Geometric Accuracy Research Articles

IMMERSIVE AUGMENTED REALITY APPLICATIONS IN JAW AND KNEE REPLACEMENT SURGERY: ACCURACY AND PROCESSING TIME RESULTS

In medicine and healthcare, augmented reality (AR) has been used by physicians during surgical procedures. It has provedhelpful in preoperative planning and procedure navigation by allowing them to display in-depth information and visualizedetails in real time during surgery, prioritizing patient safety and healthcare. Due to the critical nature of surgical procedures,extreme accuracy is required when using ar technology to maintain patients' health. A few years ago, ar faced severalchallenges and limitations in surgeries, such as noise in real-time images, cutting errors, navigation errors, wrong implantplacement, overlay errors, navigating narrow areas, geometric accuracy limitations, image alignment, image registration,and occlusion handling. This paper reviews several recently published articles exploring ar technology usage in jaw andknee replacement surgery, focusing on identifying the newest technologies, methods, and solutions for the abovementionedlimitations. Based on data collected from the published papers, the results were compared for each problem solved in eacharticle regarding accuracy and processing time.

A novel method for rapid estimation of active bone marrow dose for radiotherapy patients in epidemiological studies.

In a dedicated effort to improve the assessment of clonal hematopoiesis (CH) and study leukemia risk following radiotherapy, we are developing a large-scale cohort study among cancer patients who received radiation. To that end, it will be critical to analyze dosimetric parameters of red bone marrow (ABM) exposure in relation to CH and its progression to myeloid neoplasms, requiring reconstruction method for ABM doses of a large-scale patients rapidly and accurately. To support a large-scale cohort study on the assessment of clonal hematopoiesis and leukemia risk following radiotherapy, we present a new method for the rapid reconstruction of ABM doses of radiotherapy among cancer patients. The key idea of the presented method is to segment patient bones rapidly and automatically by matching a whole-body computational human phantom, in which the skeletal system is divided into 34 bone sites, to patient CT images via 3D skeletal registration. The automatic approach was used to segment site-specific bones for 40 radiotherapy patients. Also, we segmented the bones manually. The bones segmented both manually and automatically were then combined with the patient dose matrix calculated by the treatment planning system (TPS) to derive patient ABM dose. We evaluated the performance of the automatic method in geometric and dosimetric accuracy by comparison with the manual approach. The pelvis showed the best geometric performance [volume overlap fraction (VOF): 52% (mean) with 23% (σ) and average distance (AD): 0.8cm (mean) with 0.5cm (σ)]. The pelvis also showed the best dosimetry performance [absorbed dose difference (ADD): 0.7Gy (mean) with 1.0Gy (σ)]. Some bones showed unsatisfactory performances such as the cervical vertebrae [ADD: 5.2Gy (mean) with 10.8Gy (σ)]. This impact on the total ABM dose, however, was not significant. An excellent agreement for the total ABM dose was indeed observed [ADD: 0.4Gy (mean) with 0.4Gy (σ)]. The computation time required for dose calculation using our method was robust (about one minute per patient). We confirmed that our method estimates ABM doses across treatment sites accurately, while providing high computational efficiency. The method will be used to reconstruct patient-specific ABM doses for dose-response assessment in a large cohort study. The method can also be applied to prospective dose calculation within a clinical TPS to support clinical decision making at the point of care.

Experimental study on the effect of filament-extrusion rate on the structural, mechanical and thermal properties of material extrusion 3D-printed polylactic acid (PLA) products

AbstractMaterial extrusion (MEX), also commonly referred to as fused deposition modeling (FDM) or fused filament fabrication (FFF) is currently one of the most commonly used additive manufacturing techniques. The quality of the 3D-printed objects fabricated by MEX methods highly relies on various printing parameters, one of which is the so-called filament extrusion multiplier (k). In this study, 3D-printed parts were prepared by MEX technique during which the material feeding rate was adjusted by varying the extrusion multiplier in the range of 97–105% (k = 0.97–1.05). The fabricated parts were tested for their geometrical, structural, mechanical, and thermal conductivity properties. Based on computed tomographic analysis and scanning electron microscopic images, increasing the k parameter resulted in smaller voids, along with gradually decreasing porosity (from 5.82 to 0.05%). Parallel to the decreasing defects, the thermal conductivity of the parts improved from 0.157 to 0.188 W/mK as determined by light-flash analysis technique. On the other hand, when k was set to ≥ 1.03 the geometrical accuracy declined, the size of the specimens considerably increased relative to the nominal values, especially in the X–Y directions due to excess material getting “squeezed” on the sides of the specimens. This latter phenomenon also resulted in the formation of a number of stress concentration sites, which manifested in the decrease of mechanical properties. Accordingly, the tensile, flexural, and impact strength of the samples improved up to k = 1.03; however, above that it dropped considerably.

