Analysis Of Ultrasound Images Research Articles

Research on Region Noise Reduction and Feature Analysis of Total Focus Method Ultrasound Image Based on Branch Pipe Fillet Weld

Research on Region Noise Reduction and Feature Analysis of Total Focus Method Ultrasound Image Based on Branch Pipe Fillet Weld

A Comprehensive and Repeatable Contrast-Enhanced Ultrasound Quantification Approach for Clinical Evaluations of Tumor Blood Flow.

The aim of this study is to define a comprehensive and repeatable contrast-enhanced ultrasound (CEUS) imaging protocol and analysis method to quantitatively assess lesional blood flow. Easily repeatable CEUS evaluations are essential for longitudinal treatment monitoring. The quantification method described here aims to provide a structure for future clinical studies. This retrospective analysis study included liver CEUS studies in 80 patients, 40 of which contained lesions (primarily hepatocellular carcinoma, n = 28). Each patient was given at least 2 injections of a microbubble contrast agent, and 60-second continuous loops were acquired for each injection to enable evaluation of repeatability. For each bolus injection, 1.2 mL of contrast was delivered, whereas continuous, stationary scanning was performed. Automated respiratory gating and motion compensation algorithms dealt with breathing motion. Similar in size regions of interest were drawn around the lesion and liver parenchyma, and time-intensity curves (TICs) with linearized image data were generated. Four bolus transit parameters, rise time (RT), mean transit time (MTT), peak intensity (PI), and area under the curve (AUC), were extracted either directly from the actual TIC data or from a lognormal distribution curve fitted to the TIC. Interinjection repeatability for each parameter was evaluated with coefficient of variation. A 95% confidence interval was calculated for all fitted lognormal distribution curve coefficient of determination (R2) values, which serves as a data quality metric. One-sample t tests were performed between values obtained from injection pairs and between the fitted lognormal distribution curve and direct extraction from the TIC calculation methods to establish there were no significant differences between injections and measurement precision, respectively. Average interinjection coefficient of variation with both the fitted curve and direct calculation of RT and MTT was less than 21%, whereas PI and AUC were less than 40% for lesion and parenchyma regions of interest. The 95% confidence interval for the R2 value of all fitted lognormal curves was [0.95, 0.96]. The 1-sample t test for interinjection value difference showed no significant differences, indicating there was no relationship between the order of the repeated bolus injections and the resulting parameters. The 1-sample t test between the values from the fitted lognormal distribution curve and the direct extraction from the TIC calculation found no statistically significant differences (α = 0.05) for all perfusion-related parameters except lesion and parenchyma PI and lesion MTT. The scanning protocol and analysis method outlined and validated in this study provide easily repeatable quantitative evaluations of lesional blood flow with bolus transit parameters in CEUS data that were not available before. With vital features such as probe stabilization ideally performed with an articulated arm and an automated respiratory gating algorithm, we were able to achieve interinjection repeatability of blood flow parameters that are comparable or surpass levels currently established for clinical 2D CEUS scans. Similar values and interinjection repeatability were achieved between calculations from a fitted curve or directly from the data. This demonstrated not only the strength of the protocol to generate TICs with minimal noise, but also suggests that curve fitting might be avoided for a more standardized approach. Utilizing the imaging protocol and analysis method defined in this study, we aim for this methodology to potentially assist clinicians to assess true perfusion changes for treatment monitoring with CEUS in longitudinal studies.

