Internal Features Research Articles

Method of bed exit intention based on the internal pressure features in array air spring mattress

With the population ages, many patients are unable to receive comprehensive care, leading to an increase in hazardous incidents, particularly falls occurring after getting out of bed. To address this issue, this paper proposes a method for recognizing bed-exit intentions using an array air spring mattress. The method integrates convolutional neural networks with feature point matching techniques to identify both global and local features of the array air spring. For global features, a one-dimensional focal loss convolutional neural network (1D-FLCNN) model is employed to classify eight internal pressure time series and determine bed-exit status based on global features. For local features, the distribution matrix and feature point matrix of the internal pressure features are extracted to represent the spatial distribution of bed-exit postures. Euclidean distance is utilized to measure the similarity between these matrices and match bed-exit postures. Finally, the recognition results from both feature types are combined using a logical OR operation to produce the final result. Experimental validation confirms that the proposed method greatly improves the anti-interference capability and effectively avoids the problem of non-recognition due to body position and external environment.

CT image segmentation of foxtail millet seeds based on semantic segmentation model VGG16-UNet

Foxtail millet is an important minor cereal crop rich in nutrients. Due to the small size of its seeds, there is little information on the diversity of its seed structure among germplasms, limiting the identification of genes controlling seed development and germination. This paper utilized X-ray computed tomography (CT) scanning technology and deep learning models to reveal the microstructure of foxtail millet seeds, gaining insights into their internal features, distribution, and composition. A total of 100 foxtail millet varieties were scanned with X-ray computed tomography to obtain 3D reconstruction images and slices. Pre-processing steps were adopted to improve image segmentation accuracy, including noise reduction, rotation, contrast enhancement, and brightness enhancement. The experiment revealed that traditional OpenCV image processing methods failed to achieve precise segmentation, whereas deep learning models exhibited outstanding performance in segmenting seed CT slice images. We compared UNet, PSPNet, and DeepLabV3 models, selected different backbones and optimizers based on the dataset, and continuously adjusted learning rates and maximum training epochs to train the models. Results demonstrated that VGG16-UNet achieved an accuracy of 99.19% on the foxtail millet seed CT slice image dataset, outperforming PSPNet and DeepLabV3 models. Compared to ResNet-UNet, VGG16-UNet shows an improvement of approximately 3.18% in accuracy, demonstrating superior performance in accurately segmenting the inner glume, outer glume, embryo, and endosperm under various adhesion conditions. Accurate segmentation of foxtail millet CT images enables analysis of embryo size, endosperm size, and glume thickness, which impact germination, growth, and nutrition. This study fills a gap in small grain structure research, offering insights to optimize agriculture and molecular breeding for improved yield and quality.

Evaluation of Image Segmentation Methods for In Situ Quality Assessment in Additive Manufacturing

Additive manufacturing (AM), or 3D printing, has revolutionized the fabrication of complex parts, but assessing their quality remains a challenge. Quality assessment, especially for the interior part geometry, relies on post-print inspection techniques unsuitable for real-time in situ analysis. Vision-based approaches could be employed to capture images of any layer during fabrication, and then segmentation methods could be used to identify in-layer features in order to establish dimensional conformity and detect defects for in situ evaluation of the overall part quality. This research evaluated five image segmentation methods (simple thresholding, adaptive thresholding, Sobel edge detector, Canny edge detector, and watershed transform) on the same platform for their effectiveness in isolating and identifying features in 3D-printed layers under different contrast conditions for in situ quality assessment. The performance metrics used are accuracy, precision, recall, and the Jaccard index. The experimental set-up is based on an open-frame fused filament fabrication printer augmented with a vision system. The control system software for printing and imaging (acquisition and processing) was custom developed in Python running on a Raspberry Pi. Most of the segmentation methods reliably segmented the external geometry and high-contrast internal features. The simple thresholding, Canny edge detector, and watershed transform methods did not perform well with low-contrast parts and could not reliably segment internal features when the previous layer was visible. The adaptive thresholding and Sobel edge detector methods segmented high- and low-contrast features. However, the segmentation outputs were heavily affected by textural and image noise. The research identified factors affecting the performance and limitations of these segmentation methods and contributing to the broader effort of improving in situ quality assessment in AM, such as automatic dimensional analysis of internal and external features and the overall geometry.

