Accurate Image Research Articles

A Distance Transformation Deep Forest Framework With Hybrid-Feature Fusion for CXR Image Classification.

Detecting pneumonia, especially coronavirus disease 2019 (COVID-19), from chest X-ray (CXR) images is one of the most effective ways for disease diagnosis and patient triage. The application of deep neural networks (DNNs) for CXR image classification is limited due to the small sample size of the well-curated data. To tackle this problem, this article proposes a distance transformation-based deep forest framework with hybrid-feature fusion (DTDF-HFF) for accurate CXR image classification. In our proposed method, hybrid features of CXR images are extracted in two ways: hand-crafted feature extraction and multigrained scanning. Different types of features are fed into different classifiers in the same layer of the deep forest (DF), and the prediction vector obtained at each layer is transformed to form distance vector based on a self-adaptive scheme. The distance vectors obtained by different classifiers are fused and concatenated with the original features, then input into the corresponding classifier at the next layer. The cascade grows until DTDF-HFF can no longer gain benefits from the new layer. We compare the proposed method with other methods on the public CXR datasets, and the experimental results show that the proposed method can achieve state-of-the art (SOTA) performance. The code will be made publicly available at https://github.com/hongqq/DTDF-HFF.

Fetal body composition reference charts and sexual dimorphism using magnetic resonance imaging

BackgroundThe American Academy of Pediatrics advises that the nutrition of preterm infants should target a body composition similar to that of a fetus in utero. Still, reference charts for intrauterine body composition are missing. Moreover, data on sexual differences in intrauterine body composition during pregnancy are limited. ObjectivesThe objective of this study was to create reference charts for intrauterine body composition from 30 to 36+6 weeks postconception and to evaluate the differences between sexes. MethodsIn this single-center retrospective study, data from 197 normal developing fetuses in late gestation was acquired at 3T magnetic resonance imaging (MRI) scans, including True Fast Imaging with Steady State Free Precession and T1-weighted 2-point Dixon sequences covering the entire fetus. Deep convolutional neural networks were utilized to automatically segment the fetal body and subcutaneous adipose tissue. The fetus’s body mass (BM), fat signal fraction (FSF), fat mass (FM), FM percentage (FM%), fat-free mass (FFM), and FFM percentage (FFM%) were calculated. Using the Generalized Additive Models for Location, Scale, and Shape (GAMLSS) method, reference charts were created, and sexual dimorphism was examined using analysis of covariance (ANCOVA). A P value <0.05 was deemed significant. ResultsThroughout late gestation, BM, FSF, FM, FM%, and FFM increased, while the FFM% decreased. Reference charts for gestational age and sex-specific percentiles are provided. Males exhibited significantly higher BM (7.2%; 95% confidence interval [95% CI]: 1.9, 12.4), FFM (8.8%; 95% CI: 5.8, 11.9), and FFM% (1.7%; 95% CI: 1, 2.4) and lower FSF (−3.6%; 95% CI: −5.6, −1.8) and FM% (−1.7%; 95% CI: −2.4, -1), (P < 0.001) compared with females, with no significant difference in FM between sexes (P = 0.876). ConclusionsMRI-derived intrauterine body composition growth charts are valuable for tracking growth in preterm infants. This study demonstrated that sexual differences in body composition are already present in the intrauterine phase.

Sensing High-Density IC Substrates: Adaptive Fractional Differentiation for Accurate Image Segmentation

Sensing High-Density IC Substrates: Adaptive Fractional Differentiation for Accurate Image Segmentation

Joint [Formula: see text] and Image Reconstruction in Low-Field MRI by Physics-Informed Deep-Learning.

We present a model-based image reconstruction approach based on unrolled neural networks which corrects for image distortion and noise in low-field ( B0 ∼ 50 mT) MRI. Utilising knowledge about the underlying physics, a novel network architecture (SH-Net) is introduced which involves the estimation of spherical harmonic coefficients to guarantee a spatially smooth field map estimate. The SH-Net is integrated in an end-to-end trainable model which jointly estimates the B0-field map as well as the image. Experiments were conducted on retrospectively simulated low-field data of human knees. We compare our model to different model-based approaches at distinct noise levels and various B0-field distributions. Our results show that our physics-informed neural network approach outperforms the purely model-based methods by improving the PSNR up to 11.7% and the RMSE up to 86.3%. Our end-to-end trained model-based approach outperforms existing methods in reconstructing image and B0-field maps in the low-field regime. low-field MRI is becoming increasingly more popular as it enables access to MR in challenging situations such as intensive care units or resource poor areas. Our method allows for fast and accurate image reconstruction in such low-field imaging with B0-inhomogeneity compensation under a wide range of various environmental conditions.