A multi-institutional study on Ultrasound image analysis for staging HBV-derived liver fibrosis: a potential non-invasive alternative to liver stiffness measurement

Background: Liver stiffness measurement is principal for staging liver fibrosis but not included in routine examinations. We investigated whether comparable diagnostic performance can be achieved by mining ultrasound images and developing a novel serum index (NSI). Methods: Texture features were extracted from ultrasound images. Spearman correlation and logistics regression selected independent variables for significant (F≥2) and advanced (F≥3) fibrosis. We compared the diagnostic performance of transient elastography (TE), ultrasound image biomarker (UIB), conventional serum indices (APRI, FIB-4, GPR), and NSI in 365 chronic hepatitis B patients. Results: Among patients, 52.1% had significant fibrosis and 24.2% had advanced fibrosis. PLT, GGT, prealbumin, and globulin were incorporated into NSI. In the validation group, TE achieved the best performance (AUC: 0.765 [0.690-0.849] for significant fibrosis; 0.812 [0.745-0.878] for advanced fibrosis), followed by UIB (AUC: 0.712 [0.629-0.795]; 0.678 [0.595-0.763]) and NSI (AUC: 0.630 [0.534-0.725]; 0.659 [0.572-0.745]), outperforming conventional indices. Conclusions: Texture analysis enhances ultrasound's diagnostic utility, but TE remains superior. When TE is unavailable, ultrasound image analysis and NSI, incorporating prealbumin, can serve as alternative tools for fibrosis staging.

ANN uncertainty estimates in assessing fatty liver content from ultrasound data

Background and objectiveThis article uses three different probabilistic convolutional architectures applied to ultrasound image analysis for grading Fatty Liver Content (FLC) in Metabolic Dysfunction Associated Steatotic Liver Disease (MASLD) patients. Steatosis is a new silent epidemic and its accurate measurement is an impelling clinical need, not only for hepatologists, but also for experts in metabolic and cardiovascular diseases. This paper aims to provide a robust comparison between different uncertainty quantification strategies to identify advantages and drawbacks in a real clinical setting. MethodsWe used a classical Convolutional Neural Network, a Monte Carlo Dropout, and a Bayesian Convolutional Neural Network with the goal of not only comparing the goodness of the predictions, but also to have access to an evaluation of the uncertainty associated with the outputs. ResultsWe found that even if the prediction based on a single ultrasound view is reliable (relative RMSE [5.93%-12.04%]), networks based on two ultrasound views outperform them (relative RMSE [5.35%-5.87%]). In addition, the results show that the introduction of a “not confident” category contributes to increase the percentage of correctly predicted cases and to decrease the percentage of mispredicted cases, especially for semi-intrusive methods. ConclusionsThe possibility of having access to information about the confidence with which the network produces its outputs is a great advantage, both from the point of view of physicians who want to use neural networks as computer-aided diagnosis, and for developers who want to limit overfitting and obtain information about dataset problems in terms of out-of-distribution detection.

Characterization of Inductive Moderate Hyperthermia Effects on Intratumor Sarcoma-45 Heterogeneity Using Magnetic Resonance, Ultrasound and Histology Image Analysis

Characterization of Inductive Moderate Hyperthermia Effects on Intratumor Sarcoma-45 Heterogeneity Using Magnetic Resonance, Ultrasound and Histology Image Analysis

Analysis of thyroid nodule ultrasound images by image feature extraction technique

Analysis of thyroid nodule ultrasound images by image feature extraction technique

Non-invasive assessment of esophageal and fundic varices in patients with primary biliary cholangitis.

