Your browser doesn't support javascript.
loading
Show: 20 | 50 | 100
Results 1 - 20 de 199
Filter
2.
Microsyst Nanoeng ; 10(1): 128, 2024 Sep 12.
Article in English | MEDLINE | ID: mdl-39261463

ABSTRACT

Wearable ultrasound imaging technology has become an emerging modality for the continuous monitoring of deep-tissue physiology, providing crucial health and disease information. Fast volumetric imaging that can provide a full spatiotemporal view of intrinsic 3D targets is desirable for interpreting internal organ dynamics. However, existing 1D ultrasound transducer arrays provide 2D images, making it challenging to overcome the trade-off between the temporal resolution and volumetric coverage. In addition, the high driving voltage limits their implementation in wearable settings. With the use of microelectromechanical system (MEMS) technology, we report an ultrasonic phased-array transducer, i.e., a 2D piezoelectric micromachined ultrasound transducer (pMUT) array, which is driven by a low voltage and is chip-compatible for fast 3D volumetric imaging. By grouping multiple pMUT cells into one single drive channel/element, we propose an innovative cell-element-array design and operation of a pMUT array that can be used to quantitatively characterize the key coupling effects between each pMUT cell, allowing 3D imaging with 5-V actuation. The pMUT array demonstrates fast volumetric imaging covering a range of 40 mm × 40 mm × 70 mm in wire phantom and vascular phantom experiments, achieving a high temporal frame rate of 11 kHz. The proposed solution offers a full volumetric view of deep-tissue disorders in a fast manner, paving the way for long-term wearable imaging technology for various organs in deep tissues.

3.
IEEE Trans Med Imaging ; PP2024 Aug 21.
Article in English | MEDLINE | ID: mdl-39167523

ABSTRACT

Histological image registration is a fundamental task in histological image analysis. It is challenging because of substantial appearance differences due to multiple staining. Keypoint correspondences, i.e., matched keypoint pairs, have been introduced to guide unsupervised deep learning (DL) based registration methods to handle such a registration task. This paper proposes an iterative keypoint correspondence-guided (IKCG) unsupervised network for non-rigid histological image registration. Fixed deep features and learnable deep features are introduced as keypoint descriptors to automatically establish keypoint correspondences, the distance between which is used as a loss function to train the registration network. Fixed deep features extracted from DL networks that are pre-trained on natural image datasets are more discriminative than handcrafted ones, benefiting from the deep and hierarchical nature of DL networks. The intermediate layer outputs of the registration networks trained on histological image datasets are extracted as learnable deep features, which reveal unique information for histological images. An iterative training strategy is adopted to train the registration network and optimize learnable deep features jointly. Benefiting from the excellent matching ability of learnable deep features optimized with the iterative training strategy, the proposed method can solve the local non-rigid large displacement problem, an inevitable problem usually caused by misoperation, such as tears in producing tissue slices. The proposed method is evaluated on the Automatic Non-rigid Histology Image Registration (ANHIR) website and AutomatiC Registration Of Breast cAncer Tissue (ACROBAT) website. It ranked 1st on both websites as of August 6th, 2024.

4.
Ultrasonics ; 143: 107409, 2024 Sep.
Article in English | MEDLINE | ID: mdl-39053242

ABSTRACT

COVID-19 pneumonia severity assessment is of great clinical importance, and lung ultrasound (LUS) plays a crucial role in aiding the severity assessment of COVID-19 pneumonia due to its safety and portability. However, its reliance on qualitative and subjective observations by clinicians is a limitation. Moreover, LUS images often exhibit significant heterogeneity, emphasizing the need for more quantitative assessment methods. In this paper, we propose a knowledge fused latent representation framework tailored for COVID-19 pneumonia severity assessment using LUS examinations. The framework transforms the LUS examination into latent representation and extracts knowledge from regions labeled by clinicians to improve accuracy. To fuse the knowledge into the latent representation, we employ a knowledge fusion with latent representation (KFLR) model. This model significantly reduces errors compared to approaches that lack prior knowledge integration. Experimental results demonstrate the effectiveness of our method, achieving high accuracy of 96.4 % and 87.4 % for binary-level and four-level COVID-19 pneumonia severity assessments, respectively. It is worth noting that only a limited number of studies have reported accuracy for clinically valuable exam level assessments, and our method surpass existing methods in this context. These findings highlight the potential of the proposed framework for monitoring disease progression and patient stratification in COVID-19 pneumonia cases.


