Revealing the impact of thermal annealing on the perovskite/organic bulk heterojunction interface in photovoltaic devices.

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.

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

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

[Reconstruction of elasticity modulus distribution base on semi-supervised neural network].

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.

Competitive Swarm Optimized SVD Clutter Filtering for Ultrafast Power Doppler Imaging.

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.

Ultrafast power Doppler imaging for ischemic encephalopathy: A case report.

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.

Doppler and Pair-Wise Optical Flow Constrained 3D Motion Compensation for 3D Ultrasound Imaging.

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.

Improved Ultrafast Power Doppler Imaging Using United Spatial-Angular Adaptive Scaling Wiener Postfilter.

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.

Semi-supervised learning improves the performance of cardiac event detection in echocardiography.

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.

Effect of modified ShengYangYiwei decoction on painless gastroscopy and gastrointestinal and immune function in gastric cancer patients.

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.

Evaluation of Early Diabetic Kidney Disease Using Ultrasound Localization Microscopy: A Feasibility Study.

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.

Epinephrine-induced Effects on Cerebral Microcirculation and Oxygenation Dynamics Using Multimodal Monitoring and Functional Photoacoustic Microscopy.

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.

High-Quality Ultrafast Power Doppler Imaging Based on Spatial Angular Coherence Factor.

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.

Convolutional Neural Network-Based Speckle Tracking for Ultrasound Strain Elastography: An Unsupervised Learning Approach.

Accurate and computationally efficient motion estimation is a critical component of real-time ultrasound strain elastography (USE). With the advent of deep-learning neural network models, a growing body of work has explored supervised convolutional neural network (CNN)-based optical flow in the framework of USE. However, the above-said supervised learning was often done using simulated ultrasound data. The research community has questioned whether simulated ultrasound data containing simple motion can train deep-learning CNN models that can reliably track complex in vivo speckle motion. In parallel with other research groups' efforts, this study developed an unsupervised motion estimation neural network (UMEN-Net) for USE by adapting a well-established CNN model named PWC-Net. Our network's input is a pair of predeformation and postdeformation radio frequency (RF) echo signals. The proposed network outputs both axial and lateral displacement fields. The loss function consists of a correlation between the predeformation signal and the motion-compensated postcompression signal, smoothness of the displacement fields, and tissue incompressibility. Notably, an innovative correlation method known as the globally optimized correspondence (GOCor) volumes module developed by Truong et al. was used to replace the original Corr module to enhance our evaluation of signal correlation. The proposed CNN model was tested using simulated, phantom, and in vivo ultrasound data containing biologically confirmed breast lesions. Its performance was compared against other state-of-the-art methods, including two deep-learning-based tracking methods (MPWC-Net++ and ReUSENet) and two conventional tracking methods (GLUE and BRGMT-LPF). In summary, compared with the four known methods mentioned above, our unsupervised CNN model not only obtained higher signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) for axial strain estimates but also improved the quality of the lateral strain estimates.

Ultrafast Ultrasound Localization Microscopy by Conditional Generative Adversarial Network.

Ultrasound localization microscopy (ULM) overcomes the acoustic diffraction limit and enables the visualization of microvasculature at subwavelength resolution. However, challenges remain in ultrafast ULM implementation, where short data acquisition time, efficient data processing speed, and high imaging resolution need to be considered simultaneously. Recently, deep learning (DL)-based methods have exhibited potential in speeding up ULM imaging. Nevertheless, a certain number of ultrasound (US) data ( L frames) are still required to accumulate enough localized microbubble (MB) events, leading to an acquisition time within a time span of tens of seconds. To further speed up ULM imaging, in this article, we present a new DL-based method, termed as ULM-GAN. By using a modified conditional generative adversarial network (cGAN) framework, ULM-GAN is able to reconstruct a superresolution image directly from a temporal mean low-resolution (LR) image generated by averaging l -frame raw US images with l being significantly smaller than L . To evaluate the performance of ULM-GAN, a series of numerical simulations and phantom experiments are both implemented. The results of the numerical simulations demonstrate that when performing ULM imaging, ULM-GAN allows  âˆ¼ 40 -fold reduction in data acquisition time and  âˆ¼ 61 -fold reduction in computational time compared with the conventional Gaussian fitting method, without compromising spatial resolution according to the resolution scaled error (RSE). For the phantom experiments, ULM-GAN offers an implementation of ULM with ultrafast data acquisition time (  âˆ¼ 0.33 s) and ultrafast data processing speed (  âˆ¼ 0.60 s) that makes it promising to observe rapid biological activities in vivo.

