Influence of tube current and metal artifact reduction on the diagnosis of external cervical resorption in teeth adjacent to a dental implant in CBCT: an ex-vivo study.

OBJECTIVES: This ex-vivo study aimed to assess the influence of tube current (mA) and metal artifact reduction (MAR) on the diagnosis of early external cervical resorption (EECR) in cone-beam computed tomography (CBCT) in the presence of an adjacent dental implant. MATERIALS AND METHODS: Twenty-three single-rooted teeth were sectioned longitudinally and EECR was induced using a spherical drill and 5% nitric acid in 10 teeth. Each tooth was positioned in the socket of the lower right canine of a dry human mandible and CBCT scans were acquired using 90 kVp, voxel of 0.085 mm, field of view of 5 x 5 cm, and varying tube current (4, 8 or 12 mA), MAR (enabled or disabled) and implant conditions (with a zirconia implant in the socket of the lower right first premolar or without). Five oral radiologists evaluated the presence of EECR in a 5-point scale and the diagnostic values (area under the receiver operating characteristic curve - AUC, sensitivity, and specificity) were compared using multi-way Analysis of Variance (α = 0.05). Kappa test assessed intra-/inter-evaluator agreement. RESULTS: The tube current only influenced the AUC values in the presence of the implant and when MAR disabled; in this case, 8 mA showed lower values (p<0.007). MAR did not influence the diagnostic values (p>0.05). In general, the presence of an implant reduced the AUC values (p<0.0001); sensitivity values with 8 mA and MAR disabled, and specificity values with 4 mA and MAR enabled and 8 mA regardless MAR were also decreased (p<0.0001). CONCLUSIONS: Variations in tube current and MAR were unable to improve EECR detection, which was impaired by the presence of an adjacent implant. CLINICAL RELEVANCE: Increasing tube current or activating MAR tool does not improve EECR diagnosis, which is hampered by the artifacts generated by dental implants.

When in Doubt, Add SPECT/CT: A Case of Mistaken Identity.

A 63-y-old woman with a history of breast cancer presented with concerns of osseous metastasis. Initial whole-body planar bone scintigraphy revealed a focus of concern overlying the sternum. SPECT/CT images revealed the anomaly-localized activity in the needleless hub attached to the chemotherapy port. If not for the precision of SPECT/CT, such a rare artifact could have led to a false-positive diagnosis, particularly impactful in breast cancer patients. This case emphasizes the critical role of SPECT/CT in accurate diagnoses.

Influence of CBCT filters and contrast adjustments on peri-implant buccal bone thickness measurement and blooming expression.

OBJECTIVES: To assess whether filter and contrast adjustments can improve the accuracy of CBCT in measuring the buccal bone thickness (BBT) adjacent to dental implants by reducing blooming artifacts. MATERIALS AND METHODS: Homogeneous bone blocks with peri-implant BBT of 0.3 mm, 0.5 mm, and 1 mm were scanned using the Orthophos SL system. Three dentists measured the BBT in 234 CBCT scans under different settings of contrast adjustments and 'Sharpen' filter activation. Additionally, implant diameter measurements were taken to assess blooming artifact expression. The differences between tomographic and actual measurements of BBT and implant diameter [(CBCT - actual) * 100 / actual] were subjected to Mixed ANOVA (α = 0.05). RESULTS: The group with the thinnest BBT (0.3 mm) had the greatest difference between tomographic and actual measurements (79.9% ± 29.0%). Conversely, the 0.5 mm (36.1% ± 38.4%) and 1 mm (29.4% ± 12.3%) groups exhibited lower differences (p < 0.05). 'Sharpen' filter activation reduced blooming expression since it resulted in a lower difference for implant diameter (p < 0.05), but it did not influence BBT measurements (p = 0.673). Contrast settings had no impact on BBT (p = 0.054) or implant diameter measurements (p = 0.079). CONCLUSION: Although filter activation reduced blooming artifacts, neither filter nor contrast adjustments improved the accuracy of CBCT in measuring peri-implant BBT; actual BBT influenced this task. CLINICAL RELEVANCE: When assessing the peri-implant buccal bone plate in the CBCT system studied, dental surgeons may find it beneficial to adjust contrast and apply filters according to their preferences, since such adjustments were found to have no adverse effects on the diagnostic accuracy of this task. The use of the 'Sharpen' filter may lead to improved representation of implant dimensions.

