Single View Research Articles

Axial image restoration of a single dome window view for stereo vision in unmanned underwater vehicles (UUV)

Unmanned Underwater Vehicles (UUVs) are typically used for monitoring and inspection tasks where it’s critical to locate objects and create 3D models of the surroundings. One of the most dependable techniques for accomplishing 3D localization and modeling jobs is stereo vision. This isn’t the case in underwater conditions, though, as cameras will record distorted views, which results in inaccurate 3D estimation. In real-world applications, it is more practicable and a more difficult task to place the two cameras of the stereo vision system inside a single dome glass. The goal of this study is to recover the axial image views of the two cameras within a single dome window from the refracted views that were recorded. The cameras’ position (along the Z and X axes) and orientation (pan and tilt) within a single dome window—of which there are 448 possible combinations—are taken into account by the kinematics model. The approach has been improved and validated using around 18 million experiments. Lastly, when compared to the original axial images, our approach may report axial images with an average overlapped similarity of 96%.

Single view generalizable 3D reconstruction based on 3D Gaussian splatting

3D Gaussian Splatting (3DGS) has become a significant research focus in recent years, particularly for 3D reconstruction and novel view synthesis under non-ideal conditions. Among these studies, tasks involving sparse input data have been further classified, with the most challenging scenario being the reconstruction of 3D structures and synthesis of novel views from a single input image. In this paper, we introduce SVG3D, a method for generalizable 3D reconstruction from a single view, based on 3DGS. We use a state-of-the-art monocular depth estimator to obtain depth maps of the scenes. These depth maps, along with the original scene images, are fed into a U-Net network, which predicts the parameters for 3D Gaussian ellipsoids corresponding to each pixel. Unlike previous work, we do not stratify the predicted 3D Gaussian ellipsoids but allow the network to learn the positioning autonomously. This design enables accurate geometric representation when rendered from the target camera view, significantly enhancing novel view synthesis accuracy. We trained our model on the RealEstate10K dataset and performed both quantitative and qualitative analysis on the test set. We compared single-view novel view 3D reconstruction methods across different 3D representation techniques, including methods based on Multi-Plane Image (MPI) representation, hybrid MPI and Neural Radiance Fields representation, and the current state-of-the-art methods using 3DGS representation for single-view novel view reconstruction. These comparisons substantiated the effectiveness and accuracy of our method. Additionally, to assess the generalizability of our network, we validated it across the NYU and KITTI datasets, and the results confirmed its robust cross-dataset generalization capability.

Microcirculatory resistance based on a single angiographic view in ST-segment elevation myocardial infarction patients

BackgroundAngio-based microvascular resistance (AMR) was proposed as a tool to quantitatively assess coronary microvascular based on single angiographic projection. The aims of this study are to assess the diagnostic accuracy and prognostic significance of AMR in ST-segment elevation myocardial infarction (STEMI) patients.MethodsAMR was measured (Of these, 22 patients measured index of microvascular resistance (IMR)) in 70 STEMI patients after primary percutaneous coronary intervention (pPCI). ST-segment resolution (STR) was assessed 2 h after pPCI simultaneously. Transthoracic echocardiography was performed within 1 day and approximately 1 year after pPCI. STEMI patients underwent pPCI were followed up for 7.3 years and the primary endpoint was the major adverse cardiac and cerebral events (MACCEs).ResultsAMR showed significant correlations with IMR (R = 0.334, P = 0.005). AMR has good predictive power for STR after pPCI (area under the curve: 0.889, sensitivity: 94.59%, specificity: 75.76%) in receiver operating characteristic (ROC) curve. Low-AMR patients showed markedly improved left ventricular ejection fraction (LVEF) 1 year after pPCI (42(40–49) vs. 41(39–44), P = 0.041). High-AMR patients showed higher risk for MACCEs than those with Low-AMR (HR = 3.90, P = 0.02). In multivariate cox regression analysis, AMR was considered an independent predictor of MACCEs (HR: 1.153, P = 0.020).ConclusionsAMR is a reliable tool for the estimation of microvascular resistance and prognosis in the absence of intracoronary pressure-temperature sensor wire and adenosine based on single angiographic projection.

