to fit the prediction results under different loss dimensions. The clinical feasibility of the method was verified through an actual cases.Results and discussionTherefore, using PRT-Net to predict the dose distribution by preoperative CT in cervical cancer patients can assist clinicians to perform ovarian transposition surgery and prevent patients' ovaries from exceeding the prescribed dose limit in postoperative radiotherapy.

Abstract Cell‐laden hydrogel fibers/tubules are one of the fundamentals of tissue engineering. They have been proven as a promising method for constructing biomimetic tissues, such as muscle fibers, nerve conduits, tendon and vessels, etc. However, current hydrogel fiber/tubule production methods have limitations in ordered cell arrangements, thus impeding the biomimetic configurations. Acoustic cell patterning is a cell manipulation method that has good biocompatibility, wide tunability, and is contact‐free. However, there are few studies on acoustic cell patterning for fiber production, especially on the radial figure cell arrangements, which mimic many native tissue‐like cell arrangements. Here, an acoustic cell patterning system that can be used to produce hydrogel fibers/tubules with tunable cell patterns is shown. Cells can be pre‐patterned in the liquid hydrogel before being extruded as cross‐linked hydrogel fibers/tubules. The radial patterns can be tuned with different complexities based on the acoustic resonances. Cell viability assays after 72 h confirm good cell viability and proliferation. Considering the biocompatibility and reliability, the present method can be further used for a variety of biomimetic fabrications.

Introduction: Currently, the incidence of liver cancer is on the rise annually. Precise identification of liver tumors is crucial for clinicians to strategize the treatment and combat liver cancer. Thus far, liver tumor contours have been derived through labor-intensive and subjective manual labeling. Computers have gained widespread application in the realm of liver tumor segmentation. Nonetheless, liver tumor segmentation remains a formidable challenge owing to the diverse range of volumes, shapes, and image intensities encountered. Methods: In this article, we introduce an innovative solution called the attention connect network (AC-Net) designed for automated liver tumor segmentation. Building upon the U-shaped network architecture, our approach incorporates 2 critical attention modules: the axial attention module (AAM) and the vision transformer module (VTM), which replace conventional skip-connections to seamlessly integrate spatial features. The AAM facilitates feature fusion by computing axial attention across feature maps, while the VTM operates on the lowest resolution feature maps, employing multihead self-attention, and reshaping the output into a feature map for subsequent concatenation. Furthermore, we employ a specialized loss function tailored to our approach. Our methodology begins with pretraining AC-Net using the LiTS2017 dataset and subsequently fine-tunes it using computed tomography (CT) and magnetic resonance imaging (MRI) data sourced from Hubei Cancer Hospital. Results: The performance metrics for AC-Net on CT data are as follows: dice similarity coefficient (DSC) of 0.90, Jaccard coefficient (JC) of 0.82, recall of 0.92, average symmetric surface distance (ASSD) of 4.59, Hausdorff distance (HD) of 11.96, and precision of 0.89. For AC-Net on MRI data, the metrics are DSC of 0.80, JC of 0.70, recall of 0.82, ASSD of 7.58, HD of 30.26, and precision of 0.84. Conclusion: The comparative experiments highlight that AC-Net exhibits exceptional tumor recognition accuracy when tested on the Hubei Cancer Hospital dataset, demonstrating highly competitive performance for practical clinical applications. Furthermore, the ablation experiments provide conclusive evidence of the efficacy of each module proposed in this article. For those interested, the code for this research article can be accessed at the following GitHub repository: https://github.com/killian-zero/py_tumor-segmentation.git .