Overview of artificial intelligence in point-of-care ultrasound. New horizons for respiratory system diagnoses.

Throughout the past decades ultrasonography did not prove to be a procedure of choice if regarded as part of the routine bedside examination. The reason was the assumption defining the lungs and the bone structures as impenetrable by ultrasound. Only during the recent several years has the approach to the use of such tool in clinical daily routines changed dramatically to offer so-called point-of-care ultrasonography (POCUS). Both vertical and horizontal artefacts became valuable sources of information about the patient's clinical condition, assisting therefore the medical practitioner in differential diagnosis and monitoring of the patient. What is important is that the information is delivered in real time, and the procedure itself is non-invasive. The next stage marking the progress made in this area of diagnostic imaging is the development of arti-ficial intelligence (AI) based on machine learning algorithms. This article is intended to present the available, innovative solutions of the ultrasound systems, including Smart B-line technology, to ensure automatic identification process, as well as interpretation of B-lines in the given lung area of the examined patient. The article sums up the state of the art in ultrasound artefacts and AI applied in POCUS.

MRI reconstruction with enhanced self-similarity using graph convolutional network.

BACKGROUND: Recent Convolutional Neural Networks (CNNs) perform low-error reconstruction in fast Magnetic Resonance Imaging (MRI). Most of them convolve the image with kernels and successfully explore the local information. Nonetheless, the non-local image information, which is embedded among image patches relatively far from each other, may be lost due to the limitation of the receptive field of the convolution kernel. We aim to incorporate a graph to represent non-local information and improve the reconstructed images by using the Graph Convolutional Enhanced Self-Similarity (GCESS) network. METHODS: First, the image is reconstructed into the graph to extract the non-local self-similarity in the image. Second, GCESS uses spatial convolution and graph convolution to process the information in the image, so that local and non-local information can be effectively utilized. The network strengthens the non-local similarity between similar image patches while reconstructing images, making the reconstruction of structure more reliable. RESULTS: Experimental results on in vivo knee and brain data demonstrate that the proposed method achieves better artifact suppression and detail preservation than state-of-the-art methods, both visually and quantitatively. Under 1D Cartesian sampling with 4 × acceleration (AF = 4), the PSNR of knee data reached 34.19 dB, 1.05 dB higher than that of the compared methods; the SSIM achieved 0.8994, 2% higher than the compared methods. Similar results were obtained for the reconstructed images under other sampling templates as demonstrated in our experiment. CONCLUSIONS: The proposed method successfully constructs a hybrid graph convolution and spatial convolution network to reconstruct images. This method, through its training process, amplifies the non-local self-similarities, significantly benefiting the structural integrity of the reconstructed images. Experiments demonstrate that the proposed method outperforms the state-of-the-art reconstruction method in suppressing artifacts, as well as in preserving image details.

Aluminium Chloride instead of Ferric chloride for inducing superior sagittal sinus thrombosis to reduce ferromagnetic artifacts on MRI-imaging in experimental models.

Using ferric chloride (FeCl3) to induce experimental superior sagittal sinus (SSS) thrombosis might interfere with magnetic resonance imaging (MRI)-assisted visualization and evaluation of the thrombus, the brain parenchyma, and the quality of the occlusion. The aim of this study was to investigate whether aluminum chloride (AlCl3)-induced thrombosis of the SSS has comparable properties to those of FeCl3 without causing artifacts in MRI. SSS thrombosis was induced in 14 male Wistar rats by exposure of the SSS and subsequent topical application of a filter paper strip soaked in AlCl3 (n = 7) or FeCl3 (n = 7) over a period of 15 min. The animals with AlCl3-induced SSS thrombosis showed a constant and complete occlusion with in histological analysis large thrombi. Blood flow measurements indicated a significant reduction on the first and seventh postoperative day compared to preoperative measurements. MRI enabled visualization and subsequent evaluation of the thrombus and the surrounding parenchyma. In comparison, FeCl3-induced SSS thrombosis could not be evaluated by MRI due to artifacts caused by the paramagnetic properties and increased susceptibility of FeCl3. The occluded sinus and the surrounding area appeared hypointense. The quality of SSS occlusion by AlCl3 was comparable to that of FeCl3. AlCl3 therefore represents a significant alternative substance in experimental SSS thrombosis ideally suited for studies using MRI.