[The developments and applications of functional ultrasound imaging].

In recent years, due to the emergence of ultrafast ultrasound imaging technology, the sensitivity of detecting slow and micro blood flow with ultrasound has been dramatically improved, and functional ultrasound imaging (fUSI) has been developed. fUSI is a novel technology for neurological imaging that utilizes neurovascular coupling to detect the functional activity of the central nervous system (CNS) with high spatiotemporal resolution and high sensitivity, which is dynamic, non-invasive or minimally invasive. fUSI fills the gap between functional magnetic resonance imaging (fMRI) and optical imaging with its high accessibility and portability. Moreover, it is compatible with electrophysiological recording and optogenetics. In this paper, we review the developments of fUSI and its applications in neuroimaging. To date, fUSI has been used in various animals ranging from mice to non-human primates, as well as in clinical surgeries and bedside functional brain imaging of neonates. In conclusion, fUSI has great potential in neuroscience research and is expected to become an important tool for neuroscientists, pathologists and pharmacologists.

Hessian filter-assisted full diameter at half maximum (FDHM) segmentation and quantification method for optical-resolution photoacoustic microscopy.

Optical-resolution photoacoustic microscopy has been validated as an ideal tool for angiographic studies. Quantitative vascular analysis reveals critical information where vessel segmentation plays the key step. The comm-only used Hessian filter method suffers from varying accuracy due to the multi-kernel strategy. In this work, we developed a Hessian filter-assisted, adaptive thresholding vessel segmentation algorithm. Its performance is validated by a digital phantom and in vivo images which demonstrates a superior and consistent accuracy of 0.987 regardless of kernel selection. Subtle vessel change detection is further tested in two longitudinal studies on blood pressure agents. In the antihypotensive case, the proposed method detected a twice larger vasoconstriction over the Hessian filter method. In the antihypertensive case, the proposed method detected a vasodilation of 21.2%, while the Hessian filter method failed in change detection. The proposed algorithm may further push the limit of quantitative imaging on angiographic applications.

Encoder-decoder deep learning network for simultaneous reconstruction of fluorescence yield and lifetime distributions.

A time-domain fluorescence molecular tomography in reflective geometry (TD-rFMT) has been proposed to circumvent the penetration limit and reconstruct fluorescence distribution within a 2.5-cm depth regardless of the object size. In this paper, an end-to-end encoder-decoder network is proposed to further enhance the reconstruction performance of TD-rFMT. The network reconstructs both the fluorescence yield and lifetime distributions directly from the time-resolved fluorescent signals. According to the properties of TD-rFMT, proper noise was added to the simulation training data and a customized loss function was adopted for self-supervised and supervised joint training. Simulations and phantom experiments demonstrate that the proposed network can significantly improve the spatial resolution, positioning accuracy, and accuracy of lifetime values.

Multi-segmented feature coupling for jointly reconstructing initial pressure and speed of sound in photoacoustic computed tomography.

SIGNIFICANCE: Photoacoustic computed tomography (PACT) is a fast-growing imaging modality. In PACT, the image quality is degraded due to the unknown distribution of the speed of sound (SoS). Emerging initial pressure (IP) and SoS joint-reconstruction methods promise reduced artifacts in PACT. However, previous joint-reconstruction methods have some deficiencies. A more effective method has promising prospects in preclinical applications. AIM: We propose a multi-segmented feature coupling (MSFC) method for SoS-IP joint reconstruction in PACT. APPROACH: In the proposed method, the ultrasound detectors were divided into multiple sub-arrays with each sub-array and its opposite counterpart considered to be a pair. The delay and sum algorithm was then used to reconstruct two images based on a subarray pair and estimated a direction-specific SoS, based on image correlation and the orientation of the subarrays. Once the data generated by all pairs of subarrays were processed, an image that was optimized in terms of minimal feature splitting in all directions was generated. Further, based on the direction-specific SoS, a model-based method was used to directly reconstruct the SoS distribution. RESULTS: Both phantom and animal experiments demonstrated feasibility and showed promising results compared with conventional methods, with less splitting and blurring and fewer distortions. CONCLUSIONS: The developed MSFC method shows promising results for both IP and SoS reconstruction. The MSFC method will help to optimize the image quality of PACT in clinical applications.

