Geometric Resolution Research Articles

Convolutional network denoising for acceleration of multi-shot diffusion MRI

Multi-shot echo planar imaging is a promising technique to reduce geometric distortions and increase spatial resolution in diffusion-weighted MRI (DWI), at the expense of increased scan time. Moreover, performing DWI in the body requires multiple repetitions to obtain sufficient signal-to-noise ratio, which further increases the scan time. This work proposes to reduce the number of repetitions and perform denoising of high b-value images using a convolutional network denoising trained on single-shot DWI to accelerate the acquisition of multi-shot DWI. Convolutional network denoising is demonstrated to accelerate the acquisition of 2-shot DWI by a factor of 4 compared to the clinical standard on patients with rectal cancer. Image quality was evaluated using qualitative scores from expert body radiologists between accelerated and non-accelerated acquisition. Additionally, the effect of convolutional network denoising on each image quality score was analyzed using a Wilcoxon signed-rank test. Convolutional network denoising would enable to increase the number of shots without increasing scan time for significant geometric artifact reduction and spatial resolution increase.

GEOMETRIC PROCESSING OF VERY HIGH-RESOLUTION SATELLITE IMAGERY: QUALITY ASSESSMENT FOR 3D MAPPING NEEDS

Abstract. In recent decades, the geospatial domain has benefitted from technological advances in sensors, methodologies, and processing tools to expand capabilities in mapping applications. Airborne techniques (LiDAR and aerial photogrammetry) generally provide most of the data used for this purpose. However, despite the relevant accuracy of these technologies and the high spatial resolution of airborne data, updates are not sufficiently regular due to significant flight costs and logistics. New possibilities to fill this information gap have emerged with the advent of Very High Resolution (VHR) optical satellite images in the early 2000s. In addition to the high temporal resolution of the cost-effective datasets and their sub-meter geometric resolutions, the synoptic coverage is an unprecedented opportunity for mapping remote areas, multi-temporal analyses, updating datasets and disaster management. For all these reasons, VHR satellite imagery is clearly a relevant study for National Mapping and Cadastral Agencies (NMCAs). This work, supported by EuroSDR, summarises a series of experimental analyses carried out over diverse landscapes to explore the potential of VHR imagery for large-scale mapping.

System Design and Echo Preprocessing of Spaceborne Squinted Two-Dimensional Beam Scanning Synthetic Aperture Radar.

Conventional squinted sliding spotlight synthetic aperture radar (SAR) imaging suffers from substantial swath width reduction and complex processing requirements due to the continuous variation in the squint angle and the large range cell migration (RCM) throughout the data acquisition interval. A novel two-dimensional (2D) beam scanning mode for high-resolution wide swath (HRWS) imaging is proposed. The key to the novel imaging mode lies in the synchronous scanning of azimuth and range beams, allowing for a broader and more flexible imaging swath with a high geometric resolution. Azimuth beam scanning from fore to aft was used to improve the azimuth resolution, while range beam scanning was adopted to illuminate the oblique wide swath to avoid the large RCM and the serious swath width reduction. Compared with the conventional sliding spotlight mode, both the swath width and swath length could be extended. According to the echo model of this imaging mode, an echo signal preprocessing approach is proposed. The key points of this approach are range data extension and azimuth data upsampling. A designed system example with a resolution of 0.5 m, swath width of 60 km, and azimuth coverage length of 134 km is presented. Furthermore, a simulation experiment on point targets was carried out. Both the presented system example and imaging results of point targets validated the proposed imaging mode.

Nonlinear constitutive calculation method of rarefied flow based on deep convolution neural networks

In the field of rarefied gas dynamics, the presence of non-equilibrium flow characteristics poses significant challenges for achieving efficient and accurate numerical simulation methods. These challenges arise from the complex coexistence of these phenomena at multiple scales. The recent advent of intelligent fluid mechanics has introduced the data-driven nonlinear constitutive relation (DNCR) method as a promising approach for expeditious physical modeling of non-equilibrium rarefied flows. To enhance the generalization capabilities of the DNCR method, this study proposes a deep convolutional neural network model (DNCR-CNN) based on data-driven nonlinear constitutive relations, integrated with free-form deformation (FFD). Employing FFD technology, a series of hypersonic geometric shapes are generated for model training, and a multi-task learning-based deep convolutional neural network model is subsequently trained. The prediction of the hypersonic geometric shapes test set is carried out, and the results of the model prediction are substituted in the conservation equation for the iterative solution, thereby enhancing the DNCR method's generalization performance for varying geometric shapes. Upon conducting a comparative analysis of the outcomes obtained from DNCR, Navier–Stokes (NS), and unified gas kinetic scheme (UGKS), it is revealed that the DNCR method can maintain computational resource levels equivalent to those of the NS equation while achieving a level of accuracy comparable to UGKS under diverse geometric shapes and grid resolutions. The enhancements in usability render the DNCR method a potent tool for addressing the challenges posed by rarefied gas, thereby expanding its applicability within the field.