Three-dimensional assessment of image distortion induced by active cardiac implants in 3.0T CMR.

CMR at 3.0T in the presence of active cardiac implants remains a challenge due to susceptibility artifacts. Beyond a signal void that cancels image information, magnetic field inhomogeneities may cause distorted appearances of anatomical structures. Understanding influencing factors and the extent of distortion are a first step towards optimizing the image quality of CMR with active implants at 3.0T. All measurements were obtained at a clinical 3.0T scanner. An in-house designed phantom with a 3D cartesian grid of water filled spheres was used to analyze the distortion caused by four representative active cardiac devices (cardiac loop recorder, pacemaker, 2 ICDs). For imaging a gradient echo (3D-TFE) sequence and a turbo spin echo (2D-TSE) sequence were used. The work defines metrics to quantify the different features of distortion such as changes in size, location and signal intensity. It introduces a specialized segmentation technique based on a reaction-diffusion-equation. The distortion features are dependent on the amount of magnetic material in the active implants and showed a significant increase when measured with the 3D TFE compared to the 2D TSE. This work presents a quantitative approach for the evaluation of image distortion at 3.0T caused by active cardiac implants and serves as foundation for both further optimization of sequences and devices but also for planning of imaging procedures.

"Counting sheep PSG": EEGLAB-compatible open-source matlab software for signal processing, visualization, event marking and staging of polysomnographic data.

BACKGROUND: Progress in advancing sleep research employing polysomnography (PSG) has been negatively impacted by the limited availability of widely available, open-source sleep-specific analysis tools. NEW METHOD: Here, we introduce Counting Sheep PSG, an EEGLAB-compatible software for signal processing, visualization, event marking and manual sleep stage scoring of PSG data for MATLAB. RESULTS: Key features include: (1) signal processing tools including bad channel interpolation, down-sampling, re-referencing, filtering, independent component analysis, artifact subspace reconstruction, and power spectral analysis, (2) customizable display of polysomnographic data and hypnogram, (3) event marking mode including manual sleep stage scoring, (4) automatic event detections including movement artifact, sleep spindles, slow waves and eye movements, and (5) export of main descriptive sleep architecture statistics, event statistics and publication-ready hypnogram. COMPARISON WITH EXISTING METHODS: Counting Sheep PSG was built on the foundation created by sleepSMG (https://sleepsmg.sourceforge.net/). The scope and functionalities of the current software have made significant advancements in terms of EEGLAB integration/compatibility, preprocessing, artifact correction, event detection, functionality and ease of use. By comparison, commercial software can be costly and utilize proprietary data formats and algorithms, thereby restricting the ability to distribute and share data and analysis results. CONCLUSIONS: The field of sleep research remains shackled by an industry that resists standardization, prevents interoperability, builds-in planned obsolescence, maintains proprietary black-box data formats and analysis approaches. This presents a major challenge for the field of sleep research. The need for free, open-source software that can read open-format data is essential for scientific advancement to be made in the field.

Personalized Detection of Motion Artifacts for Telemonitoring Applications.

Among its main benefits, telemonitoring enables personalized management of chronic diseases by means of biomarkers extracted from signals. In these applications, a thorough quality assessment is required to ensure the reliability of the monitored parameters. Motion artifacts are a common problem in recordings with wearable devices. In this work, we propose a fully automated and personalized method to detect motion artifacts in multimodal recordings devoted to the monitoring of the Cardiac Time Intervals (CTIs). The detection of motion artifacts was carried out by using template matching with a personalized template. The method yielded a balanced accuracy of 86%. Moreover, it proved effective to decrease the variability of the estimated CTIs by at least 17%. Our preliminary results show that personalized detection of motion artifacts improves the robustness of the assessment CTIs and opens to the use in wearable systems.

Materials and Structural Designs toward Motion Artifact-Free Bioelectronics.