Amethyst from Newfoundland, Canada: Geology, Internal Features, and Fluid Inclusion Microthermometry

Amethyst from Newfoundland, Canada: Geology, Internal Features, and Fluid Inclusion Microthermometry

Electrochemical jet machining in deep-small holes with gas assistance: generating complex features on internal surfaces

Deep-small holes with internal features have important applications in thermal engineering but pose significant difficulties for traditional machining methods. Electrochemical jet machining (EJM) is an effective surface micromachining technique with numerous merits. Applying EJM to create complicated features on the internal surface of deep-small holes is attractive. However, this concept remains challenging due to the narrow and enclosed processing space. In this work, a novel gas assistance tool is developed to achieve the EJM process in deep-small holes for the first time. The hydrodynamic conditions to realize well-shaped and unsubmerged jets in both open space and deep-small holes using the specifically designed tool are investigated. The appropriate electrolyte flow rate and sidewall orifice dimensions enable the desired jet to be ejected laterally from the tubular cathode sidewall orifice. While in the deep-small hole the assist gas creates a local gas cavity around the orifice to prevent the jet from submerging, forming the jet shape required for EJM and consequently achieving localized machining of the internal surface. Excessive assist gas pressure should be avoided as it causes the jet to incline and deform, resulting in reduced machining accuracy. Furthermore, the influence of the main parameters on machining performance is examined. The developed gas-assisted EJM method demonstrates similar machining characteristics to the conventional EJM process when appropriate gas assistance conditions can produce the well-shaped unsubmerged jet. As such, various features with smooth surfaces and good shape accuracy are successfully machined on the internal surface of deep-small holes.

An analysis of pre-trained stable diffusion models through a semantic lens

Recently, generative models for images have garnered remarkable attention, due to their effective generalization ability and their capability to generate highly detailed and realistic content. Indeed, the success of generative networks (e.g., BigGAN, StyleGAN, Diffusion Models) has driven researchers to develop increasingly powerful models. As a result, we have observed an unprecedented improvement in terms of both image resolution and realism, making generated images indistinguishable from real ones. In this work, we focus on a family of generative models known as Stable Diffusion Models (SDMs), which have recently emerged due to their ability to generate images in a multimodal setup (i.e., from a textual prompt) and have outperformed adversarial networks by learning to reverse a diffusion process. Given the complexity of these models that makes it hard to retrain them, researchers started to exploit pre-trained SDMs to perform downstream tasks (e.g., classification and segmentation), where semantics plays a fundamental role. In this context, understanding how well the model preserves semantic information may be crucial to improve its performance.This paper presents an approach aimed at providing insights into the properties of a pre-trained SDM through the semantic lens. In particular, we analyze the features extracted by the U-Net within a SDM to explore whether and how the semantic information of an image is preserved in its internal representation. For this purpose, different distance measures are compared, and an ablation study is performed to select the layer (or combination of layers) of the U-Net that best preserves the semantic information. We also seek to understand whether semantics are preserved when the image undergoes simple transformations (e.g., rotation, flip, scale, padding, crop, and shift) and for a different number of diffusion denoising steps. To evaluate these properties, we consider popular benchmarks for semantic segmentation tasks (e.g., COCO, and Pascal-VOC). Our experiments suggest that the first encoder layer at 16×16 resolution effectively preserves semantic information. However, increasing inference steps (even for a minimal amount of noise) and applying various image transformations can affect the diffusion U-Net’s internal feature representation. Additionally, we propose some examples taken from a video benchmark (DAVIS dataset), where we investigate if an object instance within a video preserves its internal representation even after several frames. Our findings suggest that the internal object representation remains consistent across multiple frames in a video, as long as the configuration changes are not excessive.