Identification of Surgical Instruments Using a Low Frequency Magnetic Field

Abstract Background: As part of a open surgical intervention, it is desirable to identify, track and count the surgical instruments used, in order to document and potentially optimize the process. In the past, various technologies have been proposed to this end, as e.g. RFIDs attached to or included in the instruments, contact-less identification by image analysis or (bar-, QR-) codes, or counting by human resources, but all of them having their individual drawbacks. Objective: We present and evaluate a new method to identify metallic-conductive surgical instruments using Low Frequency (LF) magnetic fields. Method: The investigated LF magnetic field approach is based on the IndLoc system developed by the Fraunhofer IIS - originally for metallic asset identification within the field of supply chain management - and is applied on small collection of 13 typical surgical tools (four scissors, two needle holders, four tweezers, two retractors, a bone curette) for open surgery. Results: 9 out of the 13 instruments can be correctly identified, two pairs (2 short and 2 medium tweezers) are too self-similar and could not discriminated correctly from each other. Conclusion: These initial experiments show that an LF-based identification of metallic surgical tools is feasible, but the system needs to be modified (with more and smaller coils) to increase the geometrical resolution to better distinguish between self-similar and potentially more objects.

Detail CFD simulation of the integrated reactor pressure vessel of SMART

This paper presents a detailed analysis of the full Reactor Pressure Vessel (RPV) of SMART with ANSYS CFX 20.2. SMART is a water-cooled SMR designed by KAERI for an operation with forced convection. The thermal power of SMART amounts to 330 MWth. In the integrated SMR-concept, the main primary loop components such as the helical steam generators, pressurizer and canned pumps are located inside the RPV. The CFD model uses a concept of full geometrical detail resolution with partial boundary inflation of the mesh and porous media modelling with sources for momentum and energy for components like the core, pumps and steam generators. The detailed results obtained with a steady-state RANS simulation using ANSYS CFX 20.2 are very promising as the comparison of selected core parameters of the CFD-simulation with the ones of the system thermal hydraulic code TRACE have shown.

PFDIR – A Wideband Photonic-Assisted SAR System

The next generation of synthetic aperture radar (SAR) systems will need transmit and receive analog signals over a wide frequency range with large bandwidth to fulfil the increasing demands. Since the performance of the digital microwave component is deteriorated at increasing frequency, radio frequency (RF) front-end with the capability of frequency conversion is needed for frequency beyond a few gigahertz. However, for the widely employed RF mixers, the inherent imperfect of I-V characteristics of nonlinear devices, generates many undesired mixing spurs which restricts on the instantaneous bandwidth and the spurious-free dynamic range (SFDR). At Aerospace Information Research Institute Chinese Academy of Sciences, a photonic-assisted front-end with functions of signal generation and deramp processing has been conceived and implemented in an experimental deramp-on-receive CW SAR system. The system is called PFDIR (Photonic-Assisted Front-End Deramp-on-Receive Imaging Radar) and is envisaged to obtain SAR imaging at high resolution. In current stage, the Ku-band system with a bandwidth of 5.72 GHz, corresponding to a relative bandwidth of 37.8%, operates well with a fixed two-horn antenna. The configuration of the photonic front-end and the system design are described. Ground-based tests and airborne imaging results with geometrical resolution down to 5 cm are presented.

