Search Results (237)

Integrating diverse image datasets acquired from different satellites is challenging. Converting images from one sensor to another, like from WorldView-3 (WV) to SuperDove (SD), involves both changing image channel wavelengths and per-band intensity scales because different sensors can acquire imagery of the same scene at different wavelengths and intensities. A parametrized convolutional network approach has shown promise converting across sensor domains, but it introduces distortion artefacts when objects are in motion. The cause of spectral distortion is due to temporal delays between sequential multispectral band acquisitions. This can result in spuriously blurred images of moving objects in the converted imagery, and consequently misaligned moving object locations across image bands. To resolve this, we propose an enhanced model, the Physics-Informed Gaussian-Enforced Separated-Band Convolutional Conversion Network (PIGESBCCN), which better accounts for known spatial, spectral, and temporal correlations between bands via band reordering and branched model architecture. Full article

MRI perfusion is used to diagnose and monitor neurological conditions such as brain tumors, stroke, dementia, and traumatic brain injury. Dynamic Susceptibility Contrast (DSC) is the most widely available quantitative MRI technique for perfusion imaging. Even in its most basic implementation, DSC MRI provides critical hemodynamic metrics like cerebral blood flow (CBF), blood volume (CBV), mean transit time (MTT), and time between the peak of arterial input and residue function (Tmax), through the dynamic tracking of a gadolinium-based contrast agent. Notwithstanding its high clinical importance and widespread use, the reproducibility and diagnostic reliability are impeded by a lack of standardized pre-processing protocols and quality controls. A comprehensive literature review and the authors’ aggregated experience identified common DSC MRI artefacts and corresponding pre-processing methods. Pre-processing methods to correct for artefacts were evaluated for their practical applicability and validation status. A consensus on the pre-processing was established by a multidisciplinary team of experts. Acquisition-related artefacts include geometric distortions, slice timing misalignment, and physiological noise. Intrinsic artefacts include motion, B₁ inhomogeneities, Gibbs ringing, and noise. Motion can be mitigated using rigid-body alignment, but methods for addressing B₁ inhomogeneities, Gibbs ringing, and noise remain underexplored for DSC MRI. Pre-processing of DSC MRI is critical for reliable diagnostics and research. While robust methods exist for correcting geometric distortions, motion, and slice timing issues, further validation is needed for methods addressing B₁ inhomogeneities, Gibbs ringing, and noise. Implementing adequate mitigation methods for these artefacts could enhance reproducibility and diagnostic accuracy, supporting the growing reliance on DSC MRI in neurological imaging. Finally, we emphasize the crucial importance of pre-scan quality assurance with phantom scans. Full article

Since the COVID-19 pandemic, the utilization of transthoracic ultrasonography (TTU) in the evaluation of pulmonary field artefacts has become standard practice among clinicians. However, there is a considerable lack of knowledge regarding the assessment of diaphragm mobility in the context of various lung diseases. Although numerous conditions are known to affect diaphragm mobility, including neurological, cardiovascular, and infectious diseases, it appears that pulmonary diseases may also limit the mobility of this major respiratory muscle. Despite the evidence of diaphragm mobility disorders in patients diagnosed with lung cancer, there is a discrepancy in the literature regarding the function of the diaphragm in individuals with chronic obstructive pulmonary disease (COPD). A shared aetiological factor frequently results in the co-occurrence of the aforementioned diseases. It is, however, possible to detect patients whose obstructive airway disease is caused only by the compression of infiltrative and nodal lesions rather than COPD. Bilateral TTU of diaphragmatic mobility in correlation with other available pulmonary function tests and radiological imaging may prove to be a valuable approach to isolating lung cancer patients with COPD overdiagnosis. Conversely, the overdiagnosis of COPD has been implicated in the potentially unnecessary and harmful use of inhaled medications with their adverse effects (e.g., cardiac arrhythmias, limb tremor, cough, and pneumonia), the failure to decrease obstruction in cases of other lung disorders, and the potential to contribute to the delayed diagnosis of the underlying condition responsible for the respiratory symptoms. This paper aims to provide a comprehensive overview of the utilization of ultrasound in the evaluation of diaphragm movement impairments for the detection of obstructions while also delineating the underlying limitations of this technique. Moreover, we propose a diagnostic algorithm for the purpose of excluding unilateral obstruction resulting from infiltrative neoplastic masses based on the ultrasound assessment of diaphragmatic mobility. Full article