Lava flow field development and lava tube formation during the 1858–1861 eruption of Vesuvius (Italy), unravelled by historical documentation, lidar data and 3D mapping

Somma-Vesuvius is well known for its powerful Plinian explosive eruptions, however during the last eruptive cycle (1631–1944), persistent activity took place on the stratovolcano as mild and violent Strombolian, and effusive eruptions, forming more than one hundred lava flow fields. An important mechanism of lava transport within lava flow fields is the formation and development of lava tubes. The presence of lava tubes in a flow field can greatly increase their distance of emplacement. Observations of lava tubes at Vesuvius have been documented in historical records and speleological reports but no modern scientific studies are available. This work focuses on lava tubes formed in the compound lava flow field of the long-lived 1858 eruption (from 27 May 1858 to 12 April 1861) that was fed by seven eruptive fissures. The temporal and spatial evolution of the 1858 lava flow field was reconstructed using historical documentation. The exposed lava flow field surface was analysed using a 1-m resolution lidar Digital Surface Model (DSM). Surveys to fully digitize the interior and the overlying surface of the largest lava tube found in the 1858 lava flow field were conducted using a terrestrial laser scanner, optical cameras, and an Unmanned Aerial Vehicle (UAV). The accurate 3D model obtained was used to precisely quantify the inner dimensions and to better constrain the morphologies of the lava tube. Observed internal features were described and used to gain information on the formation and activity of the lava tube. Our data allowed us to understand that the described lava tube formed as an inflated lava flow inside which lava flowed through during an extended period ultimately draining out completely at the end of the eruption. Understanding how lava flow fields develop and how lava tubes form on Vesuvius is crucial to re-evaluate the last effusive activity of the volcano and its impact on hazard assessment.

ULTRASONIC INTERROGATION TECHNIQUE FOR DETECTING HATCH ANGLE VARIATIONS IN ADDITIVE MANUFACTURING BY PHONONIC LATTICE STRUCTURES

Abstract Additive Manufacturing/3D Printing (AM/3DP) has revolutionized part production by enabling the creation of intricate internal structures and complex geometries from diverse materials directly from digital design files. Among powder-based metal AM/3DP methods, Selective Laser Melting (SLM) is widely used in advanced applications such as biomedical devices and aerospace parts. Despite considerable progress in AM/3DP and SLM, at present, challenges in print quality persist, and vast resources for post-production quality assessment are allocated. The quality of SLM prints is influenced by various process and design parameters, such as the accuracy of hatch angle deposition, laser intensity/power, scanning speed of the laser beam, print line spacing, layer depth, printing chamber conditions, and the material's physical and chemical properties. Direct ultrasonic Non-Destructive Evaluation (NDE) offers comprehensive internal inspection and real-time data acquisition ability; however, in AM/3DP it faces severe limitations due to a build's intricate internal and external geometric features. In the current study, we present a Phononic Crystal Artifact (PCA)-based real-time ultrasonic NDE quality monitoring framework and show offline its utility in detecting and evaluating hatch angle variations, a critical process parameter. A PCA is substantially simpler and smaller than the actual build but represents its critical geometric and structural intricacies and mechanical properties. The current offline study demonstrates that hatch angle variations can be monitored from ultrasonic responses' spectral modal frequency peaks and wave dispersion relations.

Salt dome identification using incremental semi-supervised learning and unsupervised learning-based label generation