Geoinformatics Engineering and GIS for Urban Growth Patterns Assessment

Urbanization has profound effects on administrative boundaries, resulting in the expansion of urban areas, particularly at the periphery. This rapid growth leads to significant changes in landcover and land use, as agricultural and natural open areas are progressively transformed into densely populated urban landscapes characterized by housing, commercial infrastructure, and transportation systems.
 The capital city of Jordan, Amman, faces exceptional urban growth, with its population surpassing 4.5 million people. This unprecedented expansion has given rise to extensive urban landscapes, presenting challenges for planners who lack a holistic understanding of the wide-ranging impacts.
 To address these complexities and make well-informed decisions, planners urgently require comprehensive, up-to-date information on the causes, chronology, and consequences of urbanization. Integrating high-precision satellite imagery, geoinformatics data, and topographic insights offers a promising avenue to develop comprehensive inventories of urban change and growth. Such knowledge acts as a vital resource, enabling accurate assessments of expanding built-up areas and their associated implications.
 The use of high geometric resolution satellite imagery and geoinformatics data combined with topographic information and GIS could provide effective information to develop urban change and growth inventory which could be explored towards producing a very important signature for the built-up area changes.

The Effectiveness of Pan-Sharpening Algorithms on Different Land Cover Types in GeoEye-1 Satellite Images.

In recent years, the demand for very high geometric resolution satellite images has increased significantly. The pan-sharpening techniques, which are part of the data fusion techniques, enable the increase in the geometric resolution of multispectral images using panchromatic imagery of the same scene. However, it is not trivial to choose a suitable pan-sharpening algorithm: there are several, but none of these is universally recognized as the best for any type of sensor, in addition to the fact that they can provide different results with regard to the investigated scene. This article focuses on the latter aspect: analyzing pan-sharpening algorithms in relation to different land covers. A dataset of GeoEye-1 images is selected from which four study areas (frames) are extracted: one natural, one rural, one urban and one semi-urban. The type of study area is determined considering the quantity of vegetation included in it based on the normalized difference vegetation index (NDVI). Nine pan-sharpening methods are applied to each frame and the resulting pan-sharpened images are compared by means of spectral and spatial quality indicators. Multicriteria analysis permits to define the best performing method related to each specific area as well as the most suitable one, considering the co-presence of different land covers in the analyzed scene. Brovey transformation fast supplies the best results among the methods analyzed in this study.

Multidetector Computed Tomography in Cases with Chest Trauma

Introduction: Chest traumas are a significant cause of mortality and morbidity. They commonly occur in the younger population and are the most common cause of death in people between 25 and 40 years of age. Purpose: This article summarizes the place of MDCT in the diagnosis, examination technique and findings in injuries of the chest trauma patients. Methods: Multidetector computed tomography (MDCT) is considered to be the most effective imaging method in this field. Its advantages include especially high speed and high geometric resolution at any plane. The method allows us to view large parts of the body with minimal motion artifacts and to create accurate multiplanar and three[1]dimensional (3D) reformations. Results: MDCT allows us to view diagnosis significantly more accurate. One hundred and twelve patients are examined at the Emergency department. The injuries of the chest and intrathoracic organs are found. In 56 (50%) of all 112 patients included in our study, fractures of the ribs are the most common traumas. Also fracture of the scapula, sternum, extra pleural hematomas, pneumothorax, hemothorax, pulmonary contusions, laceration of trachea and bronchi, rupture of esophagus, diaphragmatic rupture and injuries of blood vessels are established. Conclusion: Because of its advantages MDCT has become the first[1]choice method in emergency chest trauma patients.

The effect of geometric design and materials on section properties of additively manufactured lattice elements

Additive manufacturing (AM) technologies such as laser-based powder bed fusion (LB-PBF) facilitate the fabrication of complex lattice structures. However, these structures consistently display dimensional variation between the idealised and as-manufactured specimens. This research proposes a method to characterise the impact of common LB-PBF powders (aluminium and titanium alloys) and geometric design parameters (polygon order, effective diameter, and inclination angle) on section properties relevant to stiffness and strength of as-manufactured strut elements. Micro-computed tomography (µCT) has been applied to algorithmically characterise the as-manufactured variation and identify a scale threshold below which additional geometric resolution does not influence the section properties of as-manufactured parts. This methodology provides a robust and algorithmic design for additive manufacturing (DFAM) tool to characterise the effects of manufacturing and design parameters on the functional response of AM strut elements, as is required for certification and optimisation.