Geometric accuracy evaluation and analysis of ZY-1 02E IRS thermal infrared image data using GCP extraction based on phase correlation matching method

Abstract. The Ziyuan-1 (ZY-1) 02E launched on December 26, 2021 is equipped with a thermal infrared sensor (IRS), which has a ground resolution of better than 16m and a width priority of 115km, balancing the advantages of high resolution and large wide observation. The geometric performance of image data is the premise of remote sensing application, and the difficulty in evaluating the geometric performance of thermal infrared image data lies in the extraction of well-distributed, reliable and accurate GCPs. To extract GCP from high-precision reference images, it is necessary to overcome the feature differences between images caused by different spectral responses. This paper adopts a phase correlation matching method based on frequency domain to realize the fine registration of the data obtained by the emission thermal spectral band with the data from the reflectance spectral band, which can not only solve the GCP extraction of conventional thermal infrared images collected during the day, but also obtain satisfactory GCP data from thermal infrared data acquired at night. In order to test the GCP method proposed in this paper, three typical areas are selected as the experimental areas, including Yiyang City in Hunan, Nagqu City in Xizang and Hami City in Xinjiang, and the internal geometric accuracy and absolute geolocation accuracy of the thermal infrared data spanning one year are evaluated and analyzed by using the reference data composed of the DOM with an accuracy of 2m and the DEM with an accuracy of 10m. The research results indicate that the internal geometric accuracy of ZY-1 02E IRS satellite image data is better than 1.0 pixels, and the performance is satisfactory. However, its absolute geolocation accuracy needs to be continuously improved, especially there are systematic errors in the ascending data at night that require further research. Overall, it meets the design accuracy indicators of satellites and can meet the application requirements of thermal infrared remote sensing.

Development of Multi-Strip Image Mosaicking for KOMPSAT-3A Images

Abstract. High-resolution satellite imagery has a limitation in terms of coverage area. This limitation presents challenges for extensive-scale analysis at regional or national levels. To maximize the utility of high-resolution satellite imagery, the implementation of image mosaicking techniques is essential. In this paper, we have developed seamline extraction techniques and relative geometric correction optimized for high-resolution satellite imagery. Ultimately, we proposed a multi-strip image mosaicking method for KOMPSAT-3A (Korea Multi-Purpose Satellite-3A) images. We applied the Dijkstra's shortest path algorithm to efficiently extract seamlines. we also performed image registration based on feature matching and homography transformation to correct the relative geometric errors between input images. We conducted experiments with our methods using 29 scenes from KOMPSAT-3A L1G data. The results indicated high relative geometric accuracy, with an average error of 1.63 pixels. Furthermore, we were able to obtain high-quality seamless mosaic images. Our proposed method is expected to enhance the utility of KOMPSAT-3A imagery for large-scale environmental and urban analysis and to provide more accurate and comprehensive data.

Leveraging Deep Learning for Automated Reconstruction of Indoor Unstructured Elements in Scan-to-BIM