Subject(s)
COVID-19 , Lung , Severity of Illness Index , Ultrasonography , COVID-19/diagnostic imaging , Humans , Ultrasonography/methods , Lung/diagnostic imaging , SARS-CoV-2 , Image Interpretation, Computer-Assisted/methods
5.
Neural Netw ; 179: 106515, 2024 Jul 07.
Article in English | MEDLINE | ID: mdl-39032393

ABSTRACT

Accurate image reconstruction is crucial for photoacoustic (PA) computed tomography (PACT). Recently, deep learning has been used to reconstruct PA images with a supervised scheme, which requires high-quality images as ground truth labels. However, practical implementations encounter inevitable trade-offs between cost and performance due to the expensive nature of employing additional channels for accessing more measurements. Here, we propose a masked cross-domain self-supervised (CDSS) reconstruction strategy to overcome the lack of ground truth labels from limited PA measurements. We implement the self-supervised reconstruction in a model-based form. Simultaneously, we take advantage of self-supervision to enforce the consistency of measurements and images across three partitions of the measured PA data, achieved by randomly masking different channels. Our findings indicate that dynamically masking a substantial proportion of channels, such as 80%, yields meaningful self-supervisors in both the image and signal domains. Consequently, this approach reduces the multiplicity of pseudo solutions and enables efficient image reconstruction using fewer PA measurements, ultimately minimizing reconstruction error. Experimental results on in-vivo PACT dataset of mice demonstrate the potential of our self-supervised framework. Moreover, our method exhibits impressive performance, achieving a structural similarity index (SSIM) of 0.87 in an extreme sparse case utilizing only 13 channels, which outperforms the performance of the supervised scheme with 16 channels (0.77 SSIM). Adding to its advantages, our method can be deployed on different trainable models in an end-to-end manner, further enhancing its versatility and applicability.

6.
Article in English | MEDLINE | ID: mdl-38976462

ABSTRACT

Ultrasound Localization Microscopy (ULM), an emerging medical imaging technique, effectively resolves the classical trade-off between resolution and penetration inherent in traditional ultrasound imaging, opening up new avenues for noninvasive observation of the microvascular system. However, traditional microbubble tracking methods encounter various practical challenges. These methods typically entail multiple processing stages, including intricate steps like pairwise correlation and trajectory optimization, rendering real-time applications unfeasible. Furthermore, existing deep learning-based tracking techniques neglect the temporal aspects of microbubble motion, leading to ineffective modeling of their dynamic behavior. To address these limitations, this study introduces a novel approach called the Gated Recurrent Unit (GRU)-based Multitasking Temporal Neural Network (GRU-MT). GRU-MT is designed to simultaneously handle microbubble trajectory tracking and trajectory optimization tasks. Additionally, we enhance the nonlinear motion model initially proposed by Piepenbrock et al. to better encapsulate the nonlinear motion characteristics of microbubbles, thereby improving trajectory tracking accuracy. In this study, we perform a series of experiments involving network layer substitutions to systematically evaluate the performance of various temporal neural networks, including Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), GRU, Transformer, and its bidirectional counterparts, on the microbubble trajectory tracking task. Concurrently, the proposed method undergoes qualitative and quantitative comparisons with traditional microbubble tracking techniques. The experimental results demonstrate that GRU-MT exhibits superior nonlinear modeling capabilities and robustness, both in simulation and in vivo dataset. Additionally, it achieves reduced trajectory tracking errors in shorter time intervals, underscoring its potential for efficient microbubble trajectory tracking. Model code is open-sourced at https://github.com/zyt-Lib/GRU-MT.