First of its kind: a test artifact for direct laser writing

With femtosecond-laser direct writing (fs-LDW) maturing in all aspects as a manufacturing technology, a toolset for quality assurance must be developed. In this work we introduce a first of its kind test artifact. Test artifacts are standardized 3D models with specific geometric features to evaluate the performance of writing parameters. Test artifacts are already common in other 3D additive manufacturing technologies e.g. selective laser melting. The test artifact introduced in this work was developed in particular to accommodate (1) the high geometrical resolution of fs-LDW structures and (2) the limited possibilities to examine the resulting structure. Geometric accuracy, surface adhesion as well as confocal Raman spectroscopy results were considered when evaluating the design of the test artifact. We will explain the individual features and design considerations of our fs-LDW test artifact. The difference between two slicers, Cura and 3DPoli, and the implications on measured feature sizes and the general shape is quantified. The measured geometries are used to derive a general design guide for a specific combination of photoresists, laser power and scanning speed and to analyze the geometric accuracy of a structure produced using these guidelines. The shown test artifact is publicly available as STL file on GitHub (https://github.com/BAMresearch/2PP-TestArtifact) and in the supplement.

Deep Convolutional Compressed Sensing-Based Adaptive 3D Reconstruction of Sparse LiDAR Data: A Case Study for Forests

LiDAR point clouds are characterized by high geometric and radiometric resolution and are therefore of great use for large-scale forest analysis. Although the analysis of 3D geometries and shapes has improved at different resolutions, processing large-scale 3D LiDAR point clouds is difficult due to their enormous volume. From the perspective of using LiDAR point clouds for forests, the challenge lies in learning local and global features, as the number of points in a typical 3D LiDAR point cloud is in the range of millions. In this research, we present a novel end-to-end deep learning framework called ADCoSNet, capable of adaptively reconstructing 3D LiDAR point clouds from a few sparse measurements. ADCoSNet uses empirical mode decomposition (EMD), a data-driven signal processing approach with Deep Learning, to decompose input signals into intrinsic mode functions (IMFs). These IMFs capture hierarchical implicit features in the form of decreasing spatial frequency. This research proposes using the last IMF (least varying component), also known as the Residual function, as a statistical prior for capturing local features, followed by fusing with the hierarchical convolutional features from the deep compressive sensing (CS) network. The central idea is that the Residue approximately represents the overall forest structure considering it is relatively homogenous due to the presence of vegetation. ADCoSNet utilizes this last IMF for generating sparse representation based on a set of CS measurement ratios. The research presents extensive experiments for reconstructing 3D LiDAR point clouds with high fidelity for various CS measurement ratios. Our approach achieves a maximum peak signal-to-noise ratio (PSNR) of 48.96 dB (approx. 8 dB better than reconstruction without data-dependent transforms) with reconstruction root mean square error (RMSE) of 7.21. It is envisaged that the proposed framework finds high potential as an end-to-end learning framework for generating adaptive and sparse representations to capture geometrical features for the 3D reconstruction of forests.

Editorial for the Special Issue “Latest Development in 3D Mapping Using Modern Remote Sensing Technologies”

Recent advances in remote sensing technologies have provided the research community with unprecedented geospatial data characterized by high geometric, radiometric, spectral, and temporal resolution [...]

Improved Analytical Formula for the SAR Doppler Centroid Estimation Standard Deviation for a Dynamic Sea Surface

The existing formulas for the synthetic aperture radar (SAR) Doppler centroid estimation standard deviation (STD) suffer from various limitations, especially for a dynamic sea surface. In this study, we derive an improved version of these formulas through three steps. First, by considering the ocean wavenumber spectrum information, a new strategy for determining the number of independent samples of the sea wave velocity field is adopted in the new formula. This is contrary to the method used in the existing formulas, where the number of SAR geometric resolution cells is taken as the number of samples assuming that adjacent SAR resolution cells are statistically uncorrelated. Second, the pulse repetition frequency and Doppler bandwidth are decoupled in the new formula, unlike in the existing formulas where they are unchangeably related to each other. Third, the effects of thermal noise and Doppler aliasing are jointly quantified in a mathematically exact manner instead of being treated separately, as in the existing formulas. Comprehensive SAR raw data simulations for the ocean surface show that the new formula has a better performance in predicting the Doppler centroid estimation STD than the existing formulas.

Focus on test, measurement, quantum metrology, and analytical equipment

Focus on test, measurement, quantum metrology, and analytical equipment

Wearable Printed Temperature Sensors: Short Review on Latest Advances for Biomedical Applications.