WidgetSketcher: Single-View and Sketch-Based 3D Modeling with Widget Manipulation

Current sketch modeling methods face challenges in terms of efficiency and quality due to frequent view changes. To empower users to create models seamlessly and accurately from a single reference drawing, we present WidgetSketcher, a single-view, sketch-based modeling method that incorporates widget manipulations. To ensure seamlessness, we implement a single-view workflow in sketch-based modeling with designed widget manipulations and other interactions. Each user stroke is equipped with a transient axis widget, allowing 3D pose specification and part creation from a single view. To improve accuracy, we develop a two-stage shape generation algorithm, ensuring the precise generation of shapes with detailed features and asymmetry. Our method enables the quick and accurate creation of 3D models consistent with reference drawings, while eliminating frequent view changes. To demonstrate the efficiency of our method, we evaluate the system usability by conducting a user study and compare the results to state-of-the-art techniques.

Traumatic hip dislocations in children and adolescents: diagnostic challenges and the significance of MRI imaging—a multi-center study

BackgroundTraumatic hip dislocations in children and adolescents are rare but can lead to severe outcomes like avascular necrosis. Delayed reductions, often due to overlooked dislocations in initial imaging, pose a major risk. The variability in symptoms and emergency care challenges early diagnosis. This multi-center study evaluates diagnostic approaches to enhance protocols for identifying traumatic hip dislocations in childhood.MethodsThis retrospective multi-center study included 76 patients (aged ≤ 17 years) with acute traumatic hip dislocations and open growth plates from 16 German hospitals. Patient data and imaging from 1979 to 2022 were analyzed, with statistical evaluation performed using SPSS under ethical guidelines.ResultsX-rays (single and biplanar views) were the primary diagnostic method, utilized in 85% of cases. Dislocations were missed in 12% (9 cases), primarily among children under eight years, with half of those under four. Delayed reductions (15.8%, n = 12) were linked to undetected dislocations in imaging in 9 cases. Conventional X-rays frequently missed dislocations, whereas MRI successfully identified all cases. Among the 76 patients, 54 (71%) had associated injuries, with 57.9% (n = 44) diagnosed exclusively via MRI.ConclusionTimely diagnosis of traumatic hip dislocations is crucial, as delays increase the risk of femoral head necrosis. An algorithmic approach is essential for young children, where dislocations may not be readily suspected. MRI is vital in the secondary diagnostic phase, providing superior visualization of associated injuries, including acetabular avulsions and soft tissue interpositions highlighting the need for integration of MRI into a unified diagnostic algorithm for children suspected of such injuries.Level of evidenceIV.

Structured 3D gaussian splatting for novel view synthesis based on single RGB-LiDAR View

Structured 3D gaussian splatting for novel view synthesis based on single RGB-LiDAR View

Deep learning for echocardiographic assessment and risk stratification of aortic, mitral, and tricuspid regurgitation: the DELINEATE-Regurgitation study.

Classification and risk stratification in aortic (AR), mitral (MR), and tricuspid regurgitation (TR) remains a significant clinical challenge. This study aimed to develop an artificial intelligence (AI) system to assess valvular regurgitation and stratify MR progression risk. Using transthoracic echocardiograms (TTEs) at two sites (internal development/test, external test), the DELINEATE-Regurgitation system was developed to classify AR, MR, and TR severity using color Doppler videos. Methods of summating video-level classifications into study-level predictions were tested, comparing single view with multiview approaches integrating predictions across multiple videos. Model agreement with cardiologists was assessed by weighted kappa. A separate AI system (DELINEATE-MR-Progression) analyzing color Doppler videos was developed to predict which patients with mild, mild-moderate, and moderate MR were most likely to progress to moderate-severe or severe MR with analysis by Kaplan-Meier and Cox proportional hazards models. A total of 71,660 TTEs with 1,203,980 color Doppler videos were included. The weighted kappa in internal/external test sets for regurgitation classification was 0.81/0.76 for AR, 0.76/0.72 for MR, and 0.73/0.64 for TR using a multiview approach intaking all color Doppler videos in a study, demonstrating substantial agreement with cardiologist interpretation with superiority of multiview over single view approaches. In the progression analysis, the AI score stratified MR progression risk even when controlled for clinical factors known to be associated with MR progression (internal test set hazard ratio 4.1 [95% confidence interval 2.5-6.6]). An AI system can accurately classify AR, MR, and TR and predict MR progression beyond currently known risk factors.

