Cavitation-on-a-Chip Enabled Size-Specific Liposomal Drugs for Selective Pharmacokinetics and Pharmacodynamics.

The size of liposomal drugs has been demonstrated to strongly correlate with their pharmacokinetics and pharmacodynamics. While the microfluidic method successfully achieves the production of liposomes with well-controlled sizes across various buffer/lipid flow rate ratio (FRR) settings, any adjustments to the FRR inevitably influence the concentration, encapsulation efficiency (EE), and stability of liposomal drugs. Here we describe a controllable cavitation-on-a-chip (CCC) strategy that facilitates the precise regulation of liposomal drug size at any desired FRR. The CCC-enabled size-specific liposomes exhibited striking differences in uptake and biodistribution behaviors, thereby demonstrating distinct antitumor efficacy in both tumor-bearing animal and melanoma patient-derived organoid (PDO) models. Intriguingly, as the liposome size decreased to approximately 80 nm, the preferential accumulation of liposomal drugs in the liver transitioned to a predominant enrichment in the kidneys. These findings underscore the considerable potential of our CCC approach in influencing the pharmacokinetics and pharmacodynamics of liposomal nanomedicines.

Sub-Nyquist sampling-based high-frequency photoacoustic computed tomography.

High-frequency (greater than 30 MHz) photoacoustic computed tomography (PACT) provides the opportunity to reveal finer details of biological tissues with high spatial resolution. To record photoacoustic signals above 30 MHz, sampling rates higher than 60 MHz are required according to the Nyquist sampling criterion. However, the highest sampling rates supported by existing PACT systems are typically within the range of 40-60 MHz. Herein, we propose a novel PACT imaging method based on sub-Nyquist sampling. The results of numerical simulation, phantom experiment, and in vivo experiment demonstrate that the proposed imaging method can achieve high-frequency PACT imaging with a relatively low sampling rate. An axial resolution of 22 µm is achieved with a 30-MHz transducer and a 41.67-MHz sampling rate. To the best of our knowledge, this is the highest axial resolution ever achieved in PACT based on a sampling rate of not greater than 60 MHz. This work is expected to provide a practical way for high-frequency PACT imaging with limited sampling rates.

Accurate and robust auto-segmentation of head and neck organ-at-risks based on a novel CNN fine-tuning workflow.

PURPOSE: Obvious inconsistencies in auto-segmentations exist among various AI software. In this study, we have developed a novel convolutional neural network (CNN) fine-tuning workflow to achieve precise and robust localized segmentation. METHODS: The datasets include Hubei Cancer Hospital dataset, Cetuximab Head and Neck Public Dataset, and Québec Public Dataset. Seven organs-at-risks (OARs), including brain stem, left parotid gland, esophagus, left optic nerve, optic chiasm, mandible, and pharyngeal constrictor, were selected. The auto-segmentation results from four commercial AI software were first compared with the manual delineations. Then a new multi-scale lightweight residual CNN model with an attention module (named as HN-Net) was trained and tested on 40 samples and 10 samples from Hubei Cancer Hospital, respectively. To enhance the network's accuracy and generalization ability, the fine-tuning workflow utilized an uncertainty estimation method for automatic selection of candidate samples of worthiness from Cetuximab Head and Neck Public Dataset for further training. The segmentation performances were evaluated on the Hubei Cancer Hospital dataset and/or the entire Québec Public Dataset. RESULTS: A maximum difference of 0.13 and 0.7 mm in average Dice value and Hausdorff distance value for the seven OARs were observed by four AI software. The proposed HN-Net achieved an average Dice value of 0.14 higher than that of the AI software, and it also outperformed other popular CNN models (HN-Net: 0.79, U-Net: 0.78, U-Net++: 0.78, U-Net-Multi-scale: 0.77, AI software: 0.65). Additionally, the HN-Net fine-tuning workflow by using the local datasets and external public datasets further improved the automatic segmentation with the average Dice value by 0.02. CONCLUSION: The delineations of commercial AI software need to be carefully reviewed, and localized further training is necessary for clinical practice. The proposed fine-tuning workflow could be feasibly adopted to implement an accurate and robust auto-segmentation model by using local datasets and external public datasets.

