2D Projection Images Research Articles

Design of an experimental approach based on the contrast-to-noise ratio measurement for X-ray computed tomography parameters optimization applied to a carbon fiber-reinforced polymer materials scan

This paper proposes a systematic approach for the optimization of scan parameters for industrial X-ray computed tomography (XCT), as regard its specific application as diagnostic tool on carbon fiber-reinforced polymer materials (CFRP). This procedure allows the system operator to overcome suboptimal scan results due to a subjective choice of XCT parameters. In this work, XCT scan quality has been measured in terms of contrast-to-noise ratio (CNR) metric, by calculating it on collected XCT 2D projection images. A four-factor five-level central composite design (CCD) was implemented to perform experiments, and a quadratic polynomial model was chosen to describe the effects of XCT scanning parameters combination on CNR measurement and finally to predict optimal results. Analysis of variance was carried out to evaluate the significance of the model on the response, reporting a R2 of 97.1%, and response surface analyses were also performed for CNR optimization purposes. In order to validate the CCD results, different XCT parameters combinations, coming from the CCD analysis on projection images, were used to run different scans, and, as result, the optimal CNR predicted from the model was also reflected in an optimal CNR measured on the reconstructed XCT images.

Fast, automated, real-time 3D passive balloon catheter tracking during MRI-guided cardiac catheterization using orthogonal projection imaging and real-time image-based catheter detection.

MRI-guidance of cardiac catheterization is currently performed using one or multiple 2D imaging planes, which may be suboptimal for catheter navigation, especially in patients with complex anatomies. The purpose of the work was to develop a robust real-time 3D catheter tracking method and 3D visualization strategy for improved MRI-guidance of cardiac catheterization procedures. A fast 3D tracking technique was developed using continuous acquisition of two orthogonal 2D-projection images. Each projection corresponds to a gradient echo stack of slices with only the central k-space lines being collected for each slice. To enhance catheter contrast, a saturation pulse is added ahead of the projection pair. An offline image processing algorithm was developed to identify the 2D coordinates of the balloon in each projection image and to estimate its corresponding 3D coordinates. Post-processing includes background signal suppression using an atlas of background 2D-projection images. 3D visualization of the catheter and anatomy is proposed using three live sagittal, coronal, and axial (MPR) views and 3D rendering. The technique was tested in a subset of a catheterization step in three patients undergoing MRI-guided cardiac catheterization using a passive balloon catheter. The extraction of the catheter balloon 3D coordinates was successful in all patients and for the majority of time-points (accuracy >96%). This tracking method enabled a novel 3D visualization strategy for passive balloon catheter, providing enhanced anatomical context during catheter navigation. The proposed tracking strategy shows promise for robust tracking of passive balloon catheter and may enable enhanced visualization during MRI-guided cardiac catheterization.

Wave-PCT: Wavelet point cloud transformer for point cloud quality assessment

Point cloud representation for real-world objects has seen a surge in interest recently, finding widespread applications in augmented reality, virtual reality, and autonomous driving. However, the process of acquiring, compressing, transmitting, and processing point cloud data often introduces unwanted distortions, highlighting the need for effective no-reference quality assessment methods for distorted point clouds. In this paper, we propose a Wavelet Point Cloud Transformer (Wave-PCT) method to assess the quality of point clouds without reference data. We start by performing patch sampling on a source point cloud, generating an extensive set of patches from the point cloud. These point cloud patches and their 2D projection images are processed using wavelets to produce multiscale local spectral features; moreover, we also extract global features of these patches using Pointnet++. Finally, a self-attention module is performed to fuse these local and global features, resulting in a distortion-sensitive quality score metric. We evaluate the Wave-PCT method on several point cloud quality assessment datasets using various metrics, and the experimental results indicate that the proposed framework exhibits outstanding and reliable performance when compared to various point cloud quality assessment methods.

Simultaneous 2D Projection and 3D Topographic Imaging of Gas-Dependent Dynamics of Catalytic Nanoparticles.