Salt domes represent distinctive geological anomalies in seismic data, crucial for pinpointing hydrocarbon reservoirs and strategizing drilling paths. Conventional seismic attributes or computer vision methods usually fail to capture the intricate details of salt domes, resulting in interpretation results marred by noise. While deep learning presents a promising approach for intelligent 3D salt dome interpretation, its effectiveness is heavily dependent on the availability of labeled samples. To facilitate accurate interpretation, we propose an innovative workflow that integrates an unsupervised label generation component with an incremental semi-supervised learning framework utilizing the U-Net architecture. To generate salt dome labels, we prioritize both the root mean square (RMS) amplitude attribute and variance attribute (VA) as foundational data. Utilizing convolutional autoencoders (CAE), we establish a relationship between the input RMS attribute and the output reconstructed attribute. The intermediate features extracted by CAE are transformed into the salt boundary feature via principal component analysis and K-Means clustering. Concurrently, we employ K-Means clustering on VA to ascertain the salt internal feature. We further propose a feature aggregation method to consolidate the salt boundary feature and the salt internal feature for label generation of the salt dome. For 3D salt dome interpretation, we begin by predicting adjacent test datasets using labels generated by the unsupervised salt dome label generation module. The prediction results of these test datasets are then integrated into the training datasets to enhance the interpretation performance of U-Net, steering it towards an incremental semi-supervised learning method for salt dome interpretation. Additionally, we extend this research by applying transfer learning techniques for identifying mound-shoals using the same semi-supervised model parameters initially developed for interpreting salt domes. This method is validated using datasets from the Netherlands F3 block for salt domes and the North China block for mound-shoals. The results demonstrate that this innovative process only requires a minimal number of labels from unsupervised methods to precisely interpret salt domes across 3D seismic data. Furthermore, the low-level features of salt domes learned from neural network can be seamlessly transferred to mound-shoal identification. This automated approach significantly streamlines the interpretation process, reducing the time and resources traditionally necessary for reservoir analysis.

Engineering Stimuli-Responsive Materials for Precision Medicine.

Over the past decade, precision medicine has garnered increasing attention, making significant strides in discovering new therapeutic drugs and mechanisms, resulting in notable achievements in symptom alleviation, pain reduction, and extended survival rates. However, the limited target specificity of primary drugs and inter-individual differences have often necessitated high-dosage strategies, leading to challenges such as restricted deep tissue penetration rates and systemic side effects. Material science advancements present a promising avenue for these issues. By leveraging the distinct internal features of diseased regions and the application of specific external stimuli, responsive materials can be tailored to achieve targeted delivery, controllable release, and specific biochemical reactions. This review aims to highlight the latest advancements in stimuli-responsive materials and their potential in precision medicine. Initially, we introduce disease-related internal stimuli and capable external stimuli, elucidating the reaction principles of responsive functional groups. Subsequently, we provide a detailed analysis of representative pre-clinical achievements of stimuli responsive materials across various clinical applications, including enhancements in the treatment of cancers, injury diseases, inflammatory diseases, infection diseases, and high-throughput microfluidic biosensors. Finally, we discuss some clinical challenges, such as off-target effects, long-term impacts of nano-materials, potential ethical concerns, and offer insights into future perspectives of stimuli-responsive materials.

Constructing High-Performance and Versatile Liquid-Solid Triboelectric Nanogenerator with Inflatable Columnar Units

Abstract The use of water resources for energy generation has become increasingly prevalent, encompassing the conversion of kinetic energy from streams, tides, and waves into renewable electrical power. Water energy sources offer numerous benefits, including widespread availability, stability, and the absence of carbon dioxide and other greenhouse gas emissions, making them a clean and environmentally friendly form of energy. In this work, we develop a droplet-based liquid-solid triboelectric nanogenerator (LS-TENG) using sophisticatedly designed inflatable columnar structures with inner and outer dual-electrode. This device can be utilized to harvest both the internal droplet-rolling mechanical energy and the external droplet-falling mechanical energy, capable of assembling into various structures for versatile applications. The design incorporates a combined structure of both internal and external TENG to optimize output performance via multiple energy harvesting strategies. The internal structure features a dual-electrode columnar-shaped LS-TENG, designed to harvest fluid kinetic energy from water droplet flowing. By leveraging the back-and-forth motion of a small amount of water within the air column, mechanical energy can be collected, achieving a maximum mass power density of 9.02 W/Kg and an energy conversion efficiency of 10.358%. The external component is a droplet-based LS-TENG, which utilizes a double-layer capacitor switch effect elucidated with an equivalent circuit model. Remarkably, without the need for pre-charging, a single droplet can generate over 140 V of high voltage, achieving a maximum power density of 7.35 W·m² and an energy conversion efficiency of 22.058%. The combined LS-TENG with sophisticated inflatable columnar structure can simultaneously collect multiple types of energy in high efficacy, exhibiting great significance in potential applications such as TENG aeration rings, inflatable lifejacket, wind energy harvesting, gas-column TENG tents, and green houses.