The Baveno VII consensus recommends endoscopic screening for varicose veins in cases of liver stiffness measurement (LSM) ≥ 20 kPa or platelet count ≤ 150 × 109/L. Whether this approach was appropriate for patients with primary biliary cholangitis (PBC) remains uncertain. This study expanded the observed risk factors by adding analysis of ultrasound images as a non-invasive tool to predict the risk of esophageal or fundic varices. We enrolled 111 patients with PBC whose complete ultrasound images, measurement data, and LSM data were available. The value of the periportal hypoechoic band (PHB), splenic area, and LSM in determining the risk of varicose veins and variceal rupture was analyzed. A prospective cohort of 67 patients provided external validation. The area under the receiver operating characteristic curve (AUC) for predicting varicose veins using LSM > 12.1 kPa or splenic areas > 41.2 cm2 was 0.806 (95% confidence interval (CI): 0.720-0.875) and 0.852 (95% CI: 0.772-0.912), respectively. This finding could assist in avoiding endoscopic screening by 76.6% and 83.8%, respectively, with diagnostic accuracy surpassing that suggested by Baveno VII guidelines. The AUCs for predicting variceal rupture using splenic areas > 56.8 cm2 was 0.717 (95% CI: 0.623-0.798). The diagnostic accuracy of PHB for variceal rupture was higher than LSM and splenic areas (75.7% vs. 50.5% vs. 68.5%). We recommend LSM > 12.1 kPa as a cutoff value to predict the risk of varicosity presence in patients with PBC. Additionally, the splenic area demonstrated high accuracy and relevance for predicting varicose veins and variceal rupture, respectively. The method is simple and reproducible, allowing endoscopy to be safely avoided. The measurement of the splenic area and identification of the periportal hypoechoic band (PHB) on ultrasound demonstrated high accuracy and relevance for predicting the risk of esophageal or fundic varices presence and variceal rupture, respectively. Predicting varices in patients with primary biliary cholangitis (PBC) can reduce the morbidity and mortality of gastrointestinal hemorrhage. Transient elastography (TE) and ultrasound play an important role in predicting patients with PBC with varices. TE and ultrasound can predict varicose veins and variceal rupture. Liver stiffness measurement and splenic area measurements can allow endoscopy to be safely avoided.

Explainable Artificial Intelligence (XAI) for Oncological Ultrasound Image Analysis: A Systematic Review

This review provides an overview of explainable AI (XAI) methods for oncological ultrasound image analysis and compares their performance evaluations. A systematic search of Medline Embase and Scopus between 25 March and 14 April 2024 identified 17 studies describing 14 XAI methods, including visualization, semantics, example-based, and hybrid functions. These methods primarily provided specific, local, and post hoc explanations. Performance evaluations focused on AI model performance, with limited assessment of explainability impact. Standardized evaluations incorporating clinical end-users are generally lacking. Enhanced XAI transparency may facilitate AI integration into clinical workflows. Future research should develop real-time methodologies and standardized quantitative evaluative metrics.

278. PROPOSED REVISION TO THE DESCRIPTION OF T3 FOR ESOPHAGEAL CANCER: REMOVAL OF “ADVENTITIA”

Abstract Background T3 is an important depth for esophageal cancer to target for surgical cure. According to the Union for International Cancer Control (UICC), T3 is defined as tumor invading the adventitia. However, it remains unclear what structures the adventitia refers to. In fact, tumors that do not directly invade adjacent organs have been classified as T3, even if they extend through the adventitia. Furthermore, in tumors arising at the esophagogastric junction (EGJ), it is difficult to determine the border with the subserosa that defines T3 of the stomach. Our aim was to revise the description of T3 in esophageal cancer. Methods The Union for International Cancer Control T-classification was reviewed from a histologic, pathologic, and embryologic perspective. Analysis of endoscopic ultrasound images, review of intraoperative images, and detailed observation of surgical specimens were followed by a review of the literature. Results Histologically, the adventitia is the extramural adipose connective tissue of the digestive tract and serves as a conduit for blood vessels, lymphatics, and nerves. The esophageal adventitia is further surrounded by connective tissue of adjacent organs, such as the tissue around the trachea, the tissue around the descending thoracic aorta, the pulmonary ligament, and the tissue on the diaphragm (Fig. 1a). In the 2022 revision of the Japanese Esophageal Society, resectable borderline cases in which adjacent organ invasion cannot be excluded by preoperative imaging were subclassified as borderline resectable T3 (T3br). From another perspective, this is a clinically reasonable criterion that not only adventitial invasion, but also invasion of connective tissue associated with adjacent organs is classified as T3. Beyond the EGJ, the adventitia is covered by mesothelium in the peritoneal cavity and leads to the subserosa. Endoscopic ultrasound revealed the fatty tissue of the adventitia as a highly echogenic layer on the surface of the muscularis propria, which continues uninterrupted beyond the EGJ into the stomach, indicating that the adventitia is connected to the subserosa. Intraoperative images showed that the descending posterior trunk of the vagus nerve and its associated vessels pass through the adipose tissue surrounding the esophagus in the esophageal hiatus (Fig. 1b). Thus, the ambiguity in the T-classification of esophageal cancer is not resolved as long as T3 is defined by the adventitia. In practice, however, physicians may have classified tumors as T2 if confined to the muscularis propria, T4 if invading adjacent organs, and T3 if neither. Conclusion We suggest that the description of T3 esophageal cancer be revised to read "tumor extends through the muscularis propria" rather than using the term "adventitia". This revision would clarify the depth criteria for determining the indication for surgical treatment not only for esophageal cancer, but also for EGJ carcinoma where the tumor center overlaps the gastric side.