Bioelectronics encompassing electronic components and circuits for accessing human information play a vital role in real-time and continuous monitoring of biophysiological signals of electrophysiology, mechanical physiology, and electrochemical physiology. However, mechanical noise, particularly motion artifacts, poses a significant challenge in accurately detecting and analyzing target signals. While software-based "postprocessing" methods and signal filtering techniques have been widely employed, challenges such as signal distortion, major requirement of accurate models for classification, power consumption, and data delay inevitably persist. This review presents an overview of noise reduction strategies in bioelectronics, focusing on reducing motion artifacts and improving the signal-to-noise ratio through hardware-based approaches such as "preprocessing". One of the main stress-avoiding strategies is reducing elastic mechanical energies applied to bioelectronics to prevent stress-induced motion artifacts. Various approaches including strain-compliance, strain-resistance, and stress-damping techniques using unique materials and structures have been explored. Future research should optimize materials and structure designs, establish stable processes and measurement methods, and develop techniques for selectively separating and processing overlapping noises. Ultimately, these advancements will contribute to the development of more reliable and effective bioelectronics for healthcare monitoring and diagnostics.

Flexible Sensor for Real-Time Monitoring of Motion Artifacts in Magnetic Resonance Imaging.

In recent years, magnetic resonance imaging has been widely used in the medical field. During the scan, if the human body moves, then there will be motion artifacts on the scan image, which will interfere with the diagnosis and only be found after the end of the scan sequence, resulting in a waste of manpower and resources. However, there is a lack of technology that halts scanning once motion artifacts arise. Here, we designed a real-time monitoring sensor (RMS) to dynamically perceive the movement of the human body and to pause in time when the movement exceeds a certain amplitude. The sensor has an array structure that can accurately sense the position of the human body in real time. The selection of the RMS ensures that there is no additional interference with the scanning results. Based on this design, the RMS can achieve the monitoring function of motion artifact generation.

Ultraconformal Skin-Interfaced Sensing Platform for Motion Artifact-Free Monitoring.

Capable of directly capturing various physiological signals from human skin, skin-interfaced bioelectronics has emerged as a promising option for human health monitoring. However, the accuracy and reliability of the measured signals can be greatly affected by body movements or skin deformations (e.g., stretching, wrinkling, and compression). This study presents an ultraconformal, motion artifact-free, and multifunctional skin bioelectronic sensing platform fabricated by a simple and user-friendly laser patterning approach for sensing high-quality human physiological data. The highly conductive membrane based on the room-temperature coalesced Ag/Cu@Cu core-shell nanoparticles in a mixed solution of polymers can partially dissolve and locally deform in the presence of water to form conformal contact with the skin. The resulting sensors to capture improved electrophysiological signals upon various skin deformations and other biophysical signals provide an effective means to monitor health conditions and create human-machine interfaces. The highly conductive and stretchable membrane can also be used as interconnects to connect commercial off-the-shelf chips to allow extended functionalities, and the proof-of-concept demonstration is highlighted in an integrated pulse oximeter. The easy-to-remove feature of the resulting device with water further allows the device to be applied on delicate skin, such as the infant and elderly.

Pro: "Is Spread Through Air Spaces an In Vivo Phenomenon or an Inducible Artifact?"

In this PRO-CON debate, you will read very different perspectives about a simple question regarding an observation under the microscope: What is the significance of tumor cells in the air spaces of the lung parenchyma beyond the tumor edge of a resected lung cancer? An important underlying question is whether this entire PRO-CON debate is a mere academic exercise or whether spread through air spaces (STAS), as currently defined, describes a clinically useful phenomenon. The journey of STAS began with a complete paradigm shift to reverse the thinking that all air space tumor cells beyond the edge of lung cancers are an artifact. This led to a new concept where STAS could be separated from artifacts with a definition that has proven to be clinically useful. As with any major change in thinking, it is understandable that there would be some disagreement with this paradigm shift. Nevertheless, after a decade since it was described, many pathologists and clinicians around the world have found STAS to provide important information about the behavior of lung cancer. Numerous PRO-STAS articles supporting the usefulness of STAS have been published with clinical data on many thousands of patients from numerous institutions all over the world. In contrast, for the CON-STAS articles, widespread international representation and data are limited. It is now difficult to ignore the numerous reports and is reasonable to consider how to use the presence of STAS in clinical decisions. Hopefully, this PRO-CON debate will further stimulate clinical and scientific investigations aimed at a better understanding of STAS.

Generalized 3D registration algorithm for enhancing retinal optical coherence tomography images.