A Novel Three-Point Localization Method for Bladder Volume Estimation.

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.

Heterogeneous all-solid multicore fiber based multipath Michelson interferometer for high temperature sensing.

A compact high temperature sensor utilizing a multipath Michelson interferometer (MI) structure based on weak coupling multicore fiber (MCF) is proposed and experimentally demonstrated. The device is fabricated by program-controlled tapering the spliced region between single mode fiber (SMF) and a segment of MCF. After that, a spherical reflective structure is formed by arc-fusion splicing the end face of MCF. Theoretical analysis has been implemented for this specific multipath MI structure; beam propagation method based simulation and corresponding experiments were performed to investigate the effect of taper and spherical end face on system's performance. Benefiting from the multipath interferences and heterogeneous structure between the center core and surrounding cores of the all-solid MCF, an enhanced temperature sensitivity of 165 pm/°C up to 900°C and a high-quality interference spectrum with 25 dB fringe visibility were achieved.

Modification of microstructure on PZT films for ultrahigh frequency transducer.

Porous Lead zirconate titanate (PZT) films may have promising applications in high frequency ultrasonic transducers for their capability to modify electrical properties for better electrical and acoustic matching. In this work, porous PZT films in range of several micrometers were fabricated using a chemical solution deposition (CSD) method modified with polyvinylpyrrolidone (PVP) as a pore-foaming agent. The crystalline phase, microstructure and electrical properties of the porous films were investigated as a function of PVP contents, molecular weights and annealing temperatures. It was found that the electrical properties were closely associated with the porosity.

Cascaded fiber-optic Fabry-Perot interferometers with Vernier effect for highly sensitive measurement of axial strain and magnetic field.

We report a highly sensitive fiber-optic sensor based on two cascaded intrinsic fiber Fabry-Perot interferometers (IFFPIs). The cascaded IFFPIs have different free spectral ranges (FSRs) and are formed by a short section of hollow core photonic crystal fiber sandwiched by two single mode fibers. With the superposition of reflective spectrum with different FSRs, the Vernier effect will be generated in the proposed sensor and we found that the strain sensitivity of the proposed sensor can be improved from 1.6 pm/µÎµ for a single IFFPI sensor to 47.14 pm/µÎµ by employing the Vernier effect. The sensor embed with a metglas ribbon can be also used to measure the magnetic field according to the similar principle. The sensitivity of the magnetic field measurement is achieved to be 71.57 pm/Oe that is significantly larger than the 2.5 pm/Oe for a single IFFPI sensor.

Temperature compensated magnetic field sensing using dual S-bend structured optical fiber modal interferometer cascaded with fiber Bragg grating.

A temperature compensated magnetic field strength optical fiber sensor has been proposed and experimentally demonstrated. A fiber Bragg grating (FBG) is cascaded to modal interferometer (MI), which is fabricated by dual S-bend splicing between thin fiber (TF) and single mode fiber (SMF) with intentionally controlled misalignment between cores. We established a modified numerical model to describe the multi-mode interference of this exceptional S-bend and misalignment structure, together with the simulation based on beam propagation method to gain insight into its operation mechanism. The FBG is used to interrogate the temperature change, and then compensate the perturbation of temperature on transmission of the MI. Thanks to the proposed dual S-bend structure and the diameter-thinned TF used here; we have obtained high magnetic sensitivity of -0.0678 dB/Oe using only 4 mm TF after the elimination of ambient temperature change.

Attention Connect Network for Liver Tumor Segmentation from CT and MRI Images.

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.

A multi-modal vision-language pipeline strategy for contour quality assurance and adaptive optimization.