Objective and Impact Statement: High-intensity focused ultrasound (HIFU) therapy is a promising noninvasive method that induces coagulative necrosis in diseased tissues through thermal and cavitation effects, while avoiding surrounding damage to surrounding normal tissues. Introduction: Accurate and real-time acquisition of the focal region temperature field during HIFU treatment marked enhances therapeutic efficacy, holding paramount scientific and practical value in clinical cancer therapy. Methods: In this paper, we initially designed and assembled an integrated HIFU system incorporating diagnostic, therapeutic, and temperature measurement functionalities to collect ultrasound echo signals and temperature variations during HIFU therapy. Furthermore, we introduced a novel multimodal teacher–student model approach, which utilizes the shared self-expressive coefficients and the deep canonical correlation analysis layer to aggregate each modality data, then through knowledge distillation strategies, transfers the knowledge from the teacher model to the student model. Results: By investigating the relationship between the phantoms, in vitro, and in vivo ultrasound echo signals and temperatures, we successfully achieved real-time reconstruction of the HIFU focal 2D temperature field region with a maximum temperature error of less than 2.5 °C. Conclusion: Our method effectively monitored the distribution of the HIFU temperature field in real time, providing scientifically precise predictive schemes for HIFU therapy, laying a theoretical foundation for subsequent personalized treatment dose planning, and providing efficient guidance for noninvasive, nonionizing cancer treatment.

IntroductionRadiation therapy is a common treatment option for Head and Neck Cancer (HNC), where the accurate segmentation of Head and Neck (HN) Organs-AtRisks (OARs) is critical for effective treatment planning. Manual labeling of HN OARs is time-consuming and subjective. Therefore, deep learning segmentation methods have been widely used. However, it is still a challenging task for HN OARs segmentation due to some small-sized OARs such as optic chiasm and optic nerve.MethodsTo address this challenge, we propose a parallel network architecture called PCG-Net, which incorporates both convolutional neural networks (CNN) and a Gate-Axial-Transformer (GAT) to effectively capture local information and global context. Additionally, we employ a cascade graph module (CGM) to enhance feature fusion through message-passing functions and information aggregation strategies. We conducted extensive experiments to evaluate the effectiveness of PCG-Net and its robustness in three different downstream tasks. ResultsThe results show that PCG-Net outperforms other methods, improves the accuracy of HN OARs segmentation, which can potentially improve treatment planning for HNC patients.DiscussionIn summary, the PCG-Net model effectively establishes the dependency between local information and global context and employs CGM to enhance feature fusion for accurate segment HN OARs. The results demonstrate the superiority of PCGNet over other methods, making it a promising approach for HNC treatment planning.

The measurement of bladder volume is crucial for the diagnosis and treatment of urinary system diseases. Ultrasound imaging, with its non-invasive, radiation-free, and repeatable scanning capabilities, has become the preferred method for measuring residual urine volume. Nevertheless, it still faces some challenges, including complex imaging methods leading to longer measurement times and lower spatial resolution. Here, we propose a novel three-point localization method that does not require ultrasound imaging to calculate bladder volume. A corresponding triple-element ultrasound probe has been designed based on this method, enabling the ultrasound probe to transmit and receive ultrasound waves in three directions. Furthermore, we utilize the Hilbert Transform algorithm to extract the envelope of the ultrasound signal to enhance the efficiency of bladder volume measurements. The experiment indicates that bladder volume estimation can be completed within 5 s, with a relative error rate of less than 15%. These results demonstrate that this novel three-point localization method offers an effective approach for bladder volume measurement in patients with urological conditions.