Catalyst deactivation through pathways such as sintering of nanoparticles and degradation of the support is a critical factor when designing high-performance catalysts. Here, structural changes of supported nanoparticle catalysts are investigated in controlled gas environments (O2, H2O, and H2) at different temperatures by imaging simultaneously the nanoparticle structures in 2D projection and the 3D surface-sensitive topography. Platinum nanoparticles on carbon support as a model system are imaged in an environmental transmission electron microscope (ETEM), with concurrent acquisition of high-angle annular dark field scanning TEM (HAADF-STEM) and secondary electron (SE) images. Particle migration and coalescence occurs and shows gas-dependent kinetics, with nanoparticles moving across and through the support during and after coalescence. The temperature required for motion is lower in O2 than in H2O and H2, explained through the nature of the gas/nanoparticle interactions. In O2 and H2, the carbon support degrades by trench formation along migration pathways, and the particles move continuously, indicating a chemical reaction between gas and support. In H2O gas, motion is more discontinuous and oriented particle attachment occurs, as expected from theoretical predictions. These results suggest that multimodal imaging in ETEM that combines HAADF-STEM and SE data provides comprehensive information regarding catalyst dynamics and degradation mechanisms.

Characterization of structure and mixing in nanoparticle hetero-aggregates using convolutional neural networks: 3D-reconstruction versus 2D-projection

Structural and chemical characterization of nanomaterials provides important information for understanding their functional properties. Nanomaterials with characteristic structure sizes in the nanometer range can be characterized by scanning transmission electron microscopy (STEM). In conventional STEM, two-dimensional (2D) projection images of the samples are acquired, information about the third dimension is lost. This drawback can be overcome by STEM tomography, where the three-dimensional (3D) structure is reconstructed from a series of projection images acquired using various projection directions. However, 3D measurements are expensive with respect to acquisition and evaluation time. Furthermore, the method is hardly applicable to beam-sensitive materials, i.e. samples that degrade under the electron beam. For this reason, it is desirable to know whether sufficient information on structural and chemical information can be extracted from 2D-projection measurements. In the present work, a comparison between 3D-reconstruction and 2D-projection characterization of structure and mixing in nanoparticle hetero-aggregates is provided. To this end, convolutional neural networks are trained in 2D and 3D to extract particle positions and material types from the simulated or experimental measurement. Results are used to evaluate structure, particle size distributions, hetero-aggregate compositions and mixing of particles quantitatively and to find an answer to the question, whether an expensive 3D characterization is required for this material system for future characterizations.

A Non-destructive Evaluation of Microstructural Analysis in Sn-Ag-Cu Solder Joint by Synchrotron X-Ray Radiation Tomography

This paper demonstrates a non-destructive technique to evaluate the internal microstructure in the Sn-Ag-Cu (SAC) solder joint through synchrotron X-ray radiation tomography. Synchrotron X-ray tomography is increasingly utilized for characterizing the internal microstructure of materials in 3D images. A 3D model is reconstructed from a set of 2D projection images taken from different angles and angular position during the sample rotation, thus it could provide a more comprehensive description of the microstructure of an alloy compared to 2D images. In this paper, it is successfully observed and evaluated the internal microstructure of a 900 μm solder joint sample. The key principles and methods of synchrotron X-ray tomography are briefly described. Examples of quantitative and qualitative assessments on the grain refinement effect of Mg addition to SAC35 solder joint are also presented in this paper.

Topology observing 3D device reconstruction from continuous-sweep limited angle fluoroscopy.

