Medical Imaging Research Articles

Deep Learning Analysis of 3D Volume of Brain Anomaly Using MRI

In this study, we present a comprehensive investigation into the efficacy of two state-of-the-art deep learning architectures, 3D UNet and VNet, for brain tumor detection and segmentation in volumetric medical images.

Early detection of glaucoma: feature visualization with a deep convolutional network

ABSTRACT Computer-aided diagnosis of ocular disorders like glaucoma can be effectively performed with retinal fundus images. Since the advent of machine learning and later deep learning techniques in medical image analysis, research focusing on automated and early detection of glaucoma has gained prominence. In this paper, we show how a deep Convolutional Neural Network model can be used in visualising the features related to Glaucoma that are consistent with (a subset of) those used by ophthalmologists for diagnosis. However, since the doctors do not necessarily depend entirely on these features but back them up with other important measurements such as visual field testing (HVF), any deep neural network such as CNN may not necessarily be expected to yield 100% accuracy. Further, other important parameters such as Intraocular Pressure (IOP), which may be built into a deep learning model, may not necessarily correlate well with the presence of Glaucoma. In this work, a deep CNN model has been trained and tested on a large number of high-quality fundus images, both normal and glaucomatous. We compare the results with transfer learning models such as VGG16, ResNet50, and MobileNetV2. On an average, we obtained an accuracy of 93.75% in identifying glaucoma, focusing only on the features of the fundus image.

Enhancing EfficientNetv2 with global and efficient channel attention mechanisms for accurate MRI-Based brain tumor classification

AbstractThe early and accurate diagnosis of brain tumors is critical for effective treatment planning, with Magnetic Resonance Imaging (MRI) serving as a key tool in the non-invasive examination of such conditions. Despite the advancements in Computer-Aided Diagnosis (CADx) systems powered by deep learning, the challenge of accurately classifying brain tumors from MRI scans persists due to the high variability of tumor appearances and the subtlety of early-stage manifestations. This work introduces a novel adaptation of the EfficientNetv2 architecture, enhanced with Global Attention Mechanism (GAM) and Efficient Channel Attention (ECA), aimed at overcoming these hurdles. This enhancement not only amplifies the model’s ability to focus on salient features within complex MRI images but also significantly improves the classification accuracy of brain tumors. Our approach distinguishes itself by meticulously integrating attention mechanisms that systematically enhance feature extraction, thereby achieving superior performance in detecting a broad spectrum of brain tumors. Demonstrated through extensive experiments on a large public dataset, our model achieves an exceptional high-test accuracy of 99.76%, setting a new benchmark in MRI-based brain tumor classification. Moreover, the incorporation of Grad-CAM visualization techniques sheds light on the model’s decision-making process, offering transparent and interpretable insights that are invaluable for clinical assessment. By addressing the limitations inherent in previous models, this study not only advances the field of medical imaging analysis but also highlights the pivotal role of attention mechanisms in enhancing the interpretability and accuracy of deep learning models for brain tumor diagnosis. This research sets the stage for advanced CADx systems, enhancing patient care and treatment outcomes.

QML Powered Interface for Diffusion Imaging

In the field of medical imaging, Diffusion Imaging (DI) has emerged as a powerful technique for investigating the microstructural properties of biological tissues. However, the complexity of DI analysis software often poses a significant barrier to its widespread adoption, as it typically requires proficiency in Python programming and command-line interactions. This technical barrier can limit the accessibility of DI technology to individuals without extensive technical expertise, hindering its potential impact in various medical and research applications To address this challenge, we propose a novel solution that leverages the capabilities of Query Markup Language (QML) to develop a user-friendly interface for Diffusion Imaging. By combining the power of Python technology, which forms the core of DI analysis, with the intuitive interface design capabilities of QML, our project aims to democratize DI analysis and make it accessible to a broader audience, including medical professionals, researchers, and students. Our research focuses on bridging the gap between the technical complexities of DI analysis and user accessibility. The proposed QML-powered interface will feature modern UI elements with fluid animations, ensuring a seamless and engaging user experience. Crucially, it will abstract away the intricacies of Python programming and command-line interactions, allowing users to concentrate on the analysis and interpretation of DI data without the burden of technical hurdles.