Purpose/Objective(s): With the development of deep learning, more convolutional neural networks (CNNs) are being introduced in automatic segmentation to reduce oncologists’ labor requirement. However, it is still challenging for oncologists to spend considerable time evaluating the quality of the contours generated by the CNNs. Besides, all the evaluation criteria, such as Dice Similarity Coefficient (DSC), need a gold standard to assess the quality of the contours. To address these problems, we propose an automatic quality assurance (QA) method using isotropic and anisotropic methods to automatically analyze contour quality without a gold standard. Materials/Methods: We used 196 individuals with 18 different head-and-neck organs-at-risk. The overall process has the following 4 main steps. (1) Use CNN segmentation network to generate a series of contours, then use these contours as organ masks to erode and dilate to generate inner/outer shells for each 2D slice. (2) Thirty-eight radiomics features were extracted from these 2 shells, using the inner/outer shells’ radiomics features ratios and DSCs as the input for 12 machine learning models. (3) Using the DSC threshold adaptively classified the passing/un-passing slices. (4) Through 2 different threshold analysis methods quantitatively evaluated the un-passing slices and obtained a series of location information of poor contours. Parts 1-3 were isotropic experiments, and part 4 was the anisotropic method. Result: From the isotropic experiments, almost all the predicted values were close to the labels. Through the anisotropic method, we obtained the contours’ location information by assessing the thresholds of the peak-to-peak and area-to-area ratios. Conclusion: The proposed automatic segmentation QA method could predict the segmentation quality qualitatively. Moreover, the method can analyze the location information for un-passing slices.

Lead halide perovskites are widely used e.g. in solar cells and LEDs, but devices based on thermal properties have received little attention. Here, the authors take advantage of the thermal properties to fabricate an optoacoustic transducer with both broad bandwidth and high conversion efficiency.

Ultrasound is extensively studied for biomedical engineering applications. As the core part of the ultrasonic system, the ultrasound transducer plays a significant role. For the purpose of meeting the requirement of precision medicine, the main challenge for the development of ultrasound transducer is to further enhance its performance. In this article, an overview of recent developments in ultrasound transducer technologies that use a variety of material strategies and device designs based on both the piezoelectric and photoacoustic mechanisms is provided. Practical applications are also presented, including ultrasound imaging, ultrasound therapy, particle/cell manipulation, drug delivery, and nerve stimulation. Finally, perspectives and opportunities are also highlighted.

PurposeAccurate segmentation of liver and liver tumors is critical for radiotherapy. Liver tumor segmentation, however, remains a difficult and relevant problem in the field of medical image processing because of the various factors like complex and variable location, size, and shape of liver tumors, low contrast between tumors and normal tissues, and blurred or difficult-to-define lesion boundaries. In this paper, we proposed a neural network (S-Net) that can incorporate attention mechanisms to end-to-end segmentation of liver tumors from CT images.MethodsFirst, this study adopted a classical coding-decoding structure to realize end-to-end segmentation. Next, we introduced an attention mechanism between the contraction path and the expansion path so that the network could encode a longer range of semantic information in the local features and find the corresponding relationship between different channels. Then, we introduced long-hop connections between the layers of the contraction path and the expansion path, so that the semantic information extracted in both paths could be fused. Finally, the application of closed operation was used to dissipate the narrow interruptions and long, thin divide. This eliminated small cavities and produced a noise reduction effect.ResultsIn this paper, we used the MICCAI 2017 liver tumor segmentation (LiTS) challenge dataset, 3DIRCADb dataset and doctors’ manual contours of Hubei Cancer Hospital dataset to test the network architecture. We calculated the Dice Global (DG) score, Dice per Case (DC) score, volumetric overlap error (VOE), average symmetric surface distance (ASSD), and root mean square error (RMSE) to evaluate the accuracy of the architecture for liver tumor segmentation. The segmentation DG for tumor was found to be 0.7555, DC was 0.613, VOE was 0.413, ASSD was 1.186 and RMSE was 1.804. For a small tumor, DG was 0.3246 and DC was 0.3082. For a large tumor, DG was 0.7819 and DC was 0.7632.ConclusionS-Net obtained more semantic information with the introduction of an attention mechanism and long jump connection. Experimental results showed that this method effectively improved the effect of tumor recognition in CT images and could be applied to assist doctors in clinical treatment.

