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The purpose of this paper is to present the first detailed three-dimensional (3D) print from micro-computed tomography data of the skeleton of an ancient Egyptian falcon mummy.

Radiographic analysis of an ancient Egyptian falcon mummy housed at Iziko Museums of South Africa was performed using non-destructive x-ray micro-computed tomography. A 1:1 physical replica of its skeleton was printed in a polymer material (polyamide) using 3D printing technology.

The combination of high-resolution computed tomography scanning and rapid prototyping allowed us to create an accurate 1:1 model of a biological object hidden by wrappings. This model can be used to study skeletal features and morphology and also enhance exhibitions hosted within the museum.

This is the first replica of its kind made of an ancient Egyptian falcon mummy skeleton. The combination of computed tomography scanning and 3D printing has the potential to facilitate scientific research and stimulate public interest in Egyptology.

This study aims to generate different three-dimensional (3D) foam models using computer tomography (CT) scan and solid continuum techniques. The generated foam models were used to study deformation mechanism and the elastic-plastic behaviour with the existing experimental foam behaviour.

CT scan model was generated by combing 2D images of foam in MIMICS software. Afterwards, it was imported in ABAQUS/CAE software. However, solid continuum model was generated in ABAQUS/CAE software by using crushable foam properties. Then, the generated foam models were sets boundary conditions for a compression test.

CT scans capture the actual morphology of foam sample which may directly an image based finite element foam model. The sectional views of both the models were used to observe deformation mechanism on compression. The real compressive behaviour of foam was visualised in CT-Scan foam model. It was observed that CT-scan model was the more accurate modelling method than crushable foam model.

The internal structure of foam is very complex and difficult to analyse. Therefore, CT-scanning may be the accurate method for capturing the macro-level detailing of foam structure. A CT-scan foam model can be used for multiple times for mechanical analysis using a simulation software, which may reduce the manufacturing and the experimental cost and time.

The purpose of this paper is to understand the relationship between defect properties and the tool path used for generating additively manufactured parts. The correlation between processing strategy and porosity architecture is one of the key aspects for a precise understanding of defect formation and possibilities for defect reduction.

The authors present a new combined geometry, processing path and porosity analysis procedure based on the use of x-ray computed micro tomography image data and numerical control programming code. The procedure allows for a covisualisation of the track of the respective processing head with the three-dimensional microstructure data.

The presented method yields statistical results about defect distribution and morphologies introduced by the respective process characteristics in parts. The functionality of the proposed procedure is demonstrated on an aluminum (AlSi10Mg) and a polylactide test sample to show the additional insight found for both additive manufacturing processes and the resulting microstructural properties.

The novelty of this paper is the analysis of the porosity with respect to the underlying additive process zone and the sample geometry.

Computed tomography (CT) scan can provide valuable information in the diagnosis of lung diseases. To detect the location of the cancerous lung nodules, this work uses novel deep learning methods. The majority of the early investigations used CT, magnetic resonance and mammography imaging. Using appropriate procedures, the professional doctor in this sector analyses these images to discover and diagnose the various degrees of lung cancer. All of the methods used to discover and detect cancer illnesses are time-consuming, expensive and stressful for the patients. To address all of these issues, appropriate deep learning approaches for analyzing these medical images, which included CT scan images, were utilized.

Radiologists currently employ chest CT scans to detect lung cancer at an early stage. In certain situations, radiologists' perception plays a critical role in identifying lung melanoma which is incorrectly detected. Deep learning is a new, capable and influential approach for predicting medical images. In this paper, the authors employed deep transfer learning algorithms for intelligent classification of lung nodules. Convolutional neural networks (VGG16, VGG19, MobileNet and DenseNet169) are used to constrain the input and output layers of a chest CT scan image dataset.

The collection includes normal chest CT scan pictures as well as images from two kinds of lung cancer, squamous and adenocarcinoma impacted chest CT scan images. According to the confusion matrix results, the VGG16 transfer learning technique has the highest accuracy in lung cancer classification with 91.28% accuracy, followed by VGG19 with 89.39%, MobileNet with 85.60% and DenseNet169 with 83.71% accuracy, which is analyzed using Google Collaborator.

The proposed approach using VGG16 maximizes the classification accuracy when compared to VGG19, MobileNet and DenseNet169. The results are validated by computing the confusion matrix for each network type.

In medical applications, it is crucial to evaluate the geometric accuracy of rapid prototyping (RP) models. Current research on evaluating geometric accuracy has focused on identifying two or more specific anatomical landmarks on the original structure and the RP model, and comparing their corresponding linear distances. Such kind of accuracy metrics is ambiguous and may induce misrepresentations of the actual errors. The purpose of this paper is to propose an alternative method and metrics to measure the accuracy of RP models.

The authors propose an accuracy metric composed of two different approaches: a global accuracy evaluation using volumetric intersection indexes calculated over segmented Computed Tomography scans of the original object and the RP model. Second, a local error metric that is computed from the surfaces of the original object and the RP model. This local error is rendered in a 3D surface using a color code, that allow differentiating regions where the model is overestimated, underestimated, or correctly estimated. Global and local error measurements are performed after rigid body registration, segmentation and triangulation.