Significance: Optical coherence tomography (OCT) has emerged as the standard of care for diagnosing and monitoring the treatment of various ocular disorders due to its noninvasive nature and in vivo volumetric acquisition capability. Despite its widespread applications in ophthalmology, motion artifacts remain a challenge in OCT imaging, adversely impacting image quality. While several multivolume registration algorithms have been developed to address this issue, they are often designed to cater to one specific OCT system or acquisition protocol. Aim: We aim to generate an OCT volume free of motion artifacts using a system-agnostic registration algorithm that is independent of system specifications or protocol. Approach: We developed a B-scan registration algorithm that removes motion and corrects for both translational eye movements and rotational angle differences between volumes. Tests were carried out on various datasets obtained from two different types of custom-built OCT systems and one commercially available system to determine the reliability of the proposed algorithm. Additionally, different system specifications were used, with variations in axial resolution, lateral resolution, signal-to-noise ratio, and real-time motion tracking. The accuracy of this method has further been evaluated through mean squared error (MSE) and multiscale structural similarity index measure (MS-SSIM). Results: The results demonstrate improvements in the overall contrast of the images, facilitating detailed visualization of retinal vasculatures in both superficial and deep vasculature plexus. Finer features of the inner and outer retina, such as photoreceptors and other pathology-specific features, are discernible after multivolume registration and averaging. Quantitative analyses affirm that increasing the number of averaged registered volumes will decrease MSE and increase MS-SSIM as compared to the reference volume. Conclusions: The multivolume registered data obtained from this algorithm offers significantly improved visualization of the retinal microvascular network as well as retinal morphological features. Furthermore, we have validated that the versatility of our methodology extends beyond specific OCT modalities, thereby enhancing the clinical utility of OCT for the diagnosis and monitoring of ocular pathologies.

Spinal magnetic resonance imaging artifacts in lumboperitoneal shunt surgery using adjustable valve implantation on the paravertebral spinal muscles.

BACKGROUND: Adjustable shunt valves that have been developed for managing hydrocephalus rely on intrinsically magnetic components ; thus, artifacts with these valves on magnetic resonance imaging (MRI) are inevitable. No studies on valve-induced artifacts in lumboperitoneal shunt (LPS) surgery have been published. Therefore, this study aimed to evaluate valve-induced artifacts in LPS. METHODS: We retrospectively reviewed all MRIs obtained between January 2023 and June 2023 in patients with an implanted Codman CERTAS Plus adjustable shunt valve (Integra Life Sciences, Princeton, New Jersey, USA). The valve was placed <1 cm subcutaneously on the paravertebral spinal muscle of the back, with its long axis perpendicular to the body axis. The scans were performed using a Toshiba Medical Systems 1.5 Tesla scanner. The in-plane artifact sizes were assessed as the maximum distance of the artifact from the expected region of the back. RESULTS: All spinal structures or spinal cords can be recognized, even with valve-induced artifacts. The median maximum valve-induced artifact distance on T1-weighted axial imaging was 25.63 mm (mean, 25.98 mm ; range, 22.24-30.94 mm). The median maximum valve-induced artifact distance on T2-weighted axial imaging was 25.56 mm (mean, 26.27 mm ; range, 21.83-29.53 mm). CONCLUSION: LPS surgery with adjustable valve implantation on paravertebral muscles did not cause valve-induced artifacts in the spine and spinal cord. We considered that LPS could simplify the postoperative care of these patients. J. Med. Invest. 71 : 154-157, February, 2024.

Improved filtering methods to suppress cardiovascular contamination in electrical impedance tomography recordings.

Objective.Electrical impedance tomography (EIT) produces clinical useful visualization of the distribution of ventilation inside the lungs. The accuracy of EIT-derived parameters can be compromised by the cardiovascular signal. Removal of these artefacts is challenging due to spectral overlapping of the ventilatory and cardiovascular signal components and their time-varying frequencies. We designed and evaluated advanced filtering techniques and hypothesized that these would outperform traditional low-pass filters.Approach.Three filter techniques were developed and compared against traditional low-pass filtering: multiple digital notch filtering (MDN), empirical mode decomposition (EMD) and the maximal overlap discrete wavelet transform (MODWT). The performance of the filtering techniques was evaluated (1) in the time domain (2) in the frequency domain (3) by visual inspection. We evaluated the performance using simulated contaminated EIT data and data from 15 adult and neonatal intensive care unit patients.Main result.Each filter technique exhibited varying degrees of effectiveness and limitations. Quality measures in the time domain showed the best performance for MDN filtering. The signal to noise ratio was best for DLP, but at the cost of a high relative and removal error. MDN outbalanced the performance resulting in a good SNR with a low relative and removal error. MDN, EMD and MODWT performed similar in the frequency domain and were successful in removing the high frequency components of the data.Significance.Advanced filtering techniques have benefits compared to traditional filters but are not always better. MDN filtering outperformed EMD and MODWT regarding quality measures in the time domain. This study emphasizes the need for careful consideration when choosing a filtering approach, depending on the dataset and the clinical/research question.