Single View Techniques for Modelling Coronary Pressures Losses. Comment on Tsigkas et al. Rapid and Precise Computation of Fractional Flow Reserve from Routine Two-Dimensional Coronary Angiograms Based on Fluid Mechanics: The Pilot FFR2D Study. J. Clin. Med. 2024, 13, 3831.

We have read the research article "Rapid and Precise Computation of Fractional Flow Reserve from Routine Two-Dimensional Coronary Angiograms Based on Fluid Mechanics: The Pilot FFR2D Study" by Tsigkas et al [...].

A Discriminative Prediction Strategy Based on Multi-View Knowledge Transfer for Dynamic Multi-Objective Optimization

Dynamic multi-objective optimization problems (DMOPs) are widely encountered in engineering optimization processes, characterized by conflicting objectives that change over time. Evolutionary transfer optimization (ETO) has recently emerged as a promising optimization paradigm for addressing DMOPs. ETO-based dynamic multi-objective evolutionary algorithms (DMOEAs) usually adopt discriminative prediction strategies to select high-quality initial solutions in new environments by transferring knowledge from the source domain, thereby accelerating the evolutionary process. However, DMOPs often change in both decision and objective spaces. Existing methods rely on a single view from the source domain for knowledge transfer, which can overlook crucial knowledge from other views, potentially affecting the selection of high-quality initial solutions and limiting optimization performance. To address this, we propose a discriminative prediction strategy based on multi-view knowledge transfer for DMOEA, called MKT-DMOEA. Specifically, we construct discriminative predictors from the view of the decision space and objective space in the source domain. Each discriminative predictor effectively reduces the differences between environments under the current view through a transfer strategy based on geometric feature transformation. Meanwhile, these predictors make full use of the transferred knowledge to achieve accurate discriminative predictions under the current view. Finally, we integrate the discriminative prediction results from multiple views to select high-quality initial solutions. Experimental results demonstrate that our proposed algorithm outperforms four other state-of-the-art DMOEAs in terms of both diversity and convergence on the well-known CEC 2018 dynamic multi-objective optimization benchmark suite DF.

DQRNet: Dynamic Quality Refinement Network for 3D Reconstruction from a Single Depth View.

With the widespread adoption of 3D scanning technology, depth view-driven 3D reconstruction has become crucial for applications such as SLAM, virtual reality, and autonomous vehicles. However, due to the effects of self-occlusion and environmental occlusion, obtaining complete and error-free 3D shapes directly from 3D scans remains challenging, as previous reconstruction methods tend to lose details. To this end, we propose Dynamic Quality Refinement Network (DQRNet) for reconstructing complete and accurate 3D shape from a single depth view. DQRNet introduces a dynamic encoder-decoder and a detail quality refiner to generate high-resolution 3D shapes, where the former designs a dynamic latent extractor to adaptively select important parts of an object and the latter designs global and local point refiners to enhance the reconstruction quality. Experimental results show that DQRNet is able to focus on capturing the details at boundaries and key areas on ShapeNet dataset, thereby achieving better accuracy and robustness than SOTA methods.

Some Critical Thoughts on "How Stereotypes Deceive Us"

The goal of this paper is to identify and discuss the weaker aspects of some of the arguments in Kathy Puddifoot’s fascinating and thought-provoking book. Section 1 deals with Puddifoot’s treatment of the “single factor view” and the “dual factor view” of stereotyping. Section 2 deals with Puddifoot’s treatment of egalitarian attitudes. Section 3 deals with Puddifoot’s treatment of the moral encroachment approach to stereotyping. Finally, section 4 deals with Puddifoot’s theory of evaluative dispositionalism. The sections can be read independently.

Development of a Telerobotic Target‐Specific Pesticide Applicator: An Intervention for Enhanced Safety and Efficiency