Ultrasound is an accurate imaging modality for diagnosing hip luxation in dogs presenting with hind limb lameness.

To determine the accuracy of ultrasound for diagnosing hip luxation in dogs presenting for hind limb lameness. 24 client-owned dogs presenting with pelvic limb lameness and concern for hip luxation were enrolled in this prospective diagnostic accuracy study from April 1, 2021, to July 1, 2022. An experienced ultrasonographer and a novice ultrasonographer, both masked to the diagnoses, performed hip ultrasonography using a point-of-care ultrasound machine. The experienced evaluator also performed a more comprehensive ultrasonography with a high-end ultrasound machine to characterize concurrent pelvis and hip disease. Pelvic radiographs served as the criterion standard for the diagnosis of hip luxation. Ultrasonographic diagnosis of hip luxation demonstrated high accuracy, ranging from 84.2% to 100% for detecting the presence of luxation and 80.6% to 98.1% for diagnosing the direction of luxation for the novice and experienced evaluators, respectively, with substantial interevaluator agreement (κ = 0.722). Compared to radiographs, ultrasound accurately diagnosed the presence of osteoarthrosis with almost perfect agreement (κ = 0.913) and the presence of fractures of the femoral head/neck and the nonaxial portions of the pelvis with substantial intermodality agreement (κ = 0.775). In 36% of patients, ultrasound identified injury to soft tissue structures that contribute to hip stability, including the gluteal muscles, gemelli, and joint capsule. Ultrasound can be utilized to accurately diagnose the presence of hip luxation and document concurrent hip and pelvic disease. Applications include the use of ultrasonography as part of a routine point-of-care protocol to catalogue injuries in patients with acute trauma and in mobile practice or remote settings where radiography is not readily available.

Analysis of land use land cover change dynamics in Habru District, Amhara Region, Ethiopia

This study examines spatiotemporal changes in Land Use Land Cover (LULC) patterns within Habru District, Amhara Region, Ethiopia, between 1985 and 2021. Employing Landsat imagery and a supervised classification approach, we mapped six LULC classes: water body, settlement area, cultivated land, bare land, shrub land, and forest land. Ground control points (GCP) were obtained through field observations to ensured image accuracy. Complementing the remote sensing analysis, focus group discussions (FGDs) and key informant interviews were conducted to understand local perspectives on LULC changes. The analysis revealed significant LULC transformations over the study period. Between 1985 and 2021, there was a significant increase in cultivated land and settlement areas, which expanded from 12.27 % to 0.23 %–42.12 % and 2.58 % of the total area, respectively. Conversely, shrub and forest lands saw a marked reduction during the same period, declining from 71.52 % to 12.88 %–45.35 % and 7.06 % of the total area, respectively. Water body area showed a minor increase, while bare land exhibited negligible change. Agricultural land and settlement area expansion, population growth, land tenure insecurity, economic challenge, and climatic change were identified as key drivers of observed LULC changes. Our findings underscore the urgency of implementing multifaceted interventions to address these drivers and promote sustainable resource use practices. Such actions are critical for safeguarding Habru District's natural resources and ensuring the long-term viability of ecosystem services.

Enhancing Full Waveform Inversion of Field GPR Data: A Source-Independent Approach with Dynamic Reference Selection via SE-Wave-U-Net

Ground Penetrating Radar (GPR) is a widely-utilized non-destructive detection tool. Full Waveform Inversion (FWI) offers a reliable algorithm for accurately imaging GPR data. Despite its potential, FWI's effectiveness is often compromised by unknown excitation sources, which can lead to computational failures and/or diminished imaging accuracy. Addressing this challenge, this paper introduces a convolution-type objective function designed to mitigate the detrimental effects of these unknown excitation sources on FWI imaging. Critical to the success of this function is the precise selection of reference traces. Further refining the capability for signal separation, we have integrated Squeeze-and-Excitation (SE) modules into the traditional Wave-U-Net architecture, culminating in the development of the SE-Wave-U-Net framework. This framework is adept at dynamically extracting accurate reference traces from field GPR data, which are essential for the accurate FWI imaging. The efficacy, accuracy, and practical applicability of proposed methods are validated through extensive analysis of numerical, laboratory experiments, and field GPR data. The proposed algorithm not only establishes a high-precision computational framework for FWI but also enhances the reliability of GPR imaging in complex environments.