Influence of key parameters on motion artifacts in lateral strain estimation with spatial angular compounding.

Strain imaging can reveal the changes in tissue mechanical properties related to pathological alterations by estimating tissue strains in the lateral and axial directions of ultrasound imaging. The estimation performance in the lateral direction is usually worse than that in the axial direction. Spatial angular compounding (SAC) has been demonstrated to improve the quality of lateral estimation by deriving the lateral displacements using axial displacements obtained from multi-angle transmissions. However, motion and deformation of tissues during multiple transmissions may cause motion artifacts, and thus deteriorate the quality of strain estimation. These artifacts can be reduced by choosing appropriate imaging parameters. However, few studies have been conducted to evaluate the influences of key parameters in strain estimation, such as the pulse repetition frequency (PRF), the number of steering angles (NSA), and the maximum steering angles (MSA), in terms of performance optimization. Therefore, this study aims to investigate the effects of these parameters through simulations and phantom experiments. The performance of strain estimation is evaluated by measuring the root-mean-square error (RMSE) and the standard deviation (SD) in the simulations and phantom experiments, respectively. The contrast-to-noise ratio (CNR) of strain images is calculated in both the simulations and phantom experiments. The results show that motion artifacts in strain estimation can be reduced by increasing the PRF to 1 kHz. When the PRF reaches 1 kHz, further increase of the PRF shows little obvious improvement in strain estimation. An increase in the NSA can cause larger motion artifacts and deteriorate the quality of strain images, and the improvement of strain estimation is limited when the NSA is increased from 3 to 7. An NSA of 3 is thus recommended to balance the influences of motion artifacts and the improvement for strain estimation. The MSA has little influence on the motion artifacts, while increased MSA can achieve improved lateral estimation performance at the cost of a smaller imaging region. In light of the lateral strain estimation performance and imaging region, an MSA of 15° is recommended. The influences of these key parameters obtained from this study may provide insights for parameter optimization in strain estimation with SAC to minimize the effects of motion artifacts.

A novel rat model of cerebral small vessel disease and evaluation by super-resolution ultrasound imaging.

Cerebral small vessel disease (CSVD), which causes cognitive, functional and emotional decline, is related to stroke events, and it is a major cause of Alzheimer's disease. In the social context of an aging population, the incidence of CSVD is on the rise yearly, and the exact pathogenesis is still controversial and remains unclear. Exploring the pathological mechanism of CSVD on the histological level using animal models is important for the investigation of new clinical diagnostic methods and treatment options. The existing surgical CSVD model preparation methods are difficult to operate and cannot control the injury location or degree. This study used ultrasound combined with microbubbles (MBs) to induce an easy-to-operate and non-invasive animal model of CSVD with controllable location and degree. The rat model was evaluated from the perspective of histology, ethology, and imageology, respectively. In addition, we utilized super-resolution ultrasound imaging (SR-US) technology to directly observe the microvessels of the model. The histological results showed that the modeling was successful in the preset position, and neurology deficits were observed in 62.5% of 8 rats. The SR-US results of one rat showed that compared with the non-sonication region, the number of cerebral small blood vessels discovered in the sonication area was reduced (43 vs 11), the blood flow speed decreased significantly (p 0.001), and blood flow volume decreased (144.7 vs 11.7 µL/s) because of vasoconstriction. This study provides a new modeling method with controllable damage location and degree for the study of CSVD, and SR-US is found to be an effective evaluation method, which can directly assess the hemodynamic changes of CSVD in vivo.