Photoacoustic transmitter represents a promising substitute for conventional piezoelectric counterparts. However, lack of easy and effective method for dynamically manipulating the focused acoustic field is a common and tricky problem faced by current photoacoustic technology. In this paper, a new strategy for constructing focus tunable photoacoustic transmitter is proposed. Different from existed prevailing device architecture, a sandwich like photoacoustic conversion layer is innovatively designed into a suspending elastic membrane with clamped boundary and it can be deformed using integrated pneumatic actuator. Owing to the membrane deflection property, concave spherical contours with variable radius of curvature can be obtained. Considering the shape determined sound emission characteristic, continuous tuning on the axial focusing length of the acoustic field has been successfully demonstrated in the photoacoustic transmitter for the first time. Besides, acoustic signal with significantly improved negative pressure has also been achieved especially at the focus, bringing additional advantage for applications.

Due to the benefit of multiple parallel channels within single cladding and easy implementation of multi-input multioutput technique, we present the spatial division multiplexing (SDM)-based reflective intensity-modulated fiber optics displacement sensor (RIMFODS). Under the condition of single-input single-output, we experimentally compare the transfer function of RIMFODS by using a pair of standard single-mode fibers (SSMFs), single seven-core-single-mode fiber, and single seven-core-few-mode fiber. In comparison with the conventional SSMF-based RIMFODS, the use of seven-core-few-mode fiber can reduce the dead zone of measurement range from 200 to 100 μm. Meanwhile, the system sensitivity can be enhanced from 0.14 to 12.2 nW/μm. In particular, with the help of two-input-five-output technique, the dead zone can be further reduced to 70 μ m, with sensitivity reaching as high as 53.87 nW/μm. Finally, we theoretically investigate the effect of both the core size and the core spacing of multicore fiber on the transfer function of RIMFODS. Such an SDM-based RIMFODS with compact footprint has lots of potential to be exploited.

Based on a modified dice-and-fill technique, a PIN-PMN-PT single crystal 1-3 composite with the kerf of 12 μm and pitch of 50 μm was prepared. The as-made piezoelectric composite material behaved with high piezoelectric constant (d33 = 1500 pC/N), high electromechanical coefficient (kt = 0.81), and low acoustic impedance (16.2 Mrayls). Using lithography and flexible circuit method, a 48-element phased array was successfully fabricated from such a piezoelectric composite. The array element was measured to have a central frequency of 20 MHz and a fractional bandwidth of approximately 77% at −6 dB. Of particular significance was that this PIN-PMN-PT single crystal 1-3 composite-based phased array exhibits a superior insertion loss compared with PMN-PT single crystal and PZT-5H-based 20 MHz phased arrays. The focusing and steering capabilities of the obtained phased array were demonstrated theoretically and experimentally. These promising results indicate that the PIN-PMN-PT single crystal 1-3 composite-based high frequency phased array is a good candidate for ultrasound imaging applications.

In this paper, a high-temperature fiber sensor based on an optical fiber Fabry-Perot interferometer is fabricated by splicing a section of simplified hollow-core fiber between two single-mode fibers (SMFs) and cleaving one of the two SMFs to a certain length. With the superposition of three beams of light reflected from two splicing joints and end face of the cleaved SMF, the modified Vernier effect will be generated in the proposed structure and improve the sensitivity of temperature measurement. The envelope of spectrum reflected from the proposed sensor head is modulated by the ambient temperature of the sensor head. By monitoring and measuring the shift of spectrum envelope, the measurement of environment temperature is carried out experimentally, and high temperature sensitivity of 1.019 nm/°C for the envelope of the reflected spectrum was obtained. A temperature measurement as high as 1050 °C has been achieved with excellent repeatability.

Single-beam acoustic tweezers (SBAT), used in laboratory-on-a-chip (LOC) device has promising implications for an individual micro-particle contactless manipulation. In this study, a freestanding hydrothermal PZT thick film with excellent piezoelectric property (d33 = 270pC/N and kt = 0.51) was employed for SBAT applications and a press-focusing technology was introduced. The obtained SBAT, acting at an operational frequency of 50MHz, a low f-number (∼0.9), demonstrated the capability to trap and manipulate a micro-particle sized 10μm in the distilled water. These results suggest that such a device has great potential as a manipulator for a wide range of biomedical and chemical science applications.