Multichannel meta-imagers based on electrically tunable metasurfaces for accelerating matrix operations.

Optical neural networks have facilitated groundbreaking advancements in machine vision, intelligent healthcare, and autonomous driving systems. However, fixed optical network architectures encounter a dramatic challenge in achieving large-scale parameter tuning without necessitating modifications to the physical components, thereby limiting their capacity to handle intricate application scenarios. Here, a meta-imager based on tunable metasurfaces is proposed, which acts as an optoelectronic front end in collaboration with a digital back end to transfer computationally intensive convolution operations to super integration, highly parallel optical devices. The tunable metasurface integrated liquid crystal (LC) within our system allows for the multiplexing of both polarization and angle, generates multiple convolution channels, and performs both positive and negative convolution operations simultaneously. Moreover, two helicity channels could be tuned by voltages to exponentially double the number of information channels thus overcoming the limitations of fixed information channels in traditional metasurfaces. Our tunable meta-imager achieves 98.5% accuracy for handwritten digitals and 90.9% accuracy for fashion images. Due to its high speed, compactness, and tunable characteristics, this system holds significant potential in the field of artificial intelligence (AI).

MR elastography vs a combination of common non-invasive tests for estimation of severe liver fibrosis in patients with hepatobiliary tumors.

To evaluate the accuracy of combined imaging and blood test indices related to liver fibrosis (LF) compared to magnetic resonance elastography (MRE) for estimating severe LF (F3-4) in preoperative patients. This retrospective study included patients who underwent MRE, gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI, and dynamic CT before liver resection. Liver stiffness measurement (LSM) using MRE, liver-to-spleen signal intensity ratio (LSR) using Gd-EOB-DTPA-enhanced MRI, and spleen volume normalized to body surface area (SV/BSA) using CT volumetry were measured. Laboratory parameters, including levels of type IV collagen 7S and hyaluronic acid, were also measured. Logistic regression and receiver operating characteristic analyses were performed to identify parameters that could estimate severe LF more accurately than LSM alone. A total of 81 patients (mean age, 67 years ± 9.9 [SD]; 58 men) were enrolled. Multivariable logistic regression analysis indicated that LSR (odds ratio [OR]: 0.14, 95% confidence interval [CI]: 0.05-0.37, p < 0.001), SV/BSA (OR: 1.25, 95% CI: 1.02-1.52, p = 0.03) and type IV collagen 7S (OR: 1.84, 95% CI: 1.12-3.00, p = 0.02) were associated with severe LF. Receiver operating characteristic analysis showed that for estimating severe LF, the area under the curve was significantly larger for the combination of LSR, SV/BSA, and type IV collagen 7S than for LSM alone (0.95 vs 0.85, p = 0.04). The combined evaluation of LSR, SV/BSA, and type IV collagen 7S obtained by clinically common preoperative examinations was more accurate than MRE alone for estimating severe LF in preoperative patients. Question What indicators among the imaging and blood tests commonly performed preoperatively can provide a more accurate estimate of severe LF compared to MRE? Findings The combination of LSR, SV/BSA, and type IV collagen 7S was more accurate thanan LSM alone for estimating severe LF. Clinical relevance A combination of commonly performed non-invasive preoperative tests provides a more accurate estimation of severe LF than MR elastography, an examination with relatively limited.

Cloud-Based Service for Fingerprint Image Matching

Fingerprint matching is one of the most important services that related to the issue of identifying individuals, as it can be used to verify the validity of contracts, documents or in other security fields such as criminal investigations. Fingerprints are also one of the most common and accurate forms, as they are easy to obtain, it also has a unique features and cannot be identical to any individuals. The proposed service exploits the advantages provided by cloud computing, such as ease of use, availability of the service anytime and anywhere, and no need to own expensive devices with high specifications, in addition to being low cost. In this service, all fingerprint image preprocessing operations and images enhancement, in addition to the matching, verification and decision-making process are done with the help of tools which provided by cloud computing environment. While the client's role in this service is limited to feeding the system with the fingerprint to be matched. The experimental results proved that this service has achieved high accuracy in matching fingerprint images, in addition to greatly facilitating the matching process for the user, and it can be used anytime and anywhere thanks to the cloud environment facilities adopted by this service.