A systematic review of the application of machine learning techniques to ultrasound tongue imaging analysis.

B-mode ultrasound has emerged as a prevalent tool for observing tongue motion in speech production, gaining traction in speech therapy applications. However, the effective analysis of ultrasound tongue image frame sequences (UTIFs) encounters many challenges, such as the presence of high levels of speckle noise and obscured views. Recently, the application of machine learning, especially deep learning techniques, to UTIF interpretation has shown promise in overcoming these hurdles. This paper presents a thorough examination of the existing literature, focusing on UTIF analysis. The scope of our work encompasses four key areas: a foundational introduction to deep learning principles, an exploration of motion tracking methodologies, a discussion of feature extraction techniques, and an examination of cross-modality mapping. The paper concludes with a detailed discussion of insights gleaned from the comprehensive literature review, outlining potential trends and challenges that lie ahead in the field.

Can incorporating image resolution into neural networks improve kidney tumor classification performance in ultrasound images?

Deep learning has been widely used in ultrasound image analysis, and it also benefits kidney ultrasound interpretation and diagnosis. However, the importance of ultrasound image resolution often goes overlooked within deep learning methodologies. In this study, we integrate the ultrasound image resolution into a convolutional neural network and explore the effect of the resolution on diagnosis of kidney tumors. In the process of integrating the image resolution information, we propose two different approaches to narrow the semantic gap between the features extracted by the neural network and the resolution features. In the first approach, the resolution is directly concatenated with the features extracted by the neural network. In the second approach, the features extracted by the neural network are first dimensionally reduced and then combined with the resolution features to form new composite features. We compare these two approaches incorporating the resolution with the method without incorporating the resolution on a kidney tumor dataset of 926 images consisting of 211 images of benign kidney tumors and 715 images of malignant kidney tumors. The area under the receiver operating characteristic curve (AUC) of the method without incorporating the resolution is 0.8665, and the AUCs of the two approaches incorporating the resolution are 0.8926 (P < 0.0001) and 0.9135 (P < 0.0001) respectively. This study has established end-to-end kidney tumor classification systems and has demonstrated the benefits of integrating image resolution, showing that incorporating image resolution into neural networks can more accurately distinguish between malignant and benign kidney tumors in ultrasound images.

Advancements in Research on the Classification of Benign and Malignant Breast Tumors Utilizing Ultrasound Radiomics and Deep Learning

This paper examines the applications of traditional machine learning and deep learning in the analysis of breast ultrasound images for tumor diagnosis and explores recent developments in multimodal imaging. Traditional machine learning efficiently classifies breast ultrasound images through preprocessing, feature extraction, and selection, utilizing classifiers such as support vector machines. However, the design of features is highly dependent and its application scope is limited. Deep learning methods, particularly convolutional neural networks, autonomously extract sophisticated features, demonstrating enhanced classification performance and generalization capabilities. For instance, they achieve diagnostic accuracies exceeding 90% in large-scale datasets, with some studies outperforming clinicians. Moreover, this study highlights that the multimodal analysis strategy, integrating breast ultrasound with shear wave elastography, compensates for the limitations of unimodal images and enhances diagnostic accuracy and reliability, signifying a significant advancement in the technology for early breast cancer diagnosis.