ABSTRACTAdvances in computing and electronics are finding applications in agriculture. These facilitate the automation of farm operations performed manually under greenhouse structures. During chemical application, farm workers are exposed to toxic chemicals and experience fatigue and drudgery with traditional spraying equipment. The traditional application methods apply chemicals uniformly, without considering the spatial variability of the canopy, which adversely impacts the economy and environment. To mitigate these, a telerobotic target‐specific pesticide applicator (robot) was designed and developed for real‐time application of pesticides based on the presence and height of foliage. It consists of a prime mover, an ultrasonic sensor‐based target‐specific pesticide application system with electronic control for navigation and spraying systems. The robot is operated from outside the greenhouse by an operator sitting in a safe environment through real‐time video. The percentage reduction in pesticide use during greenhouse field evaluation was 24.95% with activation compared to without the activation of sensors. The theoretical field capacity, effective field capacity, and field efficiency of the robot are 0.20 ha h−1, 0.15 ha h−1, and 75% respectively. The maximum range of wireless communication between the operator and the robot was 121 m. The root mean square error of deviation from a straight‐line path was 55 mm. In the context of user interface design for human–robot interaction, two display devices (a dashboard display and a first‐person view head‐mounted display [HMD]) were evaluated, along with peripheral vision (single view and adjustable multiple views). Results showed that adjustable multiple views with the HMD demonstrated statistically significant improvements in effectiveness, efficiency, and system usability scores compared with single view and dashboard display. The potential dermal exposures of the operators with a rechargeable knapsack sprayer (RKP) and with a developed robot during spraying activity were 53.75 ± 0.99 mL h−1 and zero, respectively. This means the developed robot eliminates exposure to the operator. The ergonomic parameters—heart rate, body part discomfort score, and overall discomfort score—using an RKP are 104.1 ± 6 beats min−1, 27.3 ± 2.0, and 3.5 ± 0.2, respectively, while with the developed robot, they were 99 ± 4 beats min−1, 12.1 ± 1.1, and 1.0 ± 0.5, respectively. The operating cost of the developed robot was 1.74 times higher than that of the RKP, but it offers a more precise pesticide application, reducing chemical wastage and environmental impact. The robot completes spraying on a 1500 m² greenhouse in 0.75 h compared with 4 h with a knapsack sprayer, reducing spraying time by 5.3 times and enhancing efficiency, cost savings, and operator well‐being.

Robust Identification Algorithm of Network Communication Signals via Machine Learning Model

The efficiency of communication processing and control depends heavily on the recognition of network signals, however irregularities and mistakes frequently arise during the application process. In this work, we leverage machine learning models to automatically identify computer network communication signals, leveraging recent advancements in artificial intelligence technology. In the simulation, we employed a support vector machine (SVM) model, and we utilized parameter optimization to address the overlearning issue. The process of classifying modulation signals involves the extraction of feature parameters through the application of support vector machine and radial basis function neural network (RBFNN) models, respectively. Real-world network communication involves the observation and collection of signals from various viewpoints or feature spaces. These views provide a variety of detailed insights into the signal, and feature extraction is carried out for each view to produce the associated feature vectors. An extensive description of the signal can be generated by extracting the features from several viewpoints. Various viewpoints' feature vectors are combined and synthesized. The robustness of signal recognition can be increased and the bias and inaccuracy that could be generated by a single view can be minimized by combining the data from several perspectives. The support vector machine performs better than the radial basis function neural network, according to experimental findings. When the signal-to-noise ratio (SNR) is high, network communication signals function effectively. However, the latter (RBFNN) performs significantly worse in low SNR settings whilst the former (SVM) retains good accuracy. Therefore, when it comes to computer network communication signals, the support vector machine model is thought to be more reliable.

Fast Non-Rigid Radiance Fields from Monocularized Data.

The reconstruction and novel view synthesis of dynamic scenes recently gained increased attention. As reconstruction from large-scale multi-view data involves immense memory and computational requirements, recent benchmark datasets provide collections of single monocular views per timestamp sampled from multiple (virtual) cameras. We refer to this form of inputs as monocularized data. Existing work shows impressive results for synthetic setups and forward-facing real-world data, but is often limited in the training speed and angular range for generating novel views. This paper addresses these limitations and proposes a new method for full 360° inward-facing novel view synthesis of non-rigidly deforming scenes. At the core of our method are: 1) An efficient deformation module that decouples the processing of spatial and temporal information for accelerated training and inference; and 2) A static module representing the canonical scene as a fast hash-encoded neural radiance field. In addition to existing synthetic monocularized data, we systematically analyze the performance on real-world inward-facing scenes using a newly recorded challenging dataset sampled from a synchronized large-scale multi-view rig. In both cases, our method is significantly faster than previous methods, converging in less than 7 minutes and achieving real-time framerates at 1K resolution, while obtaining a higher visual accuracy for generated novel views. Our code and dataset are available online: https://github.com/MoritzKappel/MoNeRF.