Minimally invasive procedures usually require navigating a microcatheter and guidewire through endoluminal structures such as blood vessels and airways to sites of the disease. For numerous clinical applications, two-dimensional (2D) fluoroscopy is the primary modality used for real-time image guidance during navigation. However, 2D imaging can pose challenges for navigation in complex structures. Real-time 3D visualization of devices within the anatomic context could provide considerable benefits for these procedures. Continuous-sweep limited angle (CLA) fluoroscopy has recently been proposed to provide a compromise between conventional rotational 3D acquisitions and real-timefluoroscopy. The purpose of this work was to develop and evaluate a noniterative 3D device reconstruction approach for CLA fluoroscopy acquisitions, which takes into account endoluminal topology to avoid impossible paths between disconnectedbranches. The algorithm relies on a static 3D roadmap (RM) of vessels or airways, which may be generated from conventional cone beam CT (CBCT) acquisitions prior to navigation. The RM is converted to a graph representation describing its topology. During catheter navigation, the device is segmented from the live 2D projection images using a deep learning approach from which the centerlines are extracted. Rays from the focal spot to detector pixels representing 2D device points are identified and intersections with the RM are computed. Based on the RM graph, a subset of line segments is selected as candidates to exclude device paths through disconnected branches of the RM. Depth localization for each point along the device is then performed by finding the point closest to the previous 3D reconstruction along the candidate segments. This process is repeated as the projection angle changes for each CLA image frame. The approach was evaluated in a phantom study in which a catheter and guidewire were navigated along five pathways within a complex vessel phantom. The result was compared to static cCBCT acquisitions of the device in the finalposition. The average root mean squared 3D distance between CLA reconstruction and reference centerline was mm. The Euclidean distance at the device tip was mm. The correct pathway was identified during reconstruction in of frames ( ). The percentage of 3D device points reconstructed inside the 3D roadmap was with an average distance of mm between the device points outside the roadmap and the nearest point within theroadmap. This study demonstrates the feasibility of reconstructing curvilinear devices such as catheters and guidewires during endoluminal procedures including intravascular and transbronchial interventions using a noniterative reconstruction approach for CLA fluoroscopy. This approach could improve device navigation in cases where the structure of vessels or airways is complex and includes overlappingbranches.

Broad Elevation Projection Super-Resolution Ultrasound (BEP-SRUS) Imaging With a 1-D Unfocused Linear Array.

Super-resolution ultrasound (SRUS) through localizing spatially isolated microbubbles (MBs) has been demonstrated to overcome the wave diffraction limit and reveal the microvascular structure and flow information at the microscopic scale. However, 3-D SRUS imaging remains a challenge due to the fabrication and computational complexity of 2-D matrix array probes. Inspired by X-ray radiography which can present information within a volume in a single projection image with much simpler hardware than X-ray computerized tomography (CT), this study investigates the feasibility of broad elevation projection super-resolution (BEP-SR) ultrasound using a 1-D unfocused linear array. Both simulation and in vitro experiments were conducted on 3-D microvessel phantoms. In vivo demonstration was done on the Rabbit kidney. Data from a 1-D linear array with and without an elevational focus were synthesized by summing up row signals acquired from a 2-D matrix array with and without delays. A full 3-D reconstruction was also generated as the reference, using the same data of the 2-D matrix array but without summing row signals. Results show that using an unfocused 1-D array probe, BEP-SR can capture significantly more information within a volume in both vascular structure and flow velocity than the conventional 1-D elevational-focused probe. Compared with the 2-D projection image of the full 3-D SRUS results using the 2-D array probe with the same aperture size, the 2-D projection SRUS image of BEP-SR has similar volume coverage, using 32 folds fewer independent elements. This study demonstrates BEP-SR's ability of high-resolution imaging of microvascular structures and flow velocity within a 3-D volume at significantly reduced costs. The proposed BEP method could significantly benefit the clinical translation of the SRUS imaging technique by making it more affordable and repeatable.

Deep-learning-assisted snapshot optical tomography for microscopic volume prediction: a simulation study.

In this simulation study, we demonstrate fast-yet-accurate volume measurement of microscopic objects by combining snapshot optical tomography and deep learning. Snapshot optical tomography simultaneously collects a multitude of projection images and thus can perform 3D imaging in a single snapshot. However, as with other wide-field microscopy techniques, it suffers from the missing-cone problem, which can seriously degrade the quality of 3D reconstruction. We use deep learning to generate a volume prediction from 2D projection images bypassing the 3D reconstruction.

Automatic 3D Registration of Dental CBCT and Face Scan Data Using 2D Projection Images

Automatic 3D Registration of Dental CBCT and Face Scan Data Using 2D Projection Images

The CathEye: A Forward-Looking Ultrasound Catheter for Image-Guided Cardiovascular Procedures.