Abstract. Achieving automatic 3D reconstruction for indoor scenes is extremely useful in the field of scene understanding. Building information modeling (BIM) models are essential for lowering project costs, assisting in building planning and renovations, as well as improving building management efficiency. However, nearly all current available scan-to-BIM approaches employ manual or semi-automatic methods. These approaches concentrate solely on significant structured objects, neglecting other unstructured elements such as furniture. The limitation arises from challenges in modeling incomplete point clouds of obstructed objects and capturing indoor scene details. Therefore, this research introduces an innovative and effective reconstruction framework based on deep learning semantic segmentation and model-driven techniques to address these limitations. The proposed framework utilizes wall segment recognition, feature extraction, opening detection, and automatic modeling to reconstruct 3D structured models of point clouds with different room layouts in both Manhattan and non-Manhattan architectures. Moreover, it provides 3D BIM models of actual unstructured elements by detecting objects, completing point clouds, establishing bounding boxes, determining type and orientation, and automatically generating 3D BIM models with a parametric algorithm implemented into the Revit software. We evaluated this framework using publicly available and locally generated point cloud datasets with varying furniture combinations and layout complexity. The results demonstrate the proposed framework's efficiency in reconstructing structured indoor elements, exhibiting completeness and geometric accuracy, and achieving precision and recall values greater than 98%. Furthermore, the generated unstructured 3D BIM models keep essential real-scene characteristics such as geometry, spatial locations, numerical aspects, various shapes, and orientations compared to literature methods.

Evaluating geometric measurement accuracy based on 3D model reconstruction of nursery tomato plants by Agisoft photoscan software

Measuring the vegetation structure of a plant with the help of 3D images is a non-destructive method that can be used to monitor the plant’s status during the growing season. Structure from motion (SfM) is a popular optical distance measurement method that uses 2D images to create 3D models. Identifying factors that influence the precision of SfM is important due to its increasing use in phenotyping. The primary objectives of this study were, i) to scrutinize the impact of imaging parameters—including, color checkerboard location, overlap size of images, and background filtering—on the quality indices of 3D models, and ii) to attain precise measurements of leaf area, stem diameter, and height through the combination of images captured from different camera shooting angles. The three nursery tomato plants were photographed at every five-degree rotation of the turntable, five different locations of color checkerboards, and five camera shooting angles. Results indicated the color checkerboard’s presence and position affect the quality indices of the 3D models. Visually and by the lowest mean keypoint size value, the least noisy 3D model was obtained when the wall of the plant pot was covered with a checkerboard. When the base-to-distance (b/y) ratio as the image overlap index increased from 0.1 to 0.2, the generation time of the 3D models decreased by more than two times. Optimal Mean Absolute Percentage Error (MAPE) values of 1.08 %, 1.01 %, and 1.38 % were achieved for the estimation of leaf area, stem diameter, and height, respectively. This was recorded by using images taken at 0°, 30°, and 105° vertical azimuth angles. Constructing a 3D model by integrating images captured from other angles or more azimuth angles represents numerous visual challenges and leads to problems of point mismatch, which affects the accuracy of measurements.

An emergent ontology for digitally modeling existing buildings: examining Kyoto’s Nishiki market

PurposeThis project examines digital modeling strategies for existing buildings. In this context, it aims to question assumptions about the need for geometric accuracy and the efficacy of predefined ontologies. As a counterpoint to prevailing digital modeling strategies, this project proposes a digital modeling approach using a project-specific, emergent ontology.Design/methodology/approachNishiki Market, in Kyoto, Japan, is studied as a test case. The emergent-ontology modeling process is introduced with an initial minimal set of operations including basic fold and trim operations applicable to surfaces. As the model develops iteratively, new situations are encountered for which existing rules are insufficient. In response, the model maker’s subjective judgment is invoked to introduce new operations, and ontological rules are allowed to expand.FindingsThe emergent-ontology approach, when executed on the Nishiki Market test case, enables representation of specific architectural qualities, highlighting semantic distinctions between digitally modeled elements of real-world features. The modeling approach generated project-specific knowledge, informing disciplinary understanding. Ontological emergence enabled semantic relationships to be disclosed and newly constructed.Originality/valueThe project proposes a novel methodology using an emergent ontology for digitally modeling existing buildings. Instead of remaining within the limitations a predefined ontology, the model maker’s subjective decisions shape the model’s ongoing development. This interpretive approach allows project-specific knowledge generation while challenging prevailing assumptions about accuracy and consistency in digital models of existing buildings.