Quantitative analysis of contrast-enhanced ultrasound and superb microvascular imaging for the evaluation of disease activity in inflammatory bowel disease.

To evaluate the effectiveness of conventional US (ultrasound), SMI (superb microvascular imaging), and CEUS (contrast-enhanced ultrasound) features for the assessment of the activity of inflammatory bowel disease. Conventional US, CEUS and SMI features of 76 patients were retrospectively analyzed. Patients were categorized into two groups: active group (n = 57) and inactive group (n = 19), with endoscopic results as reference standard. Results in the active group and inactive group were compared using an independent t-test, Mann-Whitney U test, chi-square test, and receiver operating characteristic curve (ROC) analysis. Cut-off values were determined using ROC analysis, and sensitivity and specificity were calculated. US quantitative and TIC-based quantitative parameters were analyzed, and each patient was scored based on the parameters that are statistically significant and immediately available in the clinic to evaluate the diagnostic ability of conventional US, SMI, and CEUS features for active IBD patients. Qualitative parameters such as CEUS enhancement pattern I/II, LimbergIII/IV, and lost bowel stratification were reliable indicators of active patients. Quantitative parameters such as bowel thickness and VI of mSMI were reliable indicators of active patients. Patients scored based on these statistically significant parameters with a score ≥3, were highly suspected to be active patients. For TIC-based quantitative parameters, PE, WiAUC, WoAUC, WiWoAUC, WiR, WiPI, and WoR were statistically significant in the differentiation of active IBD from inactive IBD. Conventional US, SMI, and CEUS features may help in the differentiation of active IBD from inactive IBD and have potential application value in the choice of treatment options.

KRC-APM: Key region cutting and artificial prior model for breast cancer recognition in ultrasound images

Computer-aided analysis of ultrasound images is important for the early detection of breast cancer, obtaining more treatment time and improving the likelihood of survival. Current methods are either limited by predefined Regions of Interest (ROIs), suffer from a low proportion and variable locations of tumors, or lack appropriate modeling of tumor characteristics in ultrasound images. This paper proposes a framework called Key Region Cutting and Artificial Prior Model (KRC-APM) for breast cancer recognition in ultrasound images. Firstly, to avoid the reliance on predefined ROIs, as well as to increase the proportion of tumor areas and eliminate abundant background, we propose a Key Region Cutting (KRC) method, which analyzes the confidence levels for tumor areas and tolerantly cuts high-confidence areas into key regions. Next, to model the characteristics of tumors in ultrasound images, we design three types of diagnosis-related Artificial Priors (APs) under the guidance of experienced oncologists: Shape Modeling, Margin Analysis, and Echogenicity Pattern Indication. Finally, to fuse the key region with the extracted APs, we propose an Artificial Prior Model (APM), which synthetically learns the pattern of breast cancer. The proposed KRC-APM is validated on two public datasets. The experimental results demonstrate that the key regions identified by the KRC method and the designed APs significantly enhance the performance of breast cancer recognition. Furthermore, the proposed KRC-APM framework outperforms current state-of-the-art (SOTA) methods.

Dynamic Responses of Human Skin and Fascia to an Innovative Stimulation Device-Shear Wave Stimulation.