Objective.Accurate delineation of organs-at-risk (OARs) is a critical step in radiotherapy. The deep learning generated segmentations usually need to be reviewed and corrected by oncologists manually, which is time-consuming and operator-dependent. Therefore, an automated quality assurance (QA) and adaptive optimization correction strategy was proposed to identify and optimize 'incorrect' auto-segmentations.Approach.A total of 586 CT images and labels from nine institutions were used. The OARs included the brainstem, parotid, and mandible. The deep learning generated contours were compared with the manual ground truth delineations. In this study, we proposed a novel contour quality assurance and adaptive optimization (CQA-AO) strategy, which consists of the following three main components: (1) the contour QA module classified the deep learning generated contours as either accepted or unaccepted; (2) the unacceptable contour categories analysis module provided the potential error reasons (five unacceptable category) and locations (attention heatmaps); (3) the adaptive correction of unacceptable contours module integrate vision-language representations and utilize convex optimization algorithms to achieve adaptive correction of 'incorrect' contours.Main results. In the contour QA tasks, the sensitivity (accuracy, precision) of CQA-AO strategy reached 0.940 (0.945, 0.948), 0.962 (0.937, 0.913), and 0.967 (0.962, 0.957) for brainstem, parotid and mandible, respectively. The unacceptable contour category analysis, the(FI,AccI,Fmicro,Fmacro)of CQA-AO strategy reached (0.901, 0.763, 0.862, 0.822), (0.855, 0.737, 0.837, 0.784), and (0.907, 0.762, 0.858, 0.821) for brainstem, parotid and mandible, respectively. After adaptive optimization correction, the DSC values of brainstem, parotid and mandible have been improved by 9.4%, 25.9%, and 13.5%, and Hausdorff distance values decreased by 62%, 70.6%, and 81.6%, respectively.Significance. The proposed CQA-AO strategy, which combines QA of contour and adaptive optimization correction for OARs contouring, demonstrated superior performance compare to conventional methods. This method can be implemented in the clinical contouring procedures and improve the efficiency of delineating and reviewing workflow.

A Self-Healing Optoacoustic Patch with High Damage Threshold and Conversion Efficiency for Biomedical Applications.

Compared with traditional piezoelectric ultrasonic devices, optoacoustic devices have unique advantages such as a simple preparation process, anti-electromagnetic interference, and wireless long-distance power supply. However, current optoacoustic devices remain limited due to a low damage threshold and energy conversion efficiency, which seriously hinder their widespread applications. In this study, using a self-healing polydimethylsiloxane (PDMS, Fe-Hpdca-PDMS) and carbon nanotube composite, a flexible optoacoustic patch is developed, which possesses the self-healing capability at room temperature, and can even recover from damage induced by cutting or laser irradiation. Moreover, this patch can generate high-intensity ultrasound (> 25 MPa) without the focusing structure. The laser damage threshold is greater than 183.44 mJ cm-2, and the optoacoustic energy conversion efficiency reaches a major achievement at 10.66 × 10-3, compared with other carbon-based nanomaterials and PDMS composites. This patch is also been successfully examined in the application of acoustic flow, thrombolysis, and wireless energy harvesting. All findings in this study provides new insight into designing and fabricating of novel ultrasound devices for biomedical applications.

PRT-Net: a progressive refinement transformer for dose prediction to guide ovarian transposition.

Introduction: Young 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. Methods: For 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 discussion: Therefore, 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.

Design and prediction of laser-induced damage threshold of CNT-PDMS optoacoustic transducer.

The optoacoustic transducer has emerged as a new candidate for medical ultrasound applications and attracts considerable attention. Optoacoustic diagnosis and treatment sometimes require high-intensity acoustic pressure, which is often accompanied by the problem of laser-induced damage. Addressing the laser-induced damage phenomenon from a theoretical perspective holds paramount importance. In this study, the theoretical model of laser-induced damage of the carbon nanotubes-polydimethylsiloxane (CNT-PDMS) composite optoacoustic transducer is established. It is found that this laser-induced damage belongs to thermal ablation damage. Furthermore, the correctness of this theory can be confirmed by experimental results. Most importantly, when the laser energy density is less than threshold value of laser energy density, the optoacoustic transducer can work stable for long time. These encouraging results demonstrate that this work can provide significant guidance for the exploration and utilization of optoacoustic transducers.