Medical steganography: Enhanced security and image quality, and new S-Q assessment

Medical diagnosing systems generate sensitive information involving the patient’s privacy. Steganography supports securely embedding secrecy, guaranteeing that an adversary cannot distinguish cover images and stego images. However, directly applying natural-image steganography in medicine significantly affects efficacy and security. Until now, medical steganography is still in its infancy, mostly solving simple data-hiding policies, remaining barren in enhancing security.To fill this gap, we propose an end-to-end steganographic framework targeting protecting medical images efficiently. Our framework integrates medical-image adaptive steganography with a generative adversarial network named MedSteGAN. As for medical images without rich semantics and textures, we observe the heightened importance of involving both security and image quality. We shed light on designing a “security-quality (S-Q) assessment”, attaining comprehensive evaluation for all aspects. Following the S-Q assessment, we improve the overall practicality of medical steganography by (i) redesigning an effective U-shape generator network for removing the dependence on complex modules, (ii) improving feature extraction capability via convolutions with various kernel sizes and avoiding the checkerboard effect through upsampling layers, (iii) optimizing the loss function via the multi-scale structural similarity and high-frequency components. Extensive experiments demonstrate that MedSteGAN outperforms state-of-the-art steganographic schemes, showing the best overall results in the security-quality domain.

Analytical Study of Medical PET Images by DWT and PCA

The PET medical images need clarification to reach a correct diagnosis of the disease by removing noise from them, and this is done by applying several techniques, including DWT, PCA. DWT is an algorithm that compresses an image and removes noise by dividing the image into several levels, removing the unnecessary (neglected) part of the image. The PCA algorithm compresses the error parameters in the image and then reconstructs them.

THE IMPORTANCE OF ULTRASONOGRAPHY IN THE DETECTION OF CONGENITAL HEART DISEASES IN CHILDREN

Over the last few years, remarkable progress has been made in the field of congenital heart diseases. Improvements considering diagnostic modalities, especially imaging, in surgical and interventional techniques, as well as in postoperative therapy and care, have contributed to a significant reduction in mortality and morbidity. One of the most important applications of medical imaging techniques in children is the detection and treatment of congenital heart anomalies. Objective of this article is to show the importance of ultrasonography in the detection of congenital heart diseases in children. The study was conducted on children with simple and complex congenital heart diseases and was conducted on the Pediatric Clinic, UKCS. The research is descriptive on a representative sample. In our study, 166 children were observed, of which 148 children (77 boys, 71 girls) with simple congenital heart diseases, and 18 children (8 boys, 10 girls) with complex congenital heart diseases. Out of the total number of observed children, 115 had a surgical correction, 97 children with simple congenital heart diseases (45 boys, 52 girls) and 18 children with complex congenital heart diseases (8 boys, 10 girls). The number of children monitored through the Cardiac Counseling Center who didn’t undergo surgical correction was 51, all with simple congenital heart diseases. Out of the total number of observed children who were frequently coming for follow-ups, 28 children had changes on the ECG, and 138 of them had no changes on the ECG, 93 were surgically treated, and 73 of them were on conservative therapy. Based on the results of the research, we conclude that ultrasonography is an important method in the detection and treatment of congenital heart diseases.

PSO-PSP-Net + InceptionV3: An optimized hyper-parameter tuned Computer-Aided Diagnostic model for liver tumor detection using CT scan slices