The introduction of technology in different sectors to optimise efficiency is increasing rapidly. As a result of the opportunities that artificial intelligence presents to different sectors by optimally performing tasks with less error compared to humans or traditional models, the use of AI in artefact detection is being investigated. This research paper thus presents a comparative model efficiency analysis based on dissimilar algorithms, namely CNN, VGG16, Inception_V3, and ResNet_50. The model developed was based on images that were obtained from a Toshiba CT scanner for two types of datasets (88 image datasets) and 170 image datasets, both comprising metal and ring artefacts. Furthermore, the results demonstrate higher data losses in the data transfer learning due to data recycling, suggesting that the model is prone to image feature losses when the model threshold is set at 75%. Additionally, two data transfer models were evaluated against “our model”. The results demonstrate that VGG16 performed better in terms of data accuracy than both the testing and training models, while the Resnet_50 algorithm performed poorly in terms of the loss encountered compared to the other three algorithms. Full article

Echocardiographic values can vary between dog breeds, making breed-specific reference intervals (RIs) preferable. Two-dimensional speckle tracking echocardiography (2-D STE) is an advanced imaging technique that enables the measurement of myocardial deformation parameters, contributing to the assessment of systolic function. The objective was to determine breed-specific RIs for 2-D, M-mode, and Doppler-derived echocardiographic parameters for Dutch Sheepdogs, and to obtain 2-D STE-derived strain and strain rate values in this breed. Apparently healthy, purebred Dutch Sheepdogs (1–7 years) were recruited. Each dog underwent a physical examination and transthoracic echocardiography. Conventional 2-D, M-mode, and Doppler measurements were obtained; strain analysis was performed with 2-D STE software. RIs were established for conventional echocardiographic parameters; clinically relevant parameters were compared with commonly used RIs. The effects of gender, age, body weight (BW) and heart rate were tested. Sixty dogs were included. Panting and/or tachycardia were observed in 24 dogs, which affected the quality of the analysis to varying degrees (e.g., out-of-sector movement, lung artefacts). The selected parameters for left ventricular (LV) and atrial dimension showed good agreement with published RIs. BW was an independent variable influencing LV dimensions. This study provides RIs for conventional echocardiographic measurements and reports 2-D STE-derived strain and strain rate values obtained in Dutch Sheepdogs. The selected parameters of LV and left atrial dimension showed good agreement with commonly used RIs. Anxious behavior could represent a breed peculiarity to take into account when performing echocardiography, as it can affect image quality. Full article

Ground-Penetrating Radar (GPR) systems with ultra-wideband (UWB) antennas introduce the benefits of both high and low frequencies. Higher frequencies offer finer spatial resolution, enabling the detection of small-scale features and details, while lower frequencies improve depth penetration by minimising signal attenuation, allowing the system to explore deeper subsurface layers. This combination optimises the performance of GPR systems by balancing the need for detailed imaging with the requirement for deeper penetration. This work presents the design of a wideband inverted U-shaped patch antenna with a wide rectangular slot centred at a frequency of 1.5 GHz. The antenna is fed through a microstrip feed line and employs a partial ground plane. Through simulation, the antenna is optimised by varying the patch dimensions and slot size. Further modifications to the partial ground plane improve the UWB and gain characteristics of the antenna. The optimised antenna is fabricated using a double-sided copper-clad FR4 substrate with a thickness of 1.6 mm and characterised using a Vector Network Analyser (VNA), with final dimensions of 200 mm × 300 mm. The experimental results demonstrate a return loss below −10 dB across the operational band from 1.068 GHz to 4 GHz and a maximum gain of 7.29 dB at 4 GHz. In addition to other bands, the antenna exhibits a return loss consistently below −20 dB in the frequency range of 1.367 GHz to 1.675 GHz. These results confirm the antenna’s UWB performance and its suitability for GPR applications in utility mapping, landmine and artefact detection, and identifying architectural defects. Full article

Archaeologists and conservation scientists join interdisciplinary projects aiming at the in-depth analysis of artefacts and the resolution of new archaeological issues, overcoming the common limits of mesoscopic observation. The aim of this research is to perform multidisciplinary research, adapting imaging techniques (RTI imaging and 3D photogrammetry) and Raman spectroscopy from their conventional field of application to study and valorise neolithic archaeological findings from Piacentine sites (Emilia-Romagna, Italy). RTI images enable the detection of a comprehensive framework of anthropic and natural traces on the object surfaces to support the hypothesis of the intended usage of artefacts. Combining qualitative and quantitative Raman spectra analysis, the specific lithological characterisation of each fragment is conducted, thereby the understanding of their probable geographic provenance is enhanced. This contributes to the identification of the External Ligurian Units as a possible primary supply area, along with the already known outcrops in the Mont Viso Massif and Voltri Group. Their potential as a powerful instrument for conservation and valorisation has been revealed by 3D models. In fact, they may enrich museum exhibits, enhancing visitors’ experience through interactive engagement and guarantee the examination of artefacts by experts across the globe through online sharing, without the need for transportation and excessive manipulation. Full article