Evaluating the feasibility and predictive accuracy of biodynamic imaging to platinum-based chemotherapy response in esophageal adenocarcinoma.

Esophageal cancer management lacks reliable response predictors to chemotherapy. In this study we evaluated the feasibility and accuracy of Biodynamic Imaging (BDI), a technology that employs digital holography as a rapid predictor of chemotherapy sensitivity in locoregional esophageal adenocarcinoma. Pre-treatment endoscopic pinch biopsies were collected from patients with esophageal adenocarcinoma during standard staging procedures. BDI analyzed the tumor samples and assessed in vitro chemotherapy sensitivity. BDI sensitivity predictions were compared to patients' pathological responses, the gold standard for determining clinical response, in the surgically treated subset (n=18). BDI was feasible with timely tissue acquisition, collection, and processing in all 30 enrolled patients and successful BDI analysis in 28/29 (96%) eligible. BDI accurately predicted chemotherapy response in 13/18 (72.2%) patients using a classifier for complete, marked, and partial/no-response. BDI technology had 100% negative predictive value for complete pathological response hence identifying patients unlikely to respond to treatment. BDI technology can potentially predict patients' response to platinum chemotherapy. Additionally, this technology represents a promising step towards optimizing treatment strategies for esophageal adenocarcinoma patients by pre-emptively identifying non-responders to conventional platinum-based chemotherapy.

Design of coal mine drilling detection model combining improved YOLOv5 and Gaussian filtering

Coal is currently the most important energy source in most countries. With the advent of information intelligence, more and more intelligent technologies are being applied in coal mine detection. A new model for coal mine drilling detection, which combines improved YOLOv5 and Gaussian filtering, is proposed to address the low efficiency and poor accuracy in manual detection of coal mine drilling. This new model incorporates attention mechanism and multi-object detection model on the basis of traditional YOLOv5. Due to factors such as equipment vibration and electrical interference in drilling detection, random noise is often mixed into the image signal data obtained. In order to effectively reduce the impact of noise on data and improve signal-to-noise ratio, Gaussian filtering method is studied for data denoising. This new model’s border regression loss value was 0.004 lower than the YOLOv5 loss value. This new optimization method’s accuracy was improved from 0.966 to 0.982. This new model improved the detection accuracy of small cracks by about 0.05. The detection depth of the coal seam in this new model was 9.54 m, which was closer to the true value than other methods. Therefore, using the new model to detect coal mine boreholes can effectively improve the accuracy of borehole detection images, which has a good effect on the analysis of coal mine rock layers. This new model has a good guiding role in the detection images and rock analysis research of future coal mine boreholes. The research has good research value in oil drilling inspection, natural gas pipeline monitoring, and quality inspection of industrial automation systems. This provides important technical support for future coal mine drilling image detection and rock analysis research.

Numerical investigation of surface and volume thermal imaging of nickel-graphene foam sheets by finite element method

The detailed theoretical analysis of heat equations is applied to Nickel foam specimens with various pore-size coated graphene thin film of 200 nm thickness under laser excitation. 2D and 3D thermal numerical models were proposed by applying the finite element method. To explain the proposed numerical model, the influence of discretization parameters and the heat diffusion length were studied. The proposed model accurately predicts the thermal spreading in the specimens. The numerical model showed the sensitivity of the model for the structure variation to achieve an accurate thermal imaging when the used laser is of radius value is within of specimen pore size and modulated at low frequency.