Catheter based procedures are typically guided by X-Ray, which suffers from low soft tissue contrast and only provides 2D projection images of a 3D volume. Intravascular ultrasound (IVUS) can serve as a complementary imaging technique. Forward viewing catheters are useful for visualizing obstructions along the path of the catheter. The CathEye system mechanically steers a single-element transducer to generate a forward-looking surface reconstruction from an irregularly spaced 2-D scan pattern. The steerable catheter leverages an expandable frame with cables to manipulate the distal end independently of vessel tortuosity. The tip position is estimated by measuring the cable displacements and used to create surface reconstructions of the imaging workspace with the single-element transducer. CathEye's imaging capabilities were tested with an agar phantom and an ex vivo chronic total occlusion (CTO) sample while the catheter was confined to various tortuous paths. The CathEye maintained similar scan patterns regardless of path tortuosity and was able to recreate major features of the imaging targets, such as holes and extrusions. The feasibility of forward-looking IVUS with the CathEye is demonstrated in this study. The CathEye mechanism can be applied to other imaging modalities with field-of-view (FOV) limitations and represents the basis for an interventional device fully integrated with image guidance.

Patient-specific in silico 3D coronary model in cardiac catheterisation laboratories.

Coronary artery disease is caused by the buildup of atherosclerotic plaque in the coronary arteries, affecting the blood supply to the heart, one of the leading causes of death around the world. X-ray coronary angiography is the most common procedure for diagnosing coronary artery disease, which uses contrast material and x-rays to observe vascular lesions. With this type of procedure, blood flow in coronary arteries is viewed in real-time, making it possible to detect stenoses precisely and control percutaneous coronary interventions and stent insertions. Angiograms of coronary arteries are used to plan the necessary revascularisation procedures based on the calculation of occlusions and the affected segments. However, their interpretation in cardiac catheterisation laboratories presently relies on sequentially evaluating multiple 2D image projections, which limits measuring lesion severity, identifying the true shape of vessels, and analysing quantitative data. In silico modelling, which involves computational simulations of patient-specific data, can revolutionise interventional cardiology by providing valuable insights and optimising treatment methods. This paper explores the challenges and future directions associated with applying patient-specific in silico models in catheterisation laboratories. We discuss the implications of the lack of patient-specific in silico models and how their absence hinders the ability to accurately predict and assess the behaviour of individual patients during interventional procedures. Then, we introduce the different components of a typical patient-specific in silico model and explore the potential future directions to bridge this gap and promote the development and utilisation of patient-specific in silico models in the catheterisation laboratories.

Visualization and quantization of 3D retinal vessels in OCTA images.

Optical coherence tomography angiography (OCTA) has been increasingly used in the analysis of ophthalmic diseases in recent years. Automatic vessel segmentation in 2D OCTA projection images is commonly used in clinical practice. However, OCTA provides a 3D volume of the retinal blood vessels with rich spatial distribution information, and it is incomplete to segment retinal vessels only in 2D projection images. Here, considering that it is difficult to manually label 3D vessels, we introduce a 3D vessel segmentation and reconstruction method for OCTA images with only 2D vessel labels. We implemented 3D vessel segmentation in the OCTA volume using a specially trained 2D vessel segmentation model. The 3D vessel segmentation results are further used to calculate 3D vessel parameters and perform 3D reconstruction. The experimental results on the public dataset OCTA-500 demonstrate that 3D vessel parameters have higher sensitivity to vascular alteration than 2D vessel parameters, which makes it meaningful for clinical analysis. The 3D vessel reconstruction provides vascular visualization in different retinal layers that can be used to monitor the development of retinal diseases. Finally, we also illustrate the use of 3D reconstruction results to determine the relationship between the location of arteries and veins.

Evaluation of Electron Tomography Capabilities for Shale Imaging.