Deep learning for the single-view echocardiographic assessment of biventricular ejection fractions: the dual-task EF2Net model

Abstract Background 2D echocardiography requires multiple views for the assessment of left ventricular (LV) systolic function and does not enable the calculation of right ventricular (RV) ejection fraction (EF). 3D echocardiography (3DE) has clear incremental value over 2D echocardiography; nevertheless, its availability and feasibility is limited. Purpose We aimed to develop a dual-task deep learning model – EF2Net – for predicting 3DE-derived LVEF and RVEF from 2D apical 4-chamber (A4C) view echocardiographic videos. Methods The EF2Net model comprises two video transformers, which were first pre-trained on 29,424 unlabeled A4C videos from 15,533 echocardiographic studies in a self-supervised fashion. In the subsequent supervised training phase, one of the transformers was trained for predicting LVEF on the publicly available EchoNet-Dynamic dataset and a dual-center international 3DE dataset comprising 5,341 labeled A4C videos from 1,408 echocardiographic studies, whereas the other transformer was trained for predicting RVEF only on the latter. Beyond testing the model internally on 20% of the dual-center dataset (i.e., internal test set), it was also validated in a labeled external validation set comprising (1) 244 A4C videos of 244 patients with different cardiac diseases and available outcome data and (2) 4,421 A4C videos of 853 healthy adults from the World Alliance of Societies of Echocardiography (WASE) study. Last, we evaluated the model on a low-risk, community-based cohort (1,166 unlabeled A4C videos of 1,166 individuals) with a 10-year follow-up to investigate the associations between the predictions and all-cause mortality. Results In the internal test set and the labeled external validation set, EF2Net predicted 3DE-derived LVEF with a mean absolute error (MAE) of 4.58 and 4.67 percentage points, respectively, whereas it achieved an MAE of 4.82 and 5.43 percentage points in predicting 3DE-derived RVEF. In the labeled external validation set, the model identified an LVEF <50% and an RVEF <45% with an area under the receiver operating characteristic curve of 0.95 and 0.86, respectively. In patients with cardiac diseases, the EF2Net-predicted LVEF and RVEF values were associated with the composite of all-cause death and heart failure hospitalization (LVEF – HR: 0.94 [0.91-0.98], p=0.001; RVEF – HR: 0.92 [0.88-0.98], p=0.004) independent of age and sex. Moreover, in the community-based cohort, the EF2Net-predicted EF values were also associated with 10-year all-cause mortality (LVEF – HR: 0.97 [0.95-0.99], p=0.026; RVEF – HR: 0.91 [0.88-0.95], p<0.001) independent of age, sex, and LV diastolic function. Conclusions EF2Net enabled the automated and accurate assessment of biventricular systolic function based on a single 2D echocardiographic view. It also exhibited robust performance when validated in a multi-ethnic dataset, and the prognostic value of its predictions was confirmed in patients with cardiac diseases and in the community.

Influence of Obesity, Race and Gender on Radiation Exposure for Epidural Procedures.

This retrospective, case controlled, comparative evaluation review of radiation exposure during epidural procedures in interventional pain management assessed variations in radiation exposure based on obesity, race, and gender. Numerous publications have shown increasing radiation exposure based on body mass index (BMI).However, the influence of race and gender have not been studied. A recent study assessing radiation exposure for epidural procedures with mandated 2 views, compared to a single view or optional 2 views, showed an increase in exposure time of 21%, with radiation dose increases of 133%. The influence of obesity, has been studied, but potential influence of race, and gender on radiation exposure for epidural procedures has not been studied. The present review shows a prevalence of total obesity with BMI of 30 or above of 50.9%, with a higher prevalence in patients below the age of 45, female gender, and African Americans. Mean fluoroscopy time and dose increased significantly from 6.9s to 0.925 milligray (mGy) for all epidural procedures for those who are underweight to 10.0s and 6.7 mGy for those with Class 3 obesity above BMI of 40. However, after adjusting for body mass index, no significant differences were noted in race for both fluoroscopy time and radiation dose across multiple epidural procedures. Fluoroscopy time and radiation dose increased in males in cervical interlaminar and lumbar transforaminal procedures.

ComNet: A Multiview Deep Learning Model for Predicting Drug Combination Side Effects.