An automated diagnostic system leads to a supreme requirement in medical image analysis, greatly impacting the death rate due to the high spreading rate of liver tumors. However, the traditional histopathological diagnostic processes cause more disruption in the medical detection framework because of the procedure of manual estimation of CT scans and medical labor-intensive, which lead to the time consumption process. It is a highly challenging task for the segmentation of liver tumor because of the irregular appearance, distinct densities, patchy edges, and tumor sizes. To overcome this issue, an automated computer-aided diagnosis framework has been utilized to diagnose the liver cancer quickly and accurately with the less workload of expert radiologists.Presently, Deep learning-based techniques have been applied to identify the tumor regions and assist radiologists in the initial diagnosis and scrutinizing the severity level. The proposed work presents a modified pyramid scene parsing network (PSP-Net) model based on InceptionV3 deep network architecture with a particle swarm optimization algorithm, which increases the efficiency and effectiveness of the liver diagnostic model. Three publicly accessible benchmark datasets have been engaged with the pre-processing task accomplished by image normalization and data augmentation techniques. Moreover, a hyper-parameter tuned improved PSO-PSP-Net algorithm has analyzed the optimal set of gathered features. In the experimental evaluation, the proposed model outperformed than grey wolf optimization (GWO-PSP-Net), PSO-U-Net, GWO-U-Net, opposition based spotted hyena optimization (O-SHO-CRNN), and grey wolf-class topper optimization (GW-CTO) + U-Net by 8.42 %, 4.9 %, 9.54 %, 5.12 %, and 1.75 %, respectively in terms of accuracy for LiTS dataset. The developed PSO-PSP-Net model attained better accuracy than GWO-PSP-Net, PSO-U-Net, GWO-U-Net, and Residual Atrous U-Net (RA-U-Net) for the segmentation of Type-I and Type-II liver tumors by 6.88 %, 8.44 %, 13.75 %, 5.99 % and 6.12 %, 9.74 %, 17.3 %, and 5.11 %, respectively for 3DIRCAD-b1 dataset. The obtained accuracy of the developed model is 14.09 %, 7.54 %, 16.36 %, 2.64 %, and 14.9 %, 8.86 %, 17.76 %, and 4.87 % maximized than GWO-PSP-Net, PSO-U-Net, GWO-U-Net, and RA-U-Net, respectively, for the segmentation of the infected arteries and bones in the 3DIRCAD-b1 dataset. For the CHAOS dataset, the improved PSO-PSP-Net model outperformed GWO-PSP-Net, PSO-U-Net, and GWO-U-Net, respectively, in terms of overall accuracy by 7.44 %, 8.85 %, and 17.2 %, respectively. The investigational analysis validates the proposed PSO-PSP-Net model's effectiveness and performance in obtaining high segmentation accuracy in comparison with other state-of-the-art approaches.

Ultrasound Elastography: Methods, Clinical Applications, and Limitations: A Review Article

Ultrasound is a highly adaptable medical imaging modality that offers several applications and a wide range of uses, both for diagnostic and therapeutic purposes. The principles of sound wave propagation and reflection enable ultrasound imaging to function as a highly secure modality. This technique facilitates the production of real-time visual representations, thereby assisting in the evaluation of various medical conditions such as cardiac, gynecologic, and abdominal diseases, among others. The ultrasound modality encompasses a diverse range of modes and mechanisms that serve to enhance the methodology of pathology and physiology assessment. Doppler imaging and US elastography, in particular, are two such techniques that contribute to this expansion. Elastography-based imaging methods have attracted significant interest in recent years for the non-invasive evaluation of tissue mechanical characteristics. These techniques utilize the changes in soft tissue elasticity in various diseases to generate both qualitative and quantitative data for diagnostic purposes. Specialized imaging techniques collect data by identifying tissue stiffness under mechanical forces such as compression or shear waves. However, in this review paper, we provide a comprehensive examination of the fundamental concepts, underlying physics, and limitations associated with ultrasound elastography. Additionally, we present a concise overview of its present-day clinical utilization and ongoing advancements across many clinical domains.

AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes.

Type 2 diabetes (T2D) presents a formidable global health challenge, highlighted by its escalating prevalence, underscoring the critical need for precision health strategies and early detection initiatives. Leveraging artificial intelligence, particularly eXtreme Gradient Boosting (XGBoost), we devise robust risk assessment models for T2D. Drawing upon comprehensive genetic and medical imaging datasets from 68,911 individuals in the Taiwan Biobank, our models integrate Polygenic Risk Scores (PRS), Multi-image Risk Scores (MRS), and demographic variables, such as age, sex, and T2D family history. Here, we show that our model achieves an Area Under the Receiver Operating Curve (AUC) of 0.94, effectively identifying high-risk T2D subgroups. A streamlined model featuring eight key variables also maintains a high AUC of 0.939. This high accuracy for T2D risk assessment promises to catalyze early detection and preventive strategies. Moreover, we introduce an accessible online risk assessment tool for T2D, facilitating broader applicability and dissemination of our findings.