Towards a unified approach for unsupervised brain MRI Motion Artefact Detection with few shot Anomaly Detection.

Automated Motion Artefact Detection (MAD) in Magnetic Resonance Imaging (MRI) is a field of study that aims to automatically flag motion artefacts in order to prevent the requirement for a repeat scan. In this paper, we identify and tackle the three current challenges in the field of automated MAD; (1) reliance on fully-supervised training, meaning they require specific examples of Motion Artefacts (MA), (2) inconsistent use of benchmark datasets across different works and use of private datasets for testing and training of newly proposed MAD techniques and (3) a lack of sufficiently large datasets for MRI MAD. To address these challenges, we demonstrate how MAs can be identified by formulating the problem as an unsupervised Anomaly Detection (AD) task. We compare the performance of three State-of-the-Art AD algorithms DeepSVDD, Interpolated Gaussian Descriptor and FewSOME on two open-source Brain MRI datasets on the task of MAD and MA severity classification, with FewSOME achieving a MAD AUC >90% on both datasets and a Spearman Rank Correlation Coefficient of 0.8 on the task of MA severity classification. These models are trained in the few shot setting, meaning large Brain MRI datasets are not required to build robust MAD algorithms. This work also sets a standard protocol for testing MAD algorithms on open-source benchmark datasets. In addition to addressing these challenges, we demonstrate how our proposed 'anomaly-aware' scoring function improves FewSOME's MAD performance in the setting where one and two shots of the anomalous class are available for training. Code available at https://github.com/niamhbelton/Unsupervised-Brain-MRI-Motion-Artefact-Detection/.

Unsupervised low-dose CT denoising using bidirectional contrastive network.

BACKGROUND AND OBJECTIVE: Low-dose computed tomography (LDCT) scans significantly reduce radiation exposure, but introduce higher levels of noise and artifacts that compromise image quality and diagnostic accuracy. Supervised learning methods have proven effective in denoising LDCT images, but are hampered by the need for large, paired datasets, which pose significant challenges in data acquisition. This study aims to develop a robust unsupervised LDCT denoising method that overcomes the reliance on paired LDCT and normal-dose CT (NDCT) samples, paving the way for more accessible and practical denoising techniques. METHODS: We propose a novel unsupervised network model, Bidirectional Contrastive Unsupervised Denoising (BCUD), for LDCT denoising. This model innovatively combines a bidirectional network structure with contrastive learning theory to map the precise mutual correspondence between the noisy LDCT image domain and the clean NDCT image domain. Specifically, we employ dual encoders and discriminators for domain-specific data generation, and use unique projection heads for each domain to adaptively learn customized embedded representations. We then align corresponding features across domains within the learned embedding spaces to achieve effective noise reduction. This approach fundamentally improves the model's ability to match features in latent space, thereby improving noise reduction while preserving fine image detail. RESULTS: Through extensive experimental validation on the AAPM-Mayo public dataset and real-world clinical datasets, the proposed BCUD method demonstrated superior performance. It achieved a peak signal-to-noise ratio (PSNR) of 31.387 dB, a structural similarity index measure (SSIM) of 0.886, an information fidelity criterion (IFC) of 2.305, and a visual information fidelity (VIF) of 0.373. Notably, subjective evaluation by radiologists resulted in a mean score of 4.23, highlighting its advantages over existing methods in terms of clinical applicability. CONCLUSIONS: This paper presents an innovative unsupervised LDCT denoising method using a bidirectional contrastive network, which greatly improves clinical applicability by eliminating the need for perfectly matched image pairs. The method sets a new benchmark in unsupervised LDCT image denoising, excelling in noise reduction and preservation of fine structural details.