7.
Med Biol Eng Comput ; 2024 Jul 11.
Article in English | MEDLINE | ID: mdl-38990410

ABSTRACT

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

8.
Ultrasonics ; 143: 107410, 2024 Sep.
Article in English | MEDLINE | ID: mdl-39084108

ABSTRACT

Ultrasound Localization Microscopy (ULM) surpasses the constraints imposed by acoustic diffraction, achieving sub-wavelength resolution visualization of microvasculature through the precise localization of minute microbubbles (MBs). Nonetheless, the analysis of densely populated regions with overlapping MB point spread responses introduces significant localization errors, limiting the use of technique to low-concentration conditions. This raises a trade-off issue between localization efficiency and MB density. In this work, we present a new deep learning framework that combines Transformer and U-Net architectures, termed ULM-TransUNet. As a non-linear model, it is able to learn the complex data patterns of overlapping MBs in dense conditions for accurate localization. To evaluate the performance of ULM-TransUNet, a series of numerical simulations and in vivo experiments are carried out. Numerical simulation results indicate that ULM-TransUNet achieves high-quality ULM imaging, with improvements of 21.93 % in detection rate, 17.36 % in detection precision, and 20.53 % in detection sensitivity, compared to previous state-of-the-art deep learning (DL) method (e.g., ULM-UNet). For the in vivo experiments, ULM-TransUNet achieves the highest spatial resolution (9.4 µm) and rapid inference speed (26.04 ms/frame). Furthermore, it consistently detects more small vessels and resolves closely spaced vessels more effectively. The outcomes of this work imply that ULM-TransUNet can potentially enhance the microvascular imaging performance on high-density MB conditions.


Subject(s)
Microbubbles , Ultrasonography , Animals , Ultrasonography/methods , Microscopy/methods , Deep Learning , Microvessels/diagnostic imaging , Mice , Computer Simulation
9.
Phys Chem Chem Phys ; 26(20): 14874-14882, 2024 May 22.
Article in English | MEDLINE | ID: mdl-38738516

ABSTRACT

Perovskite/organic bulk heterojunction (BHJ) integrated solar cells have tremendous development potential to exceed the Shockley-Queisser limit efficiency of single-junction photovoltaics, due to the merits of spectra response extension. However, the presence of energy level barriers and severe non-radiative recombination at the interface between perovskite and BHJ greatly hindered the transport and collection of charge carriers, usually leading to large Voc and photocurrent loss, as well as the stability degradation of integrated devices. Therefore, investigating the interface properties of perovskite/BHJ is crucial for understanding the charge transport process and enhancing device performance. In this study, we effectively regulated the interface properties and charge transport in perovskite/BHJ integrated devices using a thermal annealing process. Using Kelvin probe microscopy, photoluminescence, and transient absorption spectroscopy, we revealed that moderate annealing treatment would contribute to forming close interface contact and provide more channels or pathways for charge transfer, which is advantageous for the interface charge collection and device performance. In addition, the lone pair electrons of acyl, thiophene and pyrrole function groups in polymer PDPP3T and PCBM can act as the Lewis base and provide electrons to the under-coordinated lead atoms or clusters in the perovskite, effectively passivating traps on the surface and grain boundaries of the perovskite through Lewis acid-base coordination. Finally, we improved the photovoltaic conversion efficiency of the device to 21.57% with enhanced stability using an optimized thermal annealing process. This study provides a comprehensive understanding of the integrated perovskite/BHJ interface properties, which could be extended to other optoelectronic devices based on a similar integrated structure.

10.
Article in English | MEDLINE | ID: mdl-38607709

ABSTRACT

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.

11.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi ; 41(2): 262-271, 2024 Apr 25.
Article in Chinese | MEDLINE | ID: mdl-38686406

ABSTRACT

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


Subject(s)
Elastic Modulus , Elasticity Imaging Techniques , Image Processing, Computer-Assisted , Neural Networks, Computer , Phantoms, Imaging , Elasticity Imaging Techniques/methods , Image Processing, Computer-Assisted/methods , Humans , Algorithms , Deep Learning
12.
Article in English | MEDLINE | ID: mdl-38319765

ABSTRACT

Ultrafast power Doppler imaging (uPDI) can significantly increase the sensitivity of resolving small vascular paths in ultrasound. While clutter filtering is a fundamental and essential method to realize uPDI, it commonly uses singular value decomposition (SVD) to suppress clutter signals and noise. However, current SVD-based clutter filters using two cutoffs cannot ensure sufficient separation of tissue, blood, and noise in uPDI. This article proposes a new competitive swarm-optimized SVD clutter filter to improve the quality of uPDI. Specifically, without using two cutoffs, such a new filter introduces competitive swarm optimization (CSO) to search for the counterparts of blood signals in each singular value. We validate the CSO-SVD clutter filter on public in vivo datasets. The experimental results demonstrate that our method can achieve higher contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and blood-to-clutter ratio (BCR) than the state-of-the-art SVD-based clutter filters, showing a better balance between suppressing clutter signals and preserving blood signals. Particularly, our CSO-SVD clutter filter improves CNR by 0.99 ± 0.08 dB, SNR by 0.79 ± 0.08 dB, and BCR by 1.95 ± 0.03 dB when comparing a spatial-similarity-based SVD clutter filter in the in vivo dataset of rat brain bolus.