A Biometric Features-Based Watermarking Encryption Approach for Secure Authentication of Visual Images

Due to the uniqueness of biometric features, it remains practical in generating cryptographic keys. In this study, a region-based encryption method is proposed, which can avoid the attacks on the whole encryption such as block attack and frame insertion attack. This method can protect important areas while encrypting images so that the image can maintain recognition and credibility even when attacked. It has certain universality and scalability, and can be applied to a variety of scenarios and systems. First, biometric features are introduced to extract keys from fingerprint key images through grayscale segmentation and gradient segmentation. Then, a watermarking addition algorithm is completed based on single encryption and reversible exchange encryption using hybrid vectors, and the image is encrypted using encryption algorithms. Next, the generated biometric key is embedded into the encrypted image, and a complete watermark encryption model named BKE is established by adjusting the singular value decomposition algorithm. Finally, invisibility, decoding accuracy, and stability analysis are completed based on publicly available data. The experimental results show that this method has high effectiveness and feasibility in image security authentication, and this method improves the security and authentication accuracy of images. This method has broad application prospects and can be used for image security authentication in various fields.

Knowledge graph construction in hyperbolic space for automatic image annotation

Automatic image annotation (AIA) is a fundamental and challenging task in computer vision. Considering the correlations between tags can lead to more accurate image understanding, benefiting various applications, including image retrieval and visual search. While many attempts have been made to incorporate tag correlations in annotation models, the method of constructing a knowledge graph based on external knowledge sources and hyperbolic space has not been explored. In this paper, we create an attributed knowledge graph based on vocabulary, integrate external knowledge sources such as WordNet, and utilize hyperbolic word embeddings for the tag representations. These embeddings provide a sophisticated tag representation that captures hierarchical and complex correlations more effectively, enhancing the image annotation results. In addition, leveraging external knowledge sources enhances contextuality and significantly enriches existing AIA datasets. We exploit two deep learning-based models, the Relational Graph Convolutional Network (R-GCN) and the Vision Transformer (ViT), to extract the input features. We apply two R-GCN operations to obtain word descriptors and fuse them with the extracted visual features. We evaluate the proposed approach using three public benchmark datasets. Our experimental results demonstrate that the proposed architecture achieves state-of-the-art performance across most metrics on Corel5k, ESP Game, and IAPRTC-12.

Concordance and Discordance Between Radiology Residents and Consultant Radiologist Interpretation Of CT Brain

OBJECTIVES The primary objective of this study is to assess the degree of concordance and discordance between the interpretations of computed tomography (CT) brain images by resident and consultant radiologists while emphasizing the critical significance of accurate image interpretation for informed clinical decision-making. METHODOLOGY The evaluation of radiology reports for CT Brain interpretation through a prospective analysis at the Radiology Department of Rehman Medical Institute over two years, from 1st October 2020 to 31st October 2022. A total of 198 patients who underwent cranial CT scans were interpreted by residents (R1, R2, R3, R4). Following this, the consultant radiologists reviewed the images and completed their reports. The reports of the residents and the consultant radiologists were then compared, and concordance was achieved when the residents’ reports were consistent with the final radiologist’s reports. The data collected were recorded in Microsoft Excel. The statistical analysis was performed using SPSS version 22 (IBM Corp., Armonk, NY), and the kappa coefficient was used to determine the level of agreement between residents and consultants.RESULTSAmong the 198 CT Head reports evaluated, 186 of them were in agreement with the final report of the consultant radiologist. Of the correctly diagnosed cases, R1 correctly diagnosed 46 cases, R2 correctly diagnosed 80 cases, R3 correctly diagnosed 54 cases, and R4 correctly diagnosed 6 cases. Our study achieved a percentage agreement of 93.93, with a Cohen's kappa coefficient of 0.8.CONCLUSIONThe overall concordance rate between residents and consultant radiologists was 93.93%, with a kappa coefficient 0.8. This high kappa coefficient indicates strong agreement between the two groups.

Mathematical Models for Ultrasound Elastography: Recent Advances to Improve Accuracy and Clinical Utility.

Changes in biomechanical properties such as elasticity modulus, viscosity, and poroelastic features are linked to the health status of biological tissues. Ultrasound elastography is a non-invasive imaging tool that quantitatively maps these biomechanical characteristics for diagnostic and treatment monitoring purposes. Mathematical models are essential in ultrasound elastography as they convert the raw data obtained from tissue displacement caused by ultrasound waves into the images observed by clinicians. This article reviews the available mathematical frameworks of continuum mechanics for extracting the biomechanical characteristics of biological tissues in ultrasound elastography. Continuum-mechanics-based approaches such as classical viscoelasticity, elasticity, and poroelasticity models, as well as nonlocal continuum-based models, are described. The accuracy of ultrasound elastography can be increased with the recent advancements in continuum modelling techniques including hyperelasticity, biphasic theory, nonlocal viscoelasticity, inversion-based elasticity, and incorporating scale effects. However, the time taken to convert the data into clinical images increases with more complex models, and this is a major challenge for expanding the clinical utility of ultrasound elastography. As we strive to provide the most accurate imaging for patients, further research is needed to refine mathematical models for incorporation into the clinical workflow.