BiU-net: A dual-branch structure based on two-stage fusion strategy for biomedical image segmentation

Background and objectiveComputer-based biomedical image segmentation plays a crucial role in planning of assisted diagnostics and therapy. However, due to the variable size and irregular shape of the segmentation target, it is still a challenge to construct an effective medical image segmentation structure. Recently, hybrid architectures based on convolutional neural networks (CNNs) and transformers were proposed. However, most current backbones directly replace one or all convolutional layers with transformer blocks, regardless of the semantic gap between features. Thus, how to sufficiently and effectively eliminate the semantic gap as well as combine the global and local information is a critical challenge. MethodsTo address the challenge, we propose a novel structure, called BiU-Net, which integrates CNNs and transformers with a two-stage fusion strategy. In the first fusion stage, called Single-Scale Fusion (SSF) stage, the encoding layers of the CNNs and transformers are coupled, with both having the same feature map size. The SSF stage aims to reconstruct local features based on CNNs and long-range information based on transformers in each encoding block. In the second stage, Multi-Scale Fusion (MSF), BiU-Net interacts with multi-scale features from various encoding layers to eliminate the semantic gap between deep and shallow layers. Furthermore, a Context-Aware Block (CAB) is embedded in the bottleneck to reinforce multi-scale features in the decoder. ResultsExperiments on four public datasets were conducted. On the BUSI dataset, our BiU-Net achieved 85.50 % on Dice coefficient (Dice), 76.73 % on intersection over union (IoU), and 97.23 % on accuracy (ACC). Compared to the state-of-the-art method, BiU-Net improves Dice by 1.17 %. For the Monuseg dataset, the proposed method attained the highest scores, reaching 80.27 % and 67.22 % for Dice and IoU. The BiU-Net achieves 95.33 % and 81.22 % Dice on the PH2 and DRIVE datasets. ConclusionsThe results of our experiments showed that BiU-Net transcends existing state-of-the-art methods on four publicly available biomedical datasets. Due to the powerful multi-scale feature extraction ability, our proposed BiU-Net is a versatile medical image segmentation framework for various types of medical images. The source code is released on (https://github.com/ZYLandy/BiU-Net).

LCAMix: Local-and-contour aware grid mixing based data augmentation for medical image segmentation

Medical image segmentation often faces challenges related to overfitting, primarily due to the limited and complex training samples. This challenge often prompts the use of self-supervised learning and data augmentation. However, self-supervised learning requires well-defined hand-crafted tasks and multiple training stages. On the other hand, basic image augmentation techniques like cropping, rotation, and flipping, effective for natural scene images, have limited efficacy for medical images due to their isotropic nature.While regional dropout regularization data augmentation methods have proven effective in image recognition tasks, their application in image segmentation is not as extensively studied. Additionally, existing augmentation methods often operate on square regions, leading to the loss of crucial contour information. This is particularly problematic for medical image segmentation tasks dealing with regions of interest characterized by intricate shapes. In this work, we introduce LCAMix, a novel data augmentation approach designed for medical image segmentation. LCAMix operates by blending two images and their segmentation masks based on their superpixels, incorporating a local-and-contour-aware strategy. The training process on augmented images adopts two auxiliary pretext tasks: firstly, classifying local superpixels in augmented images using an adaptive focal margin, leveraging segmentation ground truth masks as prior knowledge; secondly, reconstructing the two source images using mixed superpixels as mutual masks, emphasizing spatial sensitivity. Our method stands out as a simple, one-stage, model-agnostic, and plug-and-play data augmentation solution applicable to various segmentation tasks. Notably, it requires no external data or additional models. Extensive experiments validate its superior performance across diverse medical segmentation datasets and tasks. The source codes are available at https://github.com/DanielaPlusPlus/DataAug4Medical.

Monkey Pox Detection using Deep learning

Monkeypox is a rare zoonotic disease caused by the Monkeypox virus, which manifests in humans with symptoms similar to those of smallpox. Early detection and accurate diagnosis are crucial for effective management and containment of outbreaks. This research proposes a novel Monkeypox detection system utilizing machine learning techniques to enhance the speed and accuracy of diagnosis. The proposed system integrates various data sources, including clinical records, medical imaging, and demographic information, to develop a comprehensive dataset for model training. Machine learning algorithms, such as convolutional neural networks (CNNs) for image analysis and ensemble methods for combining diverse data modalities, are employed to identify patterns indicative of Monkeypox infection

Utilizing Artificial Neural Networks for Geometric Bone Model Reconstruction in Mandibular Prognathism Patients