Exposure to mechanical stimuli such as pressure and stretching prompts the skin to undergo physiological adaptations to accommodate and distribute applied forces, a process known as mechanotransduction. Mechanotherapy, which leverages mechanotransduction, shows significant promise across various medical disciplines. Traditional methods, such as massage and compression therapy, effectively promote skin healing by utilizing this mechanism, although they require direct skin contact. This study introduces a novel contactless modality, Shear Wave Stimulation (SWS), and evaluates its efficacy compared to traditional massage in eliciting responses from human skin and fascia. Fifteen healthy volunteers received SWS, while another fifteen volunteers received massage. Tests of skin mechanical properties revealed significant enhancements in skin shear modulus for both methods, showing an increase of approximately 20%. Additionally, deformation analysis of ultrasound images showed distinct responses of the skin and fascia to the two stimuli. SWS induced extension in the dermis (∼18%), hypodermis (∼16%), and fascia (∼22%) along the X and Y axes. In contrast, massage compressed the skin layers, reducing the dermis by around 15% and the hypodermis by about 8%, while simultaneously stretching the superficial fascia by approximately 8%. The observed extension across the entire skin with SWS highlights its potential as a groundbreaking contactless approach for promoting skin healing. Furthermore, the differing responses in blood flow reaffirm the distinct stimulation modes of SWS and massage. These findings establish a foundation for future innovative skin therapy modalities.

FARN: Fetal Anatomy Reasoning Network for Detection With Global Context Semantic and Local Topology Relationship.

Accurate recognition of fetal anatomical structure is a pivotal task in ultrasound (US) image analysis. Sonographers naturally apply anatomical knowledge and clinical expertise to recognizing key anatomical structures in complex US images. However, mainstream object detection approaches usually treat each structure recognition separately, overlooking anatomical correlations between different structures in fetal US planes. In this work, we propose a Fetal Anatomy Reasoning Network (FARN) that incorporates two kinds of relationship forms: a global context semantic block summarized with visual similarity and a local topology relationship block depicting structural pair constraints. Specifically, by designing the Adaptive Relation Graph Reasoning (ARGR) module, anatomical structures are treated as nodes, with two kinds of relationships between nodes modeled as edges. The flexibility of the model is enhanced by constructing the adaptive relationship graph in a data-driven way, enabling adaptation to various data samples without the need for predefined additional constraints. The feature representation is further refined by aggregating the outputs of the ARGR module. Comprehensive experimental results demonstrate that FARN achieves promising performance in detecting 37 anatomical structures across key US planes in tertiary obstetric screening. FARN effectively utilizes key relationships to improve detection performance, demonstrates robustness to small-scale, similar, and indistinct structures, and avoids some detection errors that deviate from anatomical norms. Overall, our study serves as a resource for developing efficient and concise approaches to model inter-anatomy relationships.

Multi-Derivational Parsing of Vague Languages- The New Paradigm of Syntactic Pattern Recognition.

The new paradigm of syntactic pattern recognition, SPR, which uses multi-derivational parsing of vague languages is introduced in the paper. The methodology proposed addresses the issue of the recognition of vague/distorted patterns which is one of the important open problems in the area. The concept of the vague language of patterns and the efficient parsing method based on the class of dynamically programmed grammars are introduced. A vague language is defined with vague primitives which are vectors of "neighboring" primitives associated with measures of distance, probability, fuzziness, etc. The use of vague primitives allows us to identify b best structural templates during multi-derivational parsing that can be used for getting more adequate final result. The generic architecture of SPR system based on the approach proposed together with the system's applications for short-term electrical load forecasting and for analysis of ultrasound images in order to diagnose congenital defects of fetal palates are presented. The results of the experimental studies are discussed.

Real-time artificial intelligence-based texture analysis of muscle ultrasound data for neuromuscular disorder assessment