Understanding the deterioration processes in wooden artefacts is essential for accurately assessing their conservation status and developing effective preservation strategies. Advanced imaging techniques are currently being explored to study the impact of chemical changes on the structural and mechanical properties of wood. Nonlinear optical modalities, including second harmonic generation (SHG) and two-photon excited fluorescence (TPEF), combined with fluorescence lifetime imaging microscopy (FLIM), offer a promising non-destructive diagnostic method for evaluating lignocellulose-based materials. In this study, we employed a nonlinear multimodal approach to examine the effects of artificially induced delignification on samples of Norway spruce (Picea abies) and European beech (Fagus sylvatica) subjected to increasing treatment durations. The integration of SHG/TPEF imaging and multi-component fluorescence lifetime analysis enabled the detection of localized variations in nonlinear signals and τ-phase of key biopolymers within wood cell walls. This methodology provides a powerful tool for early detection of wood deterioration, facilitating proactive conservation efforts of wooden artefacts. Full article

Microscopic image segmentation (MIS) is a fundamental task in medical imaging and biological research, essential for precise analysis of cellular structures and tissues. Despite its importance, the segmentation process encounters significant challenges, including variability in imaging conditions, complex biological structures, and artefacts (e.g., noise), which can compromise the accuracy of traditional methods. The emergence of deep learning (DL) has catalyzed substantial advancements in addressing these issues. This systematic literature review (SLR) provides a comprehensive overview of state-of-the-art DL methods developed over the past six years for the segmentation of microscopic images. We critically analyze key contributions, emphasizing how these methods specifically tackle challenges in cell, nucleus, and tissue segmentation. Additionally, we evaluate the datasets and performance metrics employed in these studies. By synthesizing current advancements and identifying gaps in existing approaches, this review not only highlights the transformative potential of DL in enhancing diagnostic accuracy and research efficiency but also suggests directions for future research. The findings of this study have significant implications for improving methodologies in medical and biological applications, ultimately fostering better patient outcomes and advancing scientific understanding. Full article

In the evolving landscape of computer vision, the integration of machine learning algorithms with cutting-edge hardware platforms is increasingly pivotal, especially in the context of disruptive healthcare systems. This study introduces an optimized implementation of a Convolutional Neural Network (CNN) on the Basys3 FPGA, designed specifically for accelerating the classification of cytotoxicity in human kidney cells. Addressing the challenges posed by constrained dataset sizes, compute-intensive AI algorithms, and hardware limitations, the approach presented in this paper leverages efficient image augmentation and pre-processing techniques to enhance both prediction accuracy and the training efficiency. The CNN, quantized to 8-bit precision and tailored for the FPGA’s resource constraints, significantly accelerates training by a factor of three while consuming only 1.33% of the power compared to a traditional software-based CNN running on an NVIDIA K80 GPU. The network architecture, composed of seven layers with excessive hyperparameters, processes downscale grayscale images, achieving notable gains in speed and energy efficiency. A cornerstone of our methodology is the emphasis on parallel processing, data type optimization, and reduced logic space usage through 8-bit integer operations. We conducted extensive image pre-processing, including histogram equalization and artefact removal, to maximize feature extraction from the augmented dataset. Achieving an accuracy of approximately 91% on unseen images, this FPGA-implemented CNN demonstrates the potential for rapid, low-power medical diagnostics within a broader IoT ecosystem where data could be assessed online. This work underscores the feasibility of deploying resource-efficient AI models in environments where traditional high-performance computing resources are unavailable, typically in healthcare settings, paving the way for and contributing to advanced computer vision techniques in embedded systems. Full article

Image-guided treatment adaptation is a game changer in oncological particle therapy (PT), especially for younger patients. The purpose of this study is to present a cycle generative adversarial network (CycleGAN)-based method for synthetic computed tomography (sCT) generation from cone beam CT (CBCT) towards adaptive PT (APT) of paediatric patients. Firstly, 44 CBCTs of 15 young pelvic patients were pre-processed to reduce ring artefacts and rigidly registered on same-day CT scans (i.e., verification CT scans, vCT scans) and then inputted to the CycleGAN network (employing either Res-Net and U-Net generators) to synthesise sCT. In particular, 36 and 8 volumes were used for training and testing, respectively. Image quality was evaluated qualitatively and quantitatively using the structural similarity index metric (SSIM) and the peak signal-to-noise ratio (PSNR) between registered CBCT (rCBCT) and vCT and between sCT and vCT to evaluate the improvements brought by CycleGAN. Despite limitations due to the sub-optimal input image quality and the small field of view (FOV), the quality of sCT was found to be overall satisfactory from a quantitative and qualitative perspective. Our findings indicate that CycleGAN is promising to produce sCT scans with acceptable CT-like image texture in paediatric settings, even when CBCT with narrow fields of view (FOV) are employed. Full article