Patient-specific 3D models of the human mandible are finding increasing utility in medical fields such as oral and maxillofacial surgery, orthodontics, dentistry, and forensic sciences. The efficient creation of personalized 3D bone models poses a key challenge in these applications. Existing solutions often rely on 3D statistical models of human bone, offering advantages in rapid bone geometry adaptation and flexibility by capturing a range of anatomical variations, but also a disadvantage in terms of reduced precision in representing specific shapes. Considering this, the proposed parametric model allows for precise manipulation using morphometric parameters acquired from medical images. This paper highlights the significance of employing the parametric model in the creation of a personalized bone model, exemplified through a case study targeting mandibular prognathism reconstruction. A personalized model is described as 3D point cloud determined through the utilization of series of parametric functions, determined by the application of geometrical morphometrics, morphology properties, and artificial neural networks in the input dataset of human mandible samples. With 95.05% of the personalized model’s surface area displaying deviations within −1.00–1.00 mm relative to the input polygonal model, and a maximum deviation of 2.52 mm, this research accentuates the benefits of the parametric approach, particularly in the preoperative planning of mandibular deformity surgeries.

TransDiff: medical image segmentation method based on Swin Transformer with diffusion probabilistic model

TransDiff: medical image segmentation method based on Swin Transformer with diffusion probabilistic model

Leveraging Autoencoders for Better Representation Learning

ABSTRACT The effectiveness of a machine learning algorithm depends to a considerable extent on the representation used for the modeling. It is more critical with medical image data like Parkinson’s, where complexity plays a significant role. The paper proposes a methodology that leverages representation learning in Parkinson’s disease data. It uses seven types of autoencoders as learning techniques, identifying the best among them. The method is validated using three additional measures: Firstly, another dataset of Parkinson’s is used to confirm its effectiveness, thereby making the method dataset-agnostic. Secondly, seven machine-learning techniques formulate the problem in a supervised learning setting by taking the representation given by the best autoencoder as the features and the disease stage as the labels. Lastly, it uses three-dimensionality reduction techniques to visualize these latent variables or representations in a lower dimension. The high accuracy demonstrated at the supervised learning level, and the formation of patterns exhibited at the visualization level demonstrate the effectiveness of the proposed methodology.

Enhancing clinical diagnostics: novel denoising methodology for brain MRI with adaptive masking and modified non-local block.

Medical image denoising has been a subject of extensive research, with various techniques employed to enhance image quality and facilitate more accurate diagnostics. The evolution of denoising methods has highlighted impressive results but struggled to strike equilibrium between noise reduction and edge preservation which limits its applicability in various domains. This paper manifests the novel methodology that integrates an adaptive masking strategy, transformer-based U-Net Prior generator, edge enhancement module, and modified non-local block (MNLB) for denoising brain MRI clinical images. The adaptive masking strategy maintains the vital information through dynamic mask generation while the prior generator by capturing hierarchical features regenerates the high-quality prior MRI images. Finally, these images are fed to the edge enhancement module to boost structural information by maintaining crucial edge details, and the MNLB produces the denoised output by deriving non-local contextual information. The comprehensive experimental assessment is performed by employing two datasets namely the brain tumor MRI dataset and Alzheimer's dataset for diverse metrics and compared with conventional denoising approaches. The proposed denoising methodology achieves a PSNR of 40.965 and SSIM of 0.938 on the Alzheimer's dataset and also achieves a PSNR of 40.002 and SSIM of 0.926 on the brain tumor MRI dataset at a noise level of 50% revealing its supremacy in noise minimization. Furthermore, the impact of different masking ratios on denoising performance is analyzed which reveals that the proposed method showed PSNR of 40.965, SSIM of 0.938, MAE of 5.847, and MSE of 3.672 at the masking ratio of 60%. Moreover, the findings pave the way for the advancement of clinical image processing, facilitating precise detection of tumors in clinical MRI images.