Despite the advantageous resolution of electron tomography (ET), reconstruction of three-dimensional (3D) images from multiple two-dimensional (2D) projections presents several challenges, including small signal-to-noise ratios, and a limited projection range. This study evaluates the capabilities of ET for thin sections of shale, a complex nanoporous medium. A numerical phantom with 1.24 nm pixel size is constructed based on the tomographic reconstruction of a Barnett shale. A dataset of 2D projection images is numerically generated from the 3D phantom and studied over a range of conditions. First, common reconstruction techniques are used to reconstruct the shale structure. The reconstruction uncertainty is quantified by comparing overall values of storage and transport metrics, as well as the misclassification of pore voxels compared to the phantom. We then select the most robust reconstruction technique and we vary the acquisition conditions to quantify the effect of artifacts. We find a strong agreement for large pores over the different acquisition workflows, while a wider variability exists for nanometer-scale features. The limited projection range and reconstruction are identified as the main experimental bottlenecks, thereby suggesting that sample thinning, advanced holders, and advanced reconstruction algorithms offer opportunities for improvement.

Systematic review of various 3D volume construction algorithms adopted in isocentric C-arm devices

C-arm systems are well established in clinical routines which produces two- dimensional (2D) projection images, which are not sufficient to provide the depth information required by surgeons during interventional procedures. Due to this, the procedural time and radiation dosage are significantly higher which leads to increased procedural cost. In order to extract depth information from 2D projection images acquired with stepwise rotation between 0 to 180 degrees, various 3D volume construction algorithms are developed. The purpose of this paper is to systematically review the various 3D volume construction algorithms in order to develop a cost-effective solution to be incorporated in the iso-centric C-arm machines without 3D volume construction algorithms. The proposed review process is done in the following aspects: i. State-of-the-art 3D volume construction algorithms ii. Comparing the different aspects of commercially available iso- centric C-arm machines incorporated with 3D volume construction algorithms. The impact of various 3D construction algorithms in improving different aspects such as image quality, resolution, procedure time, radiation dose, and accuracy of the final 3D model is discussed. Based on the findings we recommend a procedure to develop and validate a 3D volume construction algorithm. 3D volume construction from C-arm images helps to construct Multi Planar Reconstruction (MPR) views during interventional procedures which will provide in-depth information and minimize the procedure time, cost, and dosage to the patient.

Three-dimensional Anatomy of the Velopharyngeal Muscles in the Cleft Palate.

We have used micro-computed tomography (CT) to elucidate the relationship between the muscle fibers in specimens with cleft palate. These findings could be useful for muscle reconstruction in cleft palate repair and to better understand cleft palate speech. Cadaveric anatomical study. This study included three specimens with cleft palate. The specimens were stained with phosphomolybdic acid and scanned by Micro-CT. The anatomy of the muscles. Using 2D projection images and 3D reconstruction models, subtle anatomical structures could be observed in the muscles. The attachment of the levator veli palatini (LVP) was not at the posterior edge of the hard palate or palatine aponeurosis (PA), but at the anterior 21.71-44.2% of the cleft edge. The palatopharyngeal (PP) was composed of two bundles: inferior and superior heads, which clasped the LVP. The uvularis was unevenly distributed, and located on both sides of the cleft edge, originating at the edge. The palatoglossus, superior constrictor of pharynx and anatomical structure around the pterygoid hamulus, were normal. The PA, PP and LVP were attached to the cleft edge from front to back, in that order. The position of the uvularis was not fixed. With the help of Micro-CT technology, detailed anatomical features and the relationship between muscles could be visualized. In the specimen with cleft palate, muscles in the soft palate were associated with the pharyngeal muscles, which formed the 3D "velopharyngeal muscles complex." These findings provide anatomical evidence for muscle reconstruction in cleft palate repair.