Subject(s)
Signal Processing, Computer-Assisted , Ultrasonography, Doppler , Rats , Animals , Phantoms, Imaging , Blood Flow Velocity , Ultrasonography, Doppler/methods , Ultrasonography/methods
13.
World J Clin Cases ; 11(31): 7640-7646, 2023 Nov 06.
Article in English | MEDLINE | ID: mdl-38078136

ABSTRACT

BACKGROUND: Severely elevated intracranial pressure due to various reasons, such as decreased cerebral perfusion, can lead to devastating neurological outcomes, such as brain herniation. Decompression craniectomy is a life-saving procedure that is commonly performed for such a critical situation, but the changes in cerebral microvessels after brain herniation and decompression are unclear. Ultrafast power Doppler imaging (uPDI) is a new microvascular imaging technology that utilizes high frame rate plane/diverging wave transmission and advanced clutter filters. uPDI significantly improves Doppler sensitivity and can detect microvessels, which are usually invisible using traditional ultrasound Doppler imaging. CASE SUMMARY: In this report, uPDI was used for the first time to observe the brain blood flow of a hypoperfusion area in a 4-year-old girl who underwent decompression craniectomy due to refractory intracranial hypertension (ICP) after malignant brain tumor surgery. B-mode imaging was used to verify the increased densities of the cerebral cortex and basal ganglia that were observed by computed tomography. CONCLUSION: uPDI showed the local blood supplies and anatomical structures of the patient after decompressive craniectomy. uPDI is potentially a more intuitive and noninvasive method for evaluating the effects of severe ICP on cerebral microvessels.

14.
IEEE Trans Image Process ; 32: 4501-4516, 2023.
Article in English | MEDLINE | ID: mdl-37540607

ABSTRACT

Volumetric (3D) ultrasound imaging using a 2D matrix array probe is increasingly developed for various clinical procedures. However, 3D ultrasound imaging suffers from motion artifacts due to tissue motions and a relatively low frame rate. Current Doppler-based motion compensation (MoCo) methods only allow 1D compensation in the in-range dimension. In this work, we propose a new 3D-MoCo framework that combines 3D velocity field estimation and a two-step compensation strategy for 3D diverging wave compounding imaging. Specifically, our framework explores two constraints of a round-trip scan sequence of 3D diverging waves, i.e., Doppler and pair-wise optical flow, to formulate the estimation of the 3D velocity fields as a global optimization problem, which is further regularized by the divergence-free and first-order smoothness. The two-step compensation strategy is to first compensate for the 1D displacements in the in-range dimension and then the 2D displacements in the two mutually orthogonal cross-range dimensions. Systematical in-silico experiments were conducted to validate the effectiveness of our proposed 3D-MoCo method. The results demonstrate that our 3D-MoCo method achieves higher image contrast, higher structural similarity, and better speckle patterns than the corresponding 1D-MoCo method. Particularly, the 2D cross-range compensation is effective for fully recovering image quality.