The results show that the method can be applied to different objects without any modification, and provide simple, meaningful and precise quantitative indexes to measure the geometric accuracy of RP models.

The paper presents a new approach to characterize the geometric errors in RP models using global indexes and a local surface distribution of the errors. It requires minimum human intervention and it can be applied without any modification to any kind of object.

The purpose of this study/paper To focus on finding COVID-19 with the help of DarkCovidNet architecture on patient images.

We used machine learning techniques with convolutional neural network.

Detecting COVID-19 symptoms from patient CT scan images.

This paper contains a new architecture for detecting COVID-19 symptoms from patient computed tomography scan images.

COVID-19 would have a far-reaching impact on the international health-care industry and the patients. For COVID-19, there is a need for unique screening tests to reliably and rapidly determine who is infected. Medical COVID images protection is critical when data pertaining to computer images are being transmitted through public networks in health information systems.

Medical images such as computed tomography (CT) play key role in the diagnosis of COVID-19 patients. Neural networks-based methods are designed to detect COVID patients using chest CT scan images. And CT images are transmitted securely in health information systems.

The authors hereby examine neural networks-based COVID diagnosis methods using chest CT scan images and secure transmission of CT images for health information systems. For screening patients infected with COVID-19, a new approach using convolutional neural networks is proposed, and its output is simulated.

The required patient’s chest CT scan images have been taken from online databases such as GitHub. The experiments show that neural networks-based methods are effective in the diagnosis of COVID-19 patients using chest CT scan images.

The aim of this article is to show how Taguchi methods can be applied to health care to improve the quality of medical images. Quality is often integrated with the performance and parameters of the design of medical applications. Many imaging methods can be designed by setting the correct combination of parameters and estimating the contribution of individual quality influencing factors by means of incorporating parameter design and orthogonal arrays. The performance of any imaging equipment can be measured by signal‐to‐noise ratio. This inherent index can give a sense of how close the performance is to the ideal.

Data were collected from a database of 82 diagnostic thoracic computed tomography (CT) scans. Signal‐to‐noise ratios (S/N) were calculated.

Given the S/N's, the best CT level was found to be level 4.

To reduce bias resulting from the observer's readings, robust equipments should be designed incorporating Taguchi's experimental design. Further work is needed to establish imaging protocols and new hardware design.

Orange peel formation remains to be understood clearly because it is difficult to directly observe a laser-sintered process in a partcake. Therefore, this study aims to provide insight into the orange peel formation mechanism through the nondestructive observation of laser-sintered specimens and their surrounding powders.

This study observed polyamide 12 powder in the vicinity of a laser-sintered specimen via X-ray computed tomography (CT) scanning. The specimen for nondestructive observation was 3D modeled in a hollow box using 3D CAD software. The boxes built using a laser-sintering system contained unsintered surrounding powder and sintered specimens. The box contents were preserved even after the boxes were removed from the partcake. After X-ray CT scanning, the authors broke the boxes and evaluated the unevenness formed on the specimen surface (i.e. the orange peel evaluation).

Voids (not those in sintered parts) generated in the powder in the vicinity of the specimen triggered the orange peel formation. Voids were less likely to form in the build with a 178.5° powder bed than in the build with a 173.5° powder bed. Similarly, the increment in laser energy density effectively suppressed void formation, although there was a tradeoff with overmelting. Thin-walled parts avoided void growth and made the orange peel less noticeable.

To the best of the authors’ knowledge, this study is the first to observe and understand the relationship between voids generated in the powder in the vicinity of sintered parts and orange peel formation.

Post-traumatic zygomatic osteotomy, fracture reduction, and orbital floor reconstruction pose many challenges for achieving a predictable, accurate, safe, and aesthetically pleasing result. This paper aims to describe the successful application of computer-aided design (CAD) and additive manufacturing (AM) to every stage of the process – from planning to surgery.

A multi-disciplinary team was used – comprising surgeons, prosthetists, technicians, and designers. The patient’s computed tomography scan data were segmented for bone and exported to a CAD software package. Medical models were fabricated using AM; for diagnosis, patient communication, and device verification. The surgical approach was modelled in the virtual environment and a custom surgical cutting guide, custom bone-repositioning guide, custom zygomatic implant, and custom orbital floor implant were each designed, prototyped, iterated, and validated using polymer AM prior to final fabrication using metal AM.

Post-operative clinical outcomes were as planned. The patient’s facial symmetry was improved, and their inability to fully close their eyelid was corrected. The length of the operation was reduced relative to the surgical team’s previous experiences. Post-operative scan analysis indicated a maximum deviation from the planned location for the largest piece of mobilised bone of 3.65 mm. As a result, the orbital floor implant which was fixed to this bone demonstrated a maximum deviation of 4.44 mm from the plan.

This represents the first application of CAD and AM to every stage of the process for this procedure – from diagnosis, to planning, and to surgery.