Searching for optimal solutions in a landscape fragmentation assessment: A case study from Poland – Identification of spatial data and methods

There is no single correct or optimal scale for describing spatial landscape heterogeneity. However, since landscape patterns are scale-dependent, using different scales in the analysis may result in different outcomes, which can affect landscape management options. The main goal of our research was to identify the most adequate GIS solution for converting vector data to raster data while keeping the spatial continuity of most linear features and input land cover data as detailed as possible. In addition, to compare our database with the CORINE Land Cover data approach, we used 3 different rasterization methods and raster data with 100- and 50-m pixel sizes, first, to match the geometric accuracy of the CORINE Land Cover data and second, to compare the calculation results acquired with the higher-resolution rasterized BDOT10k data. We found that it was possible to compare these methods and assess how much data resolution and selection of rasterization methods affected the calculations of the landscape metrics. The calculation results of specific fragmentation metrics differed tremendously when higher data resolutions and rasterization methods were used, especially when using the custom priority rasterization method.

Image quality evaluation of a new high-performance ring-gantry cone-beam computed tomography imager

Objective. Newer cone-beam computed tomography (CBCT) imaging systems offer reconstruction algorithms including metal artifact reduction (MAR) and extended field-of-view (eFoV) techniques to improve image quality. In this study a new CBCT imager, the new Varian HyperSight CBCT, is compared to fan-beam CT and two CBCT imagers installed in a ring-gantry and C-arm linear accelerator, respectively. Approach. The image quality was assessed for HyperSight CBCT which uses new hardware, including a large-size flat panel detector, and improved image reconstruction algorithms. The decrease of metal artifacts was quantified (structural similarity index measure (SSIM) and root-mean-squared error (RMSE)) when applying MAR reconstruction and iterative reconstruction for a dental and spine region using a head-and-neck phantom. The geometry and CT number accuracy of the eFoV reconstruction was evaluated outside the standard field-of-view (sFoV) on a large 3D-printed chest phantom. Phantom size dependency of CT numbers was evaluated on three cylindrical phantoms of increasing diameter. Signal-to-noise and contrast-to-noise were quantified on an abdominal phantom. Main results. In phantoms with streak artifacts, MAR showed comparable results for HyperSight CBCT and CT, with MAR increasing the SSIM (0.97–0.99) and decreasing the RMSE (62–55 HU) compared to iterative reconstruction without MAR. In addition, HyperSight CBCT showed better geometrical accuracy in the eFoV than CT (Jaccard Conformity Index increase of 0.02–0.03). However, the CT number accuracy outside the sFoV was lower than for CT. The maximum CT number variation between different phantom sizes was lower for the HyperSight CBCT imager (∼100 HU) compared to the two other CBCT imagers (∼200 HU), but not fully comparable to CT (∼50 HU). Significance. This study demonstrated the imaging performance of the new HyperSight CBCT imager and the potential of applying this CBCT system in more advanced scenarios by comparing the quality against fan-beam CT.

Experimental and numerical mechanical characterisation of additively manufactured polymeric lattice structures under uniaxial tensile load

AbstractAdditive manufacturing enables the production of lighter, more robust components with intricate features like lattice structures. However, since the mechanical behaviour of lattice structures is not fully characterized, the application of such potential is limited today. The challenge with lattice structures tensile tests is defining a suitable design that fits the standard requirements and process characteristics. In the polymeric powder bed fusion process, the problem is to produce powder-free geometries and to avoid stress concentrations zones, adapting the specimen accordingly. In this regard, numerical simulation may provide insightful information and support the analysis of the deformation mechanisms. This paper analyses a new tensile sample for lattice structures using finite element analysis. The sample is designed following the EN ISO 527 standard prescriptions. An area with a controlled gradation of the lattice relative density is designed to ensure both powder-free voids and fracture localization within the lattice specimen gauge length. Experimental tests are performed to validate the numerical results using a modified body cubic centred topology with two different strut diameters. The specimens are produced in polyamide by powder bed AM process. Due to the complexity of the lattice design, a digital image correlation is used to compute the full range of strains at the macroscopic level. Experimental and numerical strain maps results showed a good agreement. The recorded deviation was attributed to the process-induced defect, such as the geometrical accuracy that, if compensated, boosted the capability of the numerical model to predict the mechanical behaviour of the lattice structure.