Accuracy, repeatability, and reproducibility of water-fat magnetic resonance imaging in a phantom and healthy volunteer

Bone marrow (BM) damage due to chemoradiotherapy can increase BM fat in cervical cancer patients. Water-fat magnetic resonance (MR) scans were performed on a phantom and a healthy female volunteer to validate proton density fat fraction accuracy, reproducibility, and repeatability across different vendors, field strengths, and protocols. Phantom measurements showed a high accuracy, high repeatability, and excellent reproducibility. Volunteer measurements had an excellent intra- and interreader reliability, good repeatability, and moderate to good reproducibility. Water-fat MRI show potential for quantification of longitudinal vertebral BM fat changes. Further studies are needed to validate and extend these findings for broader clinical applicability.

Semantic Guided prototype learning for Cross-Domain Few-Shot hyperspectral image classification

Hyperspectral image(HSI) classification has found extensive application in the field of remote sensing. However, traditional HSI classification methods typically rely on a large number of labeled samples, incurring significant costs and requiring considerable time and effort for acquisition. To address this issue, this paper proposes a novel cross-domain few-shot learning method termed Semantic Guided Prototype Learning (SGPL), which aims to optimize prototype learning through the incorporation semantic information, thereby enhancing the classification accuracy of hyperspectral images in few-shot scenarios. The core idea of the SGPL method is to integrate semantic information into the prototype learning framework, thereby enhancing the expressive capacity of each category prototype. Specifically, HSI category labels are encoded via one-hot encoding, and the corresponding HSI features are concatenated with these encoded vectors to construct an augmented feature representation. These augmented feature vectors are subsequently fed into a prototype generator, which is designed to learn the generation of category prototypes from the concatenated features. Through the application of one-hot encoding and feature concatenation, SGPL effectively captures and leverages discriminative information between classes. Moreover, the proposed model exhibits superior performance on three different HSI datasets: Salinas, the University of Pavia(UP), and Indian Pines(IP) with overall classification accuracies of 91.27%, 85.84%, and 69.33%, respectively, outperforming other methods. The source code is available at https://github.com/AIYAU/SGPL.

Multimodal ultrasound imaging for patent foramen ovale and pulmonary arteriovenous malformation in patients with cryptogenic stroke or migraine: A prospective diagnostic study.

Right-to-left shunt (RLS) caused by patent foramen ovale (PFO) and pulmonary arteriovenous malformations (PAVM) have been associated with a variety of diseases, and reliable techniques for detecting RLS are essential for diagnosis. This study aimed to compare the diagnostic accuracy of multimodal ultrasound imaging, including transthoracic echocardiography (TTE) plus contrast transthoracic echocardiography (CTTE) and transesophageal echocardiography (TEE) plus contrast transesophageal echocardiography (CTEE) for PFO and PAVM in patients with cryptogenic stroke or migraine. This prospective study enrolled patients with cryptogenic stroke or migraine admitted to First Hospital of Shanxi Medical University between July 2018 and April 2023. The TTE + CTTE + TEE + CTEE multimodal ultrasound imaging was defined as the gold standard. A total of 230 patients with cryptogenic stroke (108) or migraine (122) were enrolled. The TEE + CTEE generated a better area under the receiver operator characteristic (ROC) curves (AUC) than TTE + CTTE [0.995 (0.988-1.000) vs 0.975 (0.767-0.984), P < .001], indicating better identification of PFO and PAVM. The sensitivity and specificity of the TTE + CTTE were 89.4% and 85.7%, respectively, whereas the sensitivity and specificity of TEE + CTEE were 99.1% and 100%, respectively. The missed diagnosis rate of TTE + CTTE and TEE + CTEE was 65.7 % and 12.5%, respectively. The combination of TEE + CTEE may be a more reliable and sensitive tool to detect PFO and PAVM than TTE + CTTE in patients with cryptogenic stroke or migraine.