15.
Article in English | MEDLINE | ID: mdl-37478034

ABSTRACT

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

16.
World J Gastrointest Endosc ; 15(5): 376-385, 2023 May 16.
Article in English | MEDLINE | ID: mdl-37274559

ABSTRACT

BACKGROUND: Painless gastroenteroscopy is a widely developed diagnostic and treatment technology in clinical practice. It is of great significance in the clinical diagnosis, treatment, follow-up review and other aspects of gastric cancer patients. The application of anesthesia techniques during manipulation can be effective in reducing patient fear and discomfort. In clinical work, the adverse drug reactions of anesthesia regimens and the risk of serious adverse drug reactions are increased with the increase in propofol application dose application dose; the application of opioid drugs often causes gastrointestinal reactions, such as nausea, vomiting and delayed gastrointestinal function recovery, after examination. These adverse effects can seriously affect the quality of life of patients. AIM: To observe the effect of modified ShengYangYiwei decoction on gastrointestinal function, related complications and immune function in patients with gastric cancer during and after painless gastroscopy. METHODS: A total of 106 patients with gastric cancer, who were selected from January 2022 to September 2022 in Xiamen Traditional Chinese Medicine Hospital for painless gastroscopy, were randomly divided into a treatment group (n = 56) and a control group (n = 50). Before the examination, all patients fasted for 8 h, provided their health education, and confirmed if there were contraindications to anesthesia and gastroscopy. During the examination, the patients were placed in the left decubitus position, the patients were given oxygen through a nasal catheter (6 L/min), the welling needle was opened for the venous channel, and a multifunction detector was connected for monitoring electrocardiogram, oxygen saturation, blood pressure, etc. Naporphl and propofol propofol protocols were used for routine anesthesia. Before anesthesia administration, the patients underwent several deep breathing exercises, received intravenous nalbuphine [0.nalbuphine (0.025 mg/kg)], followed by intravenous propofol [1.propofol (1.5 mg/kg)] until the palpebral reflex disappeared, and after no response, gastroscopy was performed. If palpebral reflex disappeared, and after no response, gastroscopy was performed. If any patient developed movement, frowning, or hemodynamic changes during the operation (heart rate changes during the operation (heart rate increased to > 20 beats/min, systolic blood pressure increased to > 20% of the base value), additional propofol [0.propofol (0.5 mg/kg)] was added until the patient was sedated again. The patients in the treatment group began to take the preventive intervention of Modified ShengYangYiwei decoction one week before the examination, while the patients in the control group received routine gastrointestinal endoscopy. The patients in the two groups were examined by conventional painless gastroscopy, and the characteristics of the painless gastroscopies of the patients in the two groups were recorded and compared. These characteristics included the total dosage of propofol during the examination, the incidence of complications during the operation, the time of patients' awakening, the time of independent activities, and the gastrointestinal function of the patients after examination, such as the incidence of reactions such as malignant vomiting, abdominal distension and abdominal pain, as well as the differences in the levels of various immunological indicators and inflammatory factors before anesthesia induction (T0), after conscious extubation (T1) and 24 h after surgery (T2). RESULTS: There was no difference in the patients' general information, American Society of Anesthesiologist classification or operation time between the two groups before treatment. In terms of painless gastroscopy, the total dosage of propofol in the treatment group was lower than that in the control group (P < 0.05), and the time of awakening and autonomous activity was significantly faster than that in the control group (P < 0.05). During the examination, the incidence of hypoxemia, hypotension and hiccups in the treatment group was significantly lower than that in the control group (P < 0.01). In terms of gastrointestinal function, the incidences of nausea, vomiting, abdominal distension and abdominal pain in the treatment group after examination were significantly lower than those in the control group (P < 0.01). In terms of immune function, in both groups, the number of CD4+ and CD8+ cells decreased significantly (P < 0.05), and the number of natural killer cells increased significantly (P < 0.05) at T1 and T2, compared with T0. The number of CD4+ and CD8+ cells in the treatment group at the T1 and T2 time points was higher than that in the control group (P < 0.05), while the number of natural killer cells was lower than that in the control group (P < 0.05). In terms of inflammatory factors, compared with T0, the levels of interleukin (IL) -6 and tumor necrosis factor-alpha in patients in the two groups at T1 and T2 increased significantly and then decreased (P < 0.05). The level of IL-6 at T1 and T2 in the treatment group was lower than that in the control group (P < 0.05). CONCLUSION: The preoperative use of modified ShengYangYiwei decoction can optimize the anesthesia program during painless gastroscopy, improve the gastrointestinal function of patients after the operation, reduce the occurrence of examination-related complications.

17.
Ultrasonics ; 134: 107058, 2023 Sep.
Article in English | MEDLINE | ID: mdl-37295222

ABSTRACT

Detection of end-diastole (ED) and end-systole (ES) frames in echocardiography video is a critical step for assessment of cardiac function. A recently released large public dataset, i.e., EchoNet-Dynamic, could be used as a benchmark for cardiac event detection. However, only a pair of ED and ES frames are annotated in each echocardiography video and the annotated ED comes before ES in most cases. This means that only a few frames during systole in each video are utilizable for training, which makes it challenging to train a cardiac event detection model using the dataset. Semi-supervised learning (SSL) could alleviate the problems. An architecture combining convolutional neural network (CNN), recurrent neural network (RNN) and fully-connected layers (FC) is adopted. Experimental results indicate that SSL brings at least three benefits: faster convergence rate, performance improvement and more reasonable volume curves. The best mean absolute errors (MAEs) for ED and ES detection are 40.2 ms (2.1 frames) and 32.6 ms (1.7 frames), respectively. In addition, the results show that models trained on apical four-chamber (A4C) view could work well on other standard views, such as other apical views and parasternal short axis (PSAX) views.