Prediction of the penetration depth of particle-loaded inks in binder jetting

Prediction of the penetration depth of particle-loaded inks in binder jetting

Special Issue on Advanced Metal Cutting Technologies

Cutting technologies are utilized in many industrial sectors, such as automobile, aircraft, and medical-device manufacturing as well as dies and molds. Thus, the requirements for higher geometric accuracy, better surface integrity, and longer component service lifetimes have substantially increased. In addition, maintaining high machining efficiency while simultaneously reducing power consumption and CO2 emissions during machining is important for sustainable development. This special issue features 11 papers on the most recent advances in cutting technologies for metals and composite materials. - Reverse finishing characteristics of drilling surfaces - Machining error simulation for end milling - Visualization of cutting phenomena using a single-crystal diamond tool - Milling of TiB2 particle-reinforced high-modulus steel - Electrodischarge-assisted turning of carbon fiber-reinforced polymers - Suppression of chatter vibration during double-insert turning - Prediction of surface roughness components during turning - Microtome blades for high-precision tissue sectioning - Boiling of coolant near the cutting edge in high-speed machining - Residual stress during drilling of aluminum alloy - Effect of strain hardening on burr control during drilling This issue provides an understanding of recent developments in cutting technologies, aiming to inspire further research in this field. We deeply appreciate the careful work of all the authors and thank the reviewers for their diligent efforts.

Direct-ink-write 3D printing of highly-stretchable polyaniline gel with hierarchical conducting network for customized wearable strain sensors

The development of direct-ink-write (DIW) 3D printing conducting polymer gels with remarkable mechanical properties, satisfactory conductivity, and customized three-dimensional structures is in high demand for next-generation wearable strain sensors, yet challenging due to the difficulty of preparing conducting polymer solutions with high concentrations. Herein, a novel cellulose nanocrystal (CNC)-regulated polyaniline (PANI) composite ink for DIW 3D printing customized wearable strain sensors with high stretchability and hierarchical conducting networks is fabricated. With the employment of CNC as a stabilizer to regulate the intermolecular interactions, the in-situ polymerized composite ink achieves well-dispersed PANI and abundant reversible intermolecular interactions, which is favorable for DIW 3D printing PANI composite gel with tailored planar patterns of high geometric accuracy. Benefiting from the hierarchical gel frameworks and abundant non-covalent interactions within the PANI-based composite ink, the PANI composite gel exhibits remarkable stretchability, promising low-temperature tolerance and excellent strain sensing performance. Due to its unique wavy mesh structure, the DIW-printed PANI composite gel-based wearable strain sensor is more sensitive and can accurately detect changes in the human body. This work aims to expedite the advancement of DIW printing conducting polymer gel for the development of personalized wearable strain sensors and advanced flexible electronics.

Optimized raw data selection for artifact reduction of breathing controlled four-dimensional sequence scanning

Optimized raw data selection for artifact reduction of breathing controlled four-dimensional sequence scanning

Enhancing formability in Micro-Incremental sheet forming by a novel stacking method of ultra-thin sheets

Metallic foils areused extensively in the microelectronics and avionics industries. However, forming these foils into complexshapes is challenging, due to size-effect. The foils are susceptible to bending and distortion under self-weight as they lack stiffness and fail early during forming. Processes, like drawing, stamping, etc. have been explored at the micro-scale, but they are suitable for simpler geometry and low aspect ratio parts. Micro-incremental sheet forming (μISF) is a new flexible forming process that can be used for micro-scale deformation of foils into complexgeometries with higher formability. This work attempts to improve the stiffness and formability of 100 μm thick CP-Ti Gr2 foils by proposing a unique ‘stacking of foils’ (SOF) approach in μISF. In SOF, multiple foils are stacked together which increases their stiffness and plastic deformation. Experimental results demonstrate that formability is considerably improved by about 21–27 %, with better geometrical accuracy of the micro-parts.

A novel evaluation model of image registration for cone-beam computed tomography guided lung cancer radiotherapy.