Features are more than just filling in the blanks on body size scales

ABSTRACT Despite widespread misperceptions of body size and shape, the specific visual features influencing such judgments remain unclear. This study assessed the contribution of internal body features (e.g., colour, shading) to the accuracy of female body size judgements and whether that contribution varies with body size. Using a bodyline task, 100 female participants evaluated images of female bodies, either depicted as real bodies including internal features (feature stimuli) or images of bodies as silhouettes only (silhouette stimuli). The results showed greater overall errors in body size estimation for silhouettes compared to full feature images. However, the direction of error differed for large and small bodies: small silhouettes were judged larger than their full-feature counterparts, while large silhouettes were judged smaller. These findings emphasize the importance of internal body features in shaping perceptions of female body size and highlight the complexity of judgement accuracy across different body sizes.

Use of Computerised X-ray Tomography in the Study of the Fabrication Methods and Conservation of Ceramics, Glass and Stone Building Materials

This work will review and discuss the use of computerised X-ray tomography (CXT) for analysing ancient, manufactured items, like stone building materials, glass and ceramics. It will consider particular techniques required, and/or of benefit, for CXT of heritage materials, such as special precautions during the experimentation to ensure there is no damage to the materials, special imaging methods such as elemental-specific imaging, and sample-specific image analysis requirements. This study shows how the knowledge of internal features, particularly pores, discerned from CXT can be used to reverse engineer the artefact fabrication process. CXT can be used to obtain information on both the raw materials (such as types and impurities) and fabrication techniques used. These abilities can then be used to establish technological evolution and the incidence of ancient behaviours like recycling and allow the linking of particular items to specific production sites. It will also be seen how CXT can aid the development of effective conservation techniques. This work will also consider how conclusions drawn from CXT data can be amended or augmented by the use of complementary non-destructive characterisation methods, such as gas overcondensation.

Nondestructive and quick differentiation between loupe-clean low-alkali natural and hydrothermal synthetic aquamarines: Constraints of infrared spectroscopy

A considerable increase in the price of natural aquamarine has led to the emergence of hydrothermal synthetic aquamarine as a cheap substitute in the market. In this study, comparative analysis was conducted between natural aquamarine obtained from Altay Prefecture in Xinjiang, China, and hydrothermal synthetic aquamarine in the Chinese market to distinguish them with loupe-clean clarity. Nondestructive tests including microscopic observation, X-ray fluorescence spectroscopy, infrared spectroscopy, ultraviolet–visible–near-infrared spectroscopy, and Raman spectroscopy were conducted. The two samples were low-alkali aquamarine, dominated by type Ⅰ H2O, and the infrared spectra showed similar water-related peaks, exhibiting weak absorption bands related to Cl− at 2736 and 2449 cm−1 and CO2 in the crystal channels at 2358 cm−1. Apart from the strong irregular and distinctive growth patterns, infrared absorption bands at 3313 and 4060 cm−1 due to the N–H bond (derived from ammonium halide used in crystal growth) vibrations were found to be present only in hydrothermal synthetic aquamarine. This study reveals implications for identifying hydrothermal synthetic aquamarine when the internal features of loupe-clean aquamarine are not obvious.

Study on hybrid 3D printing and milling process for customized polyether-ether-ketone components.