Real-Time Reconstruction of HIFU Focal Temperature Field Based on Deep Learning.

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.

Acoustic Cell Patterning for Structured Cell-Laden Hydrogel Fibers/Tubules.

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.

Deep learning for head and neck semi-supervised semantic segmentation.

Objective. Radiation therapy (RT) represents a prevalent therapeutic modality for head and neck (H&N) cancer. A crucial phase in RT planning involves the precise delineation of organs-at-risks (OARs), employing computed tomography (CT) scans. Nevertheless, the manual delineation of OARs is a labor-intensive process, necessitating individual scrutiny of each CT image slice, not to mention that a standard CT scan comprises hundreds of such slices. Furthermore, there is a significant domain shift between different institutions' H&N data, which makes traditional semi-supervised learning strategies susceptible to confirmation bias. Therefore, effectively using unlabeled datasets to support annotated datasets for model training has become a critical issue for preventing domain shift and confirmation bias.Approach. In this work, we proposed an innovative cross-domain orthogon-based-perspective consistency (CD-OPC) strategy within a two-branch collaborative training framework, which compels the two sub-networks to acquire valuable features from unrelated perspectives. More specifically, a novel generative pretext task cross-domain prediction (CDP) was designed for learning inherent properties of CT images. Then this prior knowledge was utilized to promote the independent learning of distinct features by the two sub-networks from identical inputs, thereby enhancing the perceptual capabilities of the sub-networks through orthogon-based pseudo-labeling knowledge transfer.Main results. Our CD-OPC model was trained on H&N datasets from nine different institutions, and validated on the four local intuitions' H&N datasets. Among all datasets CD-OPC achieved more advanced performance than other semi-supervised semantic segmentation algorithms.Significance. The CD-OPC method successfully mitigates domain shift and prevents network collapse. In addition, it enhances the network's perceptual abilities, and generates more reliable predictions, thereby further addressing the confirmation bias issue.

Machine Learning-Based Quality Assurance for Automatic Segmentation of Head-and-Neck Organs-at-Risk in Radiotherapy.

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.

In vivo ocular microvasculature imaging in rabbits with 3D ultrasound localization microscopy.

Morphological and hemodynamic changes in the ocular vasculature are important signs of various ocular diseases. The evaluation of the ocular microvasculature with high resolution is valuable in comprehensive diagnoses. However, it is difficult for current optical imaging techniques to visualize the posterior segment and retrobulbar microvasculature due to the limited penetration depth of light, particularly when the refractive medium is opaque. Thus, we have developed a 3D ultrasound localization microscopy (ULM) imaging method to visualize the ocular microvasculature in rabbits with micron-scale resolution. We used a 32 × 32 matrix array transducer (center frequency: 8 MHz) with a compounding plane wave sequence and microbubbles. Block-wise singular value decomposition spatiotemporal clutter filtering and block-matching 3D denoising were implemented to extract the flowing microbubble signals at different imaging depths with high signal-to-noise ratios. The center points of microbubbles were localized and tracked in 3D space to achieve the micro-angiography. The in vivo results demonstrate the ability of 3D ULM to visualize the microvasculature of the eye in rabbits, where vessels down to 54 µm were successfully revealed. Moreover, the microvascular maps indicated the morphological abnormalities in the eye with retinal detachment. This efficient modality shows potential for use in the diagnosis of ocular diseases.

PCG-net: feature adaptive deep learning for automated head and neck organs-at-risk segmentation.

Introduction: Radiation 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. Methods: To 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. Results: The results show that PCG-Net outperforms other methods, improves the accuracy of HN OARs segmentation, which can potentially improve treatment planning for HNC patients. Discussion: In 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.

WITHDRAWN: Design and prediction of laser-induced damage threshold of CNT-PDMS optoacoustic transducer.

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