The aim of the study was to establish a weighted comprehensive evaluation model (WCEM) of image registration for cone-beam computed tomography (CBCT) guided lung cancer radiotherapy that considers the geometric accuracy of gross target volume (GTV) and organs at risk (OARs), and assess the registration accuracy of different image registration methods to provide clinical references. The planning CT and CBCT images of 20 lung cancer patients were registered using diverse algorithms (bony and grayscale) and regions of interest (target, ipsilateral, and body). We compared the coverage ratio (CR) of the planning target volume (PTVCT) to GTVCBCT, as well as the dice similarity coefficient (DSC) of the GTV and OARs, considering the treatment position across various registration methods. Furthermore, we developed a mathematical model to assess registration results comprehensively. This model was evaluated and validated using CRFs across four automatic registration methods. The grayscale registration method, coupled with the registration of the ipsilateral structure, exhibited the highest level of automatic registration accuracy, the DSC were 0.87 ± 0.09 (GTV), 0.71 ± 0.09 (esophagus), 0.74 ± 0.09 (spinal cord), and 0.91 ± 0.05 (heart), respectively. Our proposed WCEM proved to be both practical and effective. The results clearly indicated that the grayscale registration method, when applied to the ipsilateral structure, achieved the highest CRF score. The average CRF scores, excellent rates, good rate and qualification rates were 58 ± 26, 40%, 75%, and 85%, respectively. This study successfully developed a clinically relevant weighted evaluation model for CBCT-guided lung cancer radiotherapy. Validation confirmed the grayscale method's optimal performance in ipsilateral structure registration.

Broadband topology optimization of three-dimensional structural-acoustic interaction with reduced order isogeometric FEM/BEM

This paper presents a model order reduction method to accelerate broadband topology optimization of structural-acoustic interaction systems by coupling Finite Element Methods and Boundary Element Methods. The finite element method is used for simulating thin-shell vibration and the boundary element method for exterior acoustic fields. Moreover, the finite element and boundary element methods are implemented in the context of isogeometric analysis, whereby the geometric accuracy and high order continuity of Kirchhoff-Love shells can be guaranteed and meantime no meshing is necessary. The topology optimization method takes continuous material interpolation functions in the density and bulk modulus, and adopts adjoint variable methods for sensitivity analysis. The reduced order model is constructed based on second-order Arnoldi algorithm combined with Taylor's expansions which eliminate the frequency dependence of the system matrices. Numerical results show that the proposed algorithm can significantly improve the efficiency of broadband topology optimization analysis.

An Experiment-Based Variable Compensation Method to Improve the Geometric Accuracy of Sub-Mm Features Fabricated by Stereolithography (SLA)

In this paper, we present an experimental procedure to enhance the dimensional accuracy of fabrication via stereolithography (SLA) of features at the sub-mm scale. Deviations in sub-mm hemispherical cavity diameters were detected and measured on customized samples by confocal microscopy. The characterization and experimental observations of samples allowed the identification of inaccuracy sources, mainly due to the laser beam scanning strategy and the incomplete removal of uncured liquid resin in post-processing (i.e., IPA washing). As a technology baseline, the measured dimensional errors on cavity diameters were up to −46%. A compensation method was defined and implemented, resulting in relevant improvements in dimensional accuracy. However, measurements on sub-mm cavities having different sizes revealed that a constant compensation parameter (i.e., C = 85, 96, 120 μm) is not fully effective at the sub-mm scale, where average errors remain at −24%, −18.8%, and −16% for compensations equal to 85, 96 and 120 μm, respectively. A further experimental campaign allowed the identification of an effective nonlinear compensation law where the compensation parameter depends on the sub-mm feature size C = f(D). Results show a sharp improvement in dimensional accuracy on sub-mm cavity fabrication, with errors consistently below +8.2%. The proposed method can be extended for the fabrication of any sub-mm features without restrictions on the specific technology implementation.

Springback prediction using point series and deep learning

AbstractOne of the main challenges that prevent wide adoption of Single Point Incremental Forming (SPIF) is the geometric accuracy of the process resulting primarily from the effect of springback. There are various expedients that can be adopted to address this, but one of the most common is tool path correction. The challenge is then how best to predict springback so as to implement tool path correction. It is established that springback, to a large extent, is related to the geometry of the part to be manufactured. The proposed mechanism uses a novel point series representation to capture local geometries that then form a global bank of geometries for general use. Each point series can then be associated with a predicted springback value generated using deep or machine learning. Experiments are reported using a Long Short Term Memory (LSTM) model coupled with a Multilayer Perception Network (MLP), and a Support Vector Machine (SVM) regression model. A best R2, “Coefficient of Determination”, of 0.9181 was obtained indicating that the proposed approach provided a realistic solution to the current limitations of SPIF.