Enhanced Segmentation of Ischemic Stroke Lesion in MRI Images Using a Geometrically Customised Deep Convolution Model (GCDCM)

Ischemic stroke lesion often known as a stroke, is a significant health issue that requires accurate analysis and classification of brain magnetic resonance imaging (MRI) data. In this study, we propose a novel deep transfer learning approach, called geometrically customized deep convolution model, for the purpose of MRI analysis and classification of brain stroke. Neurostroke segmentation is a serious medical image processing challenge. Segmented regions aid disease identification and treatment. Anywhere can form thrombi. Segmentation facilitates automatic detection because they can be any size or shape. Popular image analysis tool MRI diagnoses well. This diagnostic method shows brain stroke architecture. MRI must replace manual detection. Online datasets recommended cerebral stroke detection and segmentation. Deep learning model MRI scans and detectron 2 with masked CNN Nets segment thrombus. This net architecture recognises dataset stroke boundaries. Classifying strokes with vgg16, resnet50, inceptionv3, and resnet5 transfer learning is possible. Mask the image, then binary predict by eliminating the skull, extracting features, and iterating to find stroke. The model and thrombus mask are predicted if the binary prediction matches the human forecast. Otherwise, data processing resumes. Binary prediction uses the segmentation region and pixels overlap between the ground truth and predicted segmentation to calculate parameters. Compared to reality, the categorization of medical images with weak signals seems tough, especially with a short "train" dataset. Mixing deep learning architectures avoids these drawbacks and extracts signals to accurately classify classes. Deep neural networks best recognise, find, and divide computer vision objects for clinical image analysis. Preprocessing MRI scans, skull stripping with deep CNN architecture combinational net, and brain stroke segmentation are our main tasks. Modern medical image processing is hard. Flexible and uneven borders make brain strokes hard to identify and segment. The transfer learning-based super pixel approach segments brainstrokes. Because we predict every visual pixel, dense prediction occurs. Early discovery of thrombus improves treatment and survival. These procedures have considerably improved our quality indexes.

Experimental investigation of some photon interaction parameters of popular restorative dental materials with a non-destructive technique

Dental restorative materials are widely used to restore esthetics and function in prosthetic treatments. In this paper, reflection coefficients and effective atomic numbers of some restorative materials (Polyetheretherketone (PEEK), feldspathic porcelain (veneering porcelain on cobalt–chromium alloy as metal framework), lithium disilicate glass-ceramic, zircon core (veneering porcelain on yttria-stabilized tetragonal zirconia polycrystal), monolithic zirconia, and zirconia-reinforced lithium silicate glass ceramic) were measured by using 59.54 keV energy gamma rays emitted from an Am-241 radioactive source. The scattering peaks of the restorative materials were detected using an HPGe detector. The gamma radiation absorption parameters of these materials (MAC, LAC, MFP, and HVL) were also investigated using a ULEGe detector for 59.54 keV photons. It is observed that the largest MAC value is Monolithic zirconia. The material with the highest reflection parameter was found to be PEEK. Of the dental restorative materials investigated, PEEK has the lowest effective atomic number value of 21.650 and Monolithic zirconia has the highest effective atomic number value of 37.841. Effective atomic numbers can be used in non-destructive analysis and medical imaging, as is well known. In addition, the calibration curve obtained can be used in the qualitative analysis of different restorative and implant materials.

Design and Characterisation of a Read-Out System for Wireless Monitoring of a Novel Implantable Sensor for Abdominal Aortic Aneurysm Monitoring.

Abdominal aortic aneurysm (AAA) is a dilation of the aorta artery larger than its normal diameter (>3 cm). Endovascular aneurysm repair (EVAR) is a minimally invasive treatment option that involves the placement of a graft in the aneurysmal portion of the aorta artery. This treatment requires multiple follow-ups with medical imaging, which is expensive, time-consuming, and resource-demanding for healthcare systems. An alternative solution is the use of wireless implantable sensors (WIMSs) to monitor the growth of the aneurysm. A WIMS capable of monitoring aneurysm size longitudinally could serve as an alternative monitoring approach for post-EVAR patients. This study has developed and characterised a three-coil inductive read-out system to detect variations in the resonance frequency of the novel Z-shaped WIMS implanted within the AAA sac. Specifically, the spacing between the transmitter and the repeater inductors was optimised to maximise the detection of the sensor by the transmitter inductor. Moreover, an experimental evaluation was also performed for different orientations of the transmitter coil with reference to the WIMS. Finally, the FDA-approved material nitinol was used to develop the WIMS, the transmitter, and repeater inductors as a proof of concept for further studies. The findings of the characterisation from the air medium suggest that the read-out system can detect the WIMS up to 5 cm, regardless of the orientation of the Z-shape WIMS, with the detection range increasing as the orientation approaches 0°. This study provides sufficient evidence that the proposed WIMS and the read-out system can be used for AAA expansion over time.