Polyether-ether-ketone (PEEK) has been widely applied in various fields due to its excellent mechanical properties and biocompatibility. The efficient and high-quality customized manufacturing of PEEK components are investigated in this study by the hybrid 3D printing and milling process. At first, the alternating hybrid process is selected and verified by comparing two typical hybrid process categories and conducting experiments, respectively. Second, a set of procedures are designed to automate the engineering application of the hybrid process trying to avoid the disadvantages of manual programing. Then, considering the tool length and possible interferences during the hybrid process, a model segmentation algorithm, namely, the exchange principle of avoiding interference (EPAI) is proposed. Based on the introduced EPAI and the programing language Python, the additive and subtractive hybrid manufacturing (ASHM) data processing procedure is proposed and realized by post-processing of the conventional 3D printing codes. Finally, the feasibility experiments have been conducted. The experimental results verify the hybrid manufacturing process in the fabrication of parts with complex internal features. The surface roughness Ra and dimensional error L of the parts have been reduced by 75.5% and 85.2%, respectively, while the shear strength τ has been increased by 14.1%. Compared with conventional milling process, the material consumption is reduced by 48.7%.

The Challenges of a Purchase by the Berlin Papyrus Collection in 1926

Abstract Where do Arabic papyri come from? The article shifts the question to “How did the Arabic papyri reach modern institutions, and why do we know so little about their provenance”? In an exemplary way, the purchase of 142 texts from Maurice Nahman in Cairo by the Berlin Papyrus collection in 1926, which is exceptionally well documented, will be discussed here. The collection was then headed by Wilhelm Schubart, who was assisted in this purchase by Adolf Grohmann and Ludwig Borchardt. It turns out that several of the steps from the findspot to the museum were settled on the margin of legal norms, which meant that the actors had an interest to disguise business partners and archeological contexts. For this reason, most acquisitions cannot be traced back to the original findspot. If we intend to reconstruct groups of related documents, it will probably be easier to analyze internal features, like the mentioned persons and the style of handwriting. Museum archives can then complement this information.

MRI-Seed-Wizard: Combining Deep Learning Algorithms with Magnetic Resonance Imaging Enables Advanced Seed Phenotyping.

Evaluation of relevant seed traits is an essential part of most plant breeding and biotechnology programs. There is need for non-destructive, three-dimensional assessment of the morphometry, composition, and internal features of seeds. Here, we introduced a novel tool, MRI-Seed-Wizard, which integrates deep learning algorithms with non-invasive magnetic resonance imaging (MRI) for its use in the new domain - plant MRI. The tool enabled in vivo quantification of 23 grain traits, including volumetric parameters of inner seed structure. Several of these features cannot be assessed using conventional techniques, including X-ray computed tomography. MRI-Seed-Wizard was designed to automate the manual processes of identifying, labeling, and analyzing digital MRI data. We further provide advanced MRI protocols that allow the evaluation of multiple seeds simultaneously to increase throughput. The versatility of MRI-Seed-Wizard in seed phenotyping was demonstrated for wheat (Triticum aestivum) and barley (Hordeum vulgare) grains, and is applicable to a wide range of crop seeds. Thus, artificial intelligence, combined with the most versatile imaging modality - MRI, opens up new perspectives in seed phenotyping and crop improvement.

A flexible deep learning framework for liver tumor diagnosis using variable multi-phase contrast-enhanced CT scans