The rapid growth in wearable biosensing devices is driven by the strong desire to monitor the human health data and to predict the symptoms of chronic diseases at an early stage. Different sensors are developed for continuous monitoring of various biomarkers through wearable and implantable sensing patches. Temperature sensor has proved to be an important physiological parameter amongst the various wearable biosensing patches. This paper highlights the recent progresses made in printing of functional nanomaterials for developing wearable temperature sensors on polymeric substrates. A special focus is given to the advanced functional nanomaterials as well as their deposition through printing technologies. The geometric resolutions, shape, physical and electrical characteristics as well as sensing properties using different materials are compared and summarized. Wearability is the main concern of these newly developed sensors, which is summarized by discussing representative examples. Finally, the challenges concerning the stability, repeatability, reliability, sensitivity, linearity, ageing, and large-scale manufacturing are discussed with future outlook of the wearable systems.

Pattern-Based Cloth Registration and Sparse-View Animation

We propose a novel multi-view camera pipeline for the reconstruction and registration of dynamic clothing. Our proposed method relies on a specifically designed pattern that allows for precise video tracking in each camera view. We triangulate the tracked points and register the cloth surface in a fine-grained geometric resolution and low localization error. Compared to state-of-the-art methods, our registration exhibits stable correspondence, tracking the same points on the deforming cloth surface along the temporal sequence. As an application, we demonstrate how the use of our registration pipeline greatly improves state-of-the-art pose-based drivable cloth models. Furthermore, we propose a novel model, Garment Avatar , for driving cloth from a dense tracking signal which is obtained from two opposing camera views. The method produces realistic reconstructions which are faithful to the actual geometry of the deforming cloth. In this setting, the user wears a garment with our custom pattern which enables our driving model to reconstruct the geometry. Our code and data are available at https://github.com/HalimiOshri/Pattern-Based-Cloth-Registration-and-Sparse-View-Animation. The released data includes our pattern and registered mesh sequences containing four different subjects and 15k frames in total.

Feedforward control of thermal history in laser powder bed fusion: Toward physics-based optimization of processing parameters

We developed and applied a model-driven feedforward control approach to mitigate thermal-induced flaw formation in laser powder bed fusion (LPBF) additive manufacturing process. The key idea was to avert heat buildup in a LPBF part before it is printed by adapting process parameters layer-by-layer based on insights from a physics-based thermal simulation model. The motivation being to replace cumbersome empirical build-and-test parameter optimization with a physics-guided strategy. The approach consisted of three steps: prediction, analysis, and correction. First, the temperature distribution of a part was predicted rapidly using a graph theory-based computational thermal model. Second, the model-derived thermal trends were analyzed to isolate layers of potential heat buildup. Third, heat buildup in affected layers was corrected before printing by adjusting process parameters optimized through iterative simulations. The effectiveness of the approach was demonstrated experimentally on two separate build plates. In the first build plate, termed fixed processing, ten different nickel alloy 718 parts were produced under constant processing conditions. On a second identical build plate, called controlled processing, the laser power and dwell time for each part was adjusted before printing based on thermal simulations to avoid heat buildup. To validate the thermal model predictions, the surface temperature of each part was tracked with a calibrated infrared thermal camera. Post-process the parts were examined with non-destructive and destructive materials characterization techniques. Compared to fixed processing, parts produced under controlled processing showed superior geometric accuracy and resolution, finer grain size, increased microhardness, and reduced surface roughness.

Geo3o1: a tool for 3D georeferencing accuracy assessment of tri-stereo images by state-of-the-art sensor dependent orientation model

We subjected a tool for 3D georeferencing accuracy assessment of tri-stereo primary images of Pléiades 1A/1B/Neo, SPOT 6/7, and Göktürk-1, developed in the MATLAB environment considering their private sensor-dependent orientation model. Although the orientation model has been developed by the image vendors, the major contribution of Geo3o1 was the two-step adjustment process. The basic stages of estimating the 3D georeferencing accuracy were (i) estimating the direct georeferencing accuracy by raw orientation parameters, (ii) pre-adjustment of look angles using ground control points (GCPs), and (iii) bundle adjustment. In addition, the efficiency and validation of exterior orientation parameters and also the correlation among interior and exterior orientation parameters could be analyzed. Geo3o1 has the capability of handling the images in stereo or tri-stereo modes. The case study has been carried out by the Pléiades 1A panchromatic tri-stereo primary images with 50-cm geometric resolution. About 90 points were used as GCPs/ICPs in two distribution scenarios. The accuracy on ground was estimated centimeter level at GCPs whereas the accuracy was naturally coarser at ICPs at ∼ ± 1 GSD. The efficiency and validation of exterior orientation parameters and the correlations were also shown by two samples. The research was carried out over an undulating, mountainous area in Zonguldak, Türkiye.