As combination therapy becomes more common in clinical applications, predicting adverse effects of combination medications is a challenging task. However, there are three limitations of the existing prediction models. First, they rely on a single view of the drug and cannot fully utilize multiview information, resulting in limited performance when capturing complex structures. Second, they ignore subgraph information at different scales, which limits the ability to model interactions between subgraphs. Third, there has been limited research on effectively integrating multiview features of molecules. Therefore, we propose ComNet, a deep learning model that improves the accuracy of side effect prediction by integrating multiview features of drugs. First, to capture diverse features of drugs, a multiview feature extraction module is proposed, which not only uses molecular fingerprints but also extracts semantic information on SMILES and spatial information on 3D conformations. Second, to enhance the modeling ability of complex structures, a multiscale subgraph fusion mechanism is proposed, which can fuse local and global graph structures of drugs. Finally, a multiview feature fusion mechanism is proposed, which uses an attention mechanism to adaptively adjust the weights of different views to achieve multiview data fusion. Experiments on several publicly available data sets show that ComNet performs better than existing methods in various complex scenarios, especially in cold-start scenarios. Ablation experiments show that each core structure in ComNet contributes to the overall performance. Further analysis shows that ComNet not only converges rapidly and has good generalization ability but also identifies different substructures in the molecule. Finally, a case study on a self-collected data set validates the superior performance of ComNet in practical applications.

Memory-based predictions prime perceptual judgments across head turns in immersive, real-world scenes.

Memory-based predictions prime perceptual judgments across head turns in immersive, real-world scenes.

Osfit™, a Novel Stent Designed for the Treatment of Coronary Ostial Lesions: Initial Clinical Experience and Intravascular Ultrasound Evaluation.

Stent implantation for coronary ostial lesions is challenging. This study evaluated the feasibility, safety, and immediate procedural results of the Osfit™, innovatively designed sirolimus drug-eluting stent delivery system for the treatment of coronary ostial lesions. The Osfit™ has a 5 mm long extended balloon proximal to the stent (bare balloon without mounted stent). When inflated at 1.5 atm, only bare balloon inflated and acts like a stopper. We evaluated the technical feasibility in 49 patients with a coronary ostial lesion (defined as stenosis within 3 mm from the origin of the vessel) who had received the Osfit™. Intravascular ultrasound (IVUS) analysis was done in 11 patients who consented to IVUS examination, and the depth from the proximal edge of the stent to the ostial plane (DSO) was measured. In all 49 lesions, stents were successfully implanted in one single angiographic view without obvious stent protrusion or definite angiographic missing of the ostium. The proportions of aorto-ostial, and bifurcation lesions were 28.6% and 71.4%, respectively. The DSO was 0.2±0.69 mm, and the proximal stent edges were located within 1 mm of the ostial edge in all patients. No adverse events like death, myocardial infarction, target lesion revascularization, target vessel revascularization or stent thrombosis were reported during the in-hospital period or within 30 days. The implantation of the Osfit™ for coronary ostial lesions appears to be an accurate and safe procedure that may reduce multiple angiographic projections and advanced skills.

Advancing Single-Plane Wave Ultrasound Imaging with Implicit Multi-Angle Acoustic Synthesis via Deep Learning.

Plane Wave Imaging (PWI) is pivotal in medical ultrasound, prized for its ultrafast capabilities essential for real-time physiological monitoring. Traditionally, enhancing image quality in PWI has necessitated an increase in the number of plane waves, unfortunately compromising its hallmark high frame rates. To fully leverage the frame rate advantage of PWI, existing deep-learning-based methods often employ single-plane wave (PW) as the sole input for training strategies to replicate multi-PWs compounding results. However, these typically fail to capture the intricate information provided by steered waves. In response, we have developed a sophisticated architecture that implicitly integrates multi-angle information by generating and dynamically combining virtual steered plane waves within the network. Employing deep learning techniques, this system creates virtual steered waves from the single primary input view, simulating a limited number of steering angles. These virtual PWs are then expertly merged with actual single PW data through an advanced attention mechanism. Through implicit multi-angle acoustic synthesis, our approach achieves the high-quality output typically associated with extensive multi-angle compounding. Rigorously evaluated on datasets acquired from simulations, experimental phantoms, and in vivo targets, our method has demonstrated superior performance over traditional single-plane wave strategies by providing more stable, reliable, and robust imaging outcomes. It excels in restoring detailed speckle patterns and diagnostic characteristics crucial for in vivo imaging, thereby offering a promising advancement in PWI technology without sacrificing speed. The code of the network is publicly available at https://github.com/yijiaLiu12/Implicit-Plane-Wave-Synthesis.