BackgroundLiver cancer is a significant cause of cancer-related mortality worldwide and requires tailored treatment strategies for different types. However, preoperative accurate diagnosis of the type presents a challenge. This study aims to develop an automatic diagnostic model based on multi-phase contrast-enhanced CT (CECT) images to distinguish between hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and normal individuals.MethodsWe designed a Hierarchical Long Short-Term Memory (H-LSTM) model, whose core components consist of a shared image feature extractor across phases, an internal LSTM for each phase, and an external LSTM across phases. The internal LSTM aggregates features from different layers of 2D CECT images, while the external LSTM aggregates features across different phases. H-LSTM can handle incomplete phases and varying numbers of CECT image layers, making it suitable for real-world decision support scenarios. Additionally, we applied phase augmentation techniques to process multi-phase CECT images, improving the model’s robustness.ResultsThe H-LSTM model achieved an overall average AUROC of 0.93 (0.90, 1.00) on the test dataset, with AUROC for HCC classification reaching 0.97 (0.93, 1.00) and for ICC classification reaching 0.90 (0.78, 1.00). Comprehensive validation in scenarios with incomplete phases was performed, with the H-LSTM model consistently achieving AUROC values over 0.9.ConclusionThe proposed H-LSTM model can be employed for classification tasks involving incomplete phases of CECT images in real-world scenarios, demonstrating high performance. This highlights the potential of AI-assisted systems in achieving accurate diagnosis and treatment of liver cancer. H-LSTM offers an effective solution for processing multi-phase data and provides practical value for clinical diagnostics.

Automatic detection of hidden defects and qualification of additively manufactured parts using X-ray computed tomography and computer vision

Additive manufacturing (AM) has the unique capability to produce parts with complex three-dimensional structures with features that are internal to the part and hidden from view. Such internal features are difficult to inspect and may have defects that affect the function of a part. X-ray computed tomography (CT) is one of the only methods for nondestructive inspection (NDI) of the interior of AM parts. This paper explores how machine learning (ML) methods can be used to analyze CT scans of AM parts to automatically identify the presence of defects in geometric features. We designed a nozzle part with internal three-dimensional (3D) channels and introduced five different synthetic defects whose presence could affect the nozzle performance. A resin-based AM process fabricated 155 nozzle parts that included both defect-free parts and parts with synthetic defects. CT scans were collected for each part and processed into 68,510 image cross sections. The extracted images were used to train an ML model based on the ResNet34 architecture. The model can automatically identify and classify defects in individual CT slice images with over 98% accuracy. High model accuracy is possible with training on as few as 30 parts. The research demonstrates the potential of ML methods to automatically identify hidden defects and qualify AM parts using X-ray CT scans.

Role of ultrasound and color Doppler in diagnosis of ectopic pregnancy and to differentiate from corpus luteum cyst of pregnancy

Background: Ectopic pregnancy is the leading cause of direct maternal deaths. Ectopic pregnancy and corpus luteum cyst are two most common differential diagnoses in a patient with urine pregnancy test (UPT) positive and no sonographic evidence of intrauterine pregnancy. Internal echotexture and color Doppler features of the cyst wall are also considered in an attempt to differentiate the tubal ring of an ectopic pregnancy from that of a corpus luteum. Aims and Objectives: The aim of this study was to diagnose the presence of ectopic pregnancy and to differentiate ectopic pregnancy from corpus luteum cyst using grayscale ultrasound and color Doppler findings. Materials and Methods: The prospective study was conducted in 46 patients who were referred to the Department of Radiodiagnosis in R.L Jalappa Hospital with UPT positive and clinical features suggestive of ectopic pregnancy. Results: Out of 46 patients, 33 were diagnosed with ectopic pregnancy and 13 with corpus luteum cyst. Ectopic pregnancies had thicker walls as compared to corpus luteum cysts. Most of the ectopic pregnancies had hyperechoic walls as compared to ovaries (63.6%) and endometrium (60.6%). Free fluid with echoes was seen in the pelvis in 59.4% ectopic pregnancies whereas most of the corpus luteum cysts (69.2%) had no free fluid. Most of the corpus luteum cysts (61.5%) had clear internal echotexture whereas ectopic pregnancies were mostly lacy or solid. Yolk sac was seen exclusively in ectopic pregnancy (30.3%). RI 0.7 was found to be highly specific for diagnosing ectopic pregnancy. Conclusion: Significant ultrasound parameters that aid in differentiating between the two include the thickness of the mass’s wall, the wall’s echogenicity in relation to the ovary and endometrium, the internal echotexture of the cystic mass, the presence of the yolk sac, and the presence of free fluid with echoes. It was shown that RI 0.7 has a very good specificity in identifying ectopic pregnancy.