ObjectiveMany artificial intelligence approaches to muscle ultrasound image analysis have not been implemented on usable devices in clinical neuromuscular medicine practice, owing to high computational demands and lack of standardised testing protocols. This study evaluated the feasibility of using real-time texture analysis to differentiate between various pathological conditions. MethodsWe analysed 17,021 cross-sectional ultrasound images of the biceps brachii of 75 participants, including 25 each with neurogenic disorders, myogenic disorders, and healthy controls. The size and location of the regions of interest were randomly selected to minimise bias. A random forest classifier utilising texture features such as Dissimilarity and Homogeneity was developed and deployed on a mobile PC, enabling real-time analysis. ResultsThe classifier distinguished patients with an accuracy of 81 %. Echogenicity and Contrast from the Co-Occurrence Matrix were significant predictive features. Validation on 15 patients achieved accuracies of 78 %/93 % per image/patient over 15-second videos, respectively. The use of a mobile PC facilitated real-time estimation of the underlying pathology during ultrasound examination, without influencing procedures. ConclusionsReal-time automatic texture analysis is feasible as an adjunct for the diagnosis of neuromuscular disorders. SignificanceArtificial intelligence using texture analysis with a light computational load supports the semi-quantitative evaluation of neuromuscular ultrasound.

Targeted nanoparticles triggered by plaque microenvironment for atherosclerosis treatment through cascade effects of reactive oxygen species scavenging and anti-inflammation

Inflammatory factors and reactive oxygen species (ROS) are risk factors for atherosclerosis. Many existing therapies use ROS-sensitive delivery systems to alleviate atherosclerosis, which achieved certain efficacy, but cannot eliminate excessive ROS. Moreover, the potential biological safety concerns of carrier materials through chemical synthesis cannot be ignored. Herein, an amphiphilic low molecular weight heparin- lipoic acid conjugate (LMWH-LA) was used as a ROS-sensitive carrier material, which consisted of injectable drug molecules used clinically, avoiding unknown side effects. LMWH-LA and curcumin (Cur) self-assembled to form LLC nanoparticles (LLC NPs) with LMWH as shell and LA/Cur as core, in which LMWH could target P-selectin on plaque endothelial cells and competitively block the migration of monocytes to endothelial cells to inhibit the origin of ROS and inflammatory factors, and LA could be oxidized to trigger hydrophilic-hydrophobic transformation and accelerate the release of Cur. Cur released within plaques further exerted anti-inflammatory and antioxidant effects, thereby suppressing ROS and inflammatory factors. We used ultrasound imaging, pathology and serum analysis to evaluate the therapeutic effect of nanoparticles on atherosclerotic plaques in apoe−/− mice, and the results showed that LLC showed significant anti-atherosclerotic effects. Our finding provided a promising therapeutic nanomedicine for the treatment of atherosclerosis.

Liner orientation change of dual mobility cup determined via 3D ultrasound imaging and motion analysis: A cadaver study

BackgroundA mobile polyethylene liner enables the dual mobility cup (DMC) to contribute to restoring hip joint range-of-motion, decreasing wear and increasing implant stability. However, more data is required on how liner orientation changes with hip joint movement. As a first step towards better understanding liner orientation change in vivo, this cadaver study focuses on quantifying DMC liner orientation change after different hip passive movements, using ultrasound imaging and motion analysis. HypothesisThe liner does not always go back to its initial orientation and its final orientation depends mainly on hip movement amplitude. Methods3D ultrasound imaging and motion analysis were used to define liner and hip movements for four fresh post-mortem human subjects with six implanted DMC. Abduction and anteversion angles of the liner plane relative to the pelvis were measured before and after hip flexion, internal rotation, external rotation, abduction, adduction. ResultsLiner orientation changes were generally defined by angle variation smaller than 5°, with the liner nearly going back to its initial orientation. However, hip flexion caused liner abduction and anteversion angle variations greater than 15°. Except for hip adduction, only weak or no correlation was found between the final angle of the liner and the maximal hip joint movement amplitude. DiscussionThis study is the first attempt to quantify liner orientation change for implanted DMC via ultrasound imaging and constitutes a step forward in the understanding of liner orientation change and its relationship with hip joint movement. The hypothesis that the final liner abduction and anteversion angles depend mainly on hip movement amplitude was not confirmed, even if hip flexion was the movement generating the most liner orientation changes over 15°. This approach should be extended to in vivo clinical investigations, as measured liner angle variation could provide important support for the wear and stability claims made for DMC. Level of evidenceIV; cadaveric study.