It is difficult to achieve high-precision color reproduction using traditional color reproduction methods when the angle is changed, and, for large-sized artefacts, it is also significantly difficult to collect a large amount of data and reproduce the colors. In this paper, we use three Bidirectional Reflectance Distribution Function (BRDF) modeling methods based on spectral imaging techniques, namely, the five-parameter model, the Cook–Torrance model and the segmented linear interpolation model. We investigated the color reproduction of color chips with matte surfaces and Chinese paintings with rough surfaces under unknown illumination angles. Experiments have shown that all three models can effectively perform image reconstruction under small illumination angle intervals. The segmented linear interpolation model exhibits a higher stability and accuracy in color reconstruction under small and large illumination angle intervals; it can not only reconstruct color chips and Chinese painting images under any illumination angle, but also achieve high-quality image color reconstruction standards in terms of objective data and intuitive perception. The best test model (segmented linear interpolation) performs well in reconstruction, reconstructing Chinese paintings at 65° and 125° with an illumination angle interval of 10°. The average RMSE of the selected reference color blocks is 0.0450 and 0.0589, the average CIEDE2000 color difference is 1.07 and 1.50, and the SSIM values are 0.9227 and 0.9736, respectively. This research can provide a theoretical basis and methodological support for accurate color reproduction as well as the large-sized scientific prediction of artifacts at any angle, and has potential applications in cultural relic protection, art reproduction, and other fields. Full article

The ancient Roman town of Turris Libisonis was located on the northern coast of Sardinia and was known in the past as an important naval port. Located in the Gulf of Asinara, it was a Roman colony from the 1st century BCE and became one of the richest towns on the island. Among the archaeological finds in the area, the cippus exhibited in the Antiquarium Turritano is of great interest for its well-preserved traces of polychromy. The artefact dates back to the early Imperial Age and could have had a funerary or votive function. The artefact was first examined using a portable and non-invasive protocol involving multi-band imaging (MBI), portable X-ray fluorescence (p-XRF), portable FT-IR in external reflectance mode (ER FT-IR) and Raman spectroscopy. After this initial examination, a few microfragments were collected and investigated by optical microscopy (OM), X-ray powder diffraction (XRPD), Fourier-transform infrared spectroscopy in ATR mode (ATR FT-IR) and micro-ATR mode (μATR FT-IR) and Scanning Electron Microscopy/Energy Dispersive Spectroscopy (SEM-EDS) to improve our knowledge and characterize the materials and to determine their provenience. The results contribute to a better understanding of the provenance of materials and shed light on pigments on stone and their use outside the Italian peninsula and, in particular, Roman Sardinia. Full article

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

Additive manufacturing (AM) and topology optimization (TO) emerge as vital processes in modern industries, with broad adoption driven by reduced expenses and the desire for lightweight and complex designs. However, iterative topology optimization can be inefficient and time-consuming for individual products with a large set of parameters. To address this shortcoming, machine learning (ML), primarily neural networks, is considered a viable tool to enhance topology optimization and streamline AM processes. In this work, a machine learning (ML) model that generates a parameterized optimized topology is presented, capable of eliminating the conventional iterative steps of TO, which shortens the development cycle and decreases overall development costs. The ML algorithm used, a conditional generative adversarial network (cGAN) known as Pix2Pix-GAN, is adopted to train using a variety of training data pairs consisting of color-coded images and is applied to an example of cantilever optimization, significantly enhancing model accuracy and operational efficiency. The analysis of training data numbers in relation to the model’s accuracy shows that as data volume increases, the accuracy of the model improves. Various ML models are developed and validated in this study; however, some artefacts are still present in the generated designs. Structures that are free from these artefacts achieve 91% reliability successfully. On the other hand, the images generated with artefacts may still serve as suitable design templates with minimal adjustments. Furthermore, this research also assesses compliance with two manufacturing constraints: the limitations on build space and passive elements (voids). Incorporating manufacturing constraints into model design ensures that the generated designs are not only optimized for performance but also feasible for production. By adhering to these constraints, the models can deliver superior performance in future use while maintaining practicality in real-world applications. Full article