Subject(s)
Cardiovascular Diseases , Echocardiography , Humans , Echocardiography/methods , Systole , Neural Networks, Computer , Supervised Machine Learning
18.
J Ultrasound Med ; 42(10): 2277-2292, 2023 Oct.
Article in English | MEDLINE | ID: mdl-37146242

ABSTRACT

OBJECTIVE: The purpose of this study is to detect the hemodynamic changes of microvessels in the early stage of diabetic kidney disease (DKD) and to test the feasibility of ultrasound localization microscopy (ULM) in early diagnosis of DKD. METHODS: In this study, streptozotocin (STZ) induced DKD rat model was used. Normal rats served as the control group. Conventional ultrasound, contrast-enhanced ultrasound (CEUS), and ULM data were collected and analyzed. The kidney cortex was divided into four segments, which are 0.25-0.5 mm (Segment 1), 0.5-0.75 mm (Segment 2), 0.75-1 mm (Segment 3), and 1-1.25 mm (Segment 4) away from the renal capsule, respectively. The mean blood flow velocities of arteries and veins in each segment were separately calculated, and also the velocity gradients and overall mean velocities of arteries and veins. Mann-Whitney U test was used for comparison of the data. RESULTS: Quantitative results of microvessel velocity obtained by ULM show that the arterial velocity of Segments 2, 3, and 4, and the overall mean arterial velocity of the four segments in the DKD group are significantly lower than those in the normal group. The venous velocity of Segment 3 and the overall mean venous velocity of the four segments in the DKD group are higher than those in the normal group. The arterial velocity gradient in the DKD group is lower than that in the normal group. CONCLUSION: ULM can visualize and quantify the blood flow and may be used for early diagnosis of DKD.


Subject(s)
Diabetes Mellitus , Diabetic Nephropathies , Rats , Animals , Diabetic Nephropathies/diagnostic imaging , Feasibility Studies , Microscopy , Kidney , Ultrasonography
19.
Anesthesiology ; 139(2): 173-185, 2023 08 01.
Article in English | MEDLINE | ID: mdl-37079748

ABSTRACT

BACKGROUND: The administration of epinephrine after severe refractory hypotension, shock, or cardiac arrest restores systemic blood flow and major vessel perfusion but may worsen cerebral microvascular perfusion and oxygen delivery through vasoconstriction. The authors hypothesized that epinephrine induces significant microvascular constriction in the brain, with increased severity after repetitive dosing and in the aged brain, eventually leading to tissue hypoxia. METHODS: The authors investigated the effects of intravenous epinephrine administration in healthy young and aged C57Bl/6 mice on cerebral microvascular blood flow and oxygen delivery using multimodal in vivo imaging, including functional photoacoustic microscopy, brain tissue oxygen sensing, and follow-up histologic assessment. RESULTS: The authors report three main findings. First, after epinephrine administration, microvessels exhibited severe immediate vasoconstriction (57 ± 6% of baseline at 6 min, P < 0.0001, n = 6) that outlasted the concurrent increase in arterial blood pressure, while larger vessels demonstrated an initial increase in flow (108 ± 6% of baseline at 6 min, P = 0.02, n = 6). Second, oxyhemoglobin decreased significantly within cerebral vessels with a more pronounced effect in smaller vessels (microvessels to 69 ± 8% of baseline at 6 min, P < 0.0001, n = 6). Third, oxyhemoglobin desaturation did not indicate brain hypoxia; on the contrary, brain tissue oxygen increased after epinephrine application (from 31 ± 11 mmHg at baseline to 56 ± 12 mmHg, 80% increase, P = 0.01, n = 12). In the aged brains, microvascular constriction was less prominent yet slower to recover compared to young brains, but tissue oxygenation was increased, confirming relative hyperoxia. CONCLUSIONS: Intravenous application of epinephrine induced marked cerebral microvascular constriction, intravascular hemoglobin desaturation, and paradoxically, an increase in brain tissue oxygen levels, likely due to reduced transit time heterogeneity.


Subject(s)
Microscopy , Oxyhemoglobins , Mice , Animals , Microcirculation , Oxyhemoglobins/pharmacology , Epinephrine/pharmacology , Oxygen , Cerebrovascular Circulation
20.
Article in English | MEDLINE | ID: mdl-37028058

ABSTRACT

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


Subject(s)
Microvessels , Ultrasonography, Doppler , Humans , Ultrasonography/methods , Phantoms, Imaging , Microvessels/diagnostic imaging , Image Processing, Computer-Assisted/methods
SELECTION OF CITATIONS
SEARCH DETAIL