A general image misalignment correction method for tomography experiments

Tomography experiments generate three-dimensional (3D) reconstructed slices from a series of two-dimensional (2D) projection images. However, the mechanical system generates joint offsets that result in unaligned 2D projections. This misalignment affects the reconstructed images and reduces their actual spatial resolution. In this study, we present a novel method called outer contour-based misalignment correction (OCMC) for correcting image misalignments in tomography. We use the sample's outer contour structure as auxiliary information to estimate the extent of misalignment in each image. This method is generic and can be used with various tomography imaging techniques. We validated our method with five datasets collected from different samples and across various tomography techniques. The OCMC method demonstrated significant advantages in terms alignment accuracy and time efficiency. As an end-to-end correction method, OCMC can be easily integrated into an online tomography data processing pipeline and facilitate feedback control in future synchrotron tomography experiments.

Monte Carlo model of a prototype flat-panel detector for multi-energy applications in radiotherapy.

The incorporation of multi-energy capabilities into radiotherapy flat-panel detectors offers advantages including enhanced soft tissue visualization by reduction of signal from overlapping anatomy such as bone in 2D image projections; creation of virtual monoenergetic images for 3D contrast enhancement, metal artefact reduction and direct acquisition of relative electron density. A novel dual-layer on-board imager offering dual energy processing capabilities is being designed. As opposed to other dual-energy implementation techniques which require separate acquisition with two different x-ray spectra, the dual-layer detector design enables simultaneous acquisition of high and low energy images with a single exposure. A computational framework is required to optimize the design parameters and evaluate detector performance for specific clinical applications. In this study, we report on the development of a Monte Carlo (MC) model of the imager including model validation. The stack-up of the dual-layer imager (DLI) was implemented in GEANT4 Application for Tomographic Emission (GATE). The DLI model has an active area of 43×43 cm2 , with top and bottom Cesium Iodide (CsI) scintillators of 600 and 800μm thickness, respectively. Measurement of spatial resolution and imaging of dedicated multi-material dual-energy (DE) phantoms were used to validate the model. The modulation transfer function (MTF) of the detector was calculated for a 120 kVp x-ray spectrum using a 0.5mm thick tantalum edge rotated by 2.5o . For imaging validation, the DE phantom was imaged using a 140 kVp x-ray spectrum. For both validation simulations, corresponding measurements were done using an initial prototype of the imager. Agreement between simulations and measurement was assessed using normalized root mean square error (NRMSE) and 1D profile difference for the MTF and phantom images respectively. Further comparison between measurement and simulation was made using virtual monoenergetic images (VMIs) generated from basis material images derived using precomputed look-up tables. The MTF of the bottom layer of the dual-layer model shows values decreasing more quickly with spatial frequency, compared to the top layer, due to the thicker bottom scintillator thickness and scatter from the top layer. A comparison with measurement shows NRMSE of 0.013 and 0.015 as well as identical MTF50 of 0.8 mm1 and 1.0 mm1 for the top and bottom layer respectively. For the DE imaging of the DE-phantom, although a maximum deviation of 3.3% is observed for the 10mm aluminum and Teflon inserts at the top layer, the agreement for all other inserts is less than 2.2% of the measured value at both layers. Material decomposition of simulated scatter-free DE images gives an average accuracy in PMMA and aluminum composition of 4.9% and 10.3% for 11-30mm PMMA and 1-10mm aluminum objects respectively. A comparison of decomposed values using scatter containing measured and simulated DE images shows good agreement within statistical uncertainty. Validation using both MTF and phantom imaging shows good agreement between simulation and measurements. With the present configuration of the digital prototype, the model can generate material decomposed images and virtual monoenergetic images.

Learning View-Based Graph Convolutional Network for Multi-View 3D Shape Analysis.

View-based approach that recognizes 3D shape through its projected 2D images has achieved state-of-the-art results for 3D shape recognition. The major challenges are how to aggregate multi-view features and deal with 3D shapes in arbitrary poses. We propose two versions of a novel view-based Graph Convolutional Network, dubbed view-GCN and view-GCN++, to recognize 3D shape based on graph representation of multiple views. We first construct view-graph with multiple views as graph nodes, then design two graph convolutional networks over the view-graph to hierarchically learn discriminative shape descriptor considering relations of multiple views. Specifically, view-GCN is a hierarchical network based on two pivotal operations, i.e., feature transform based on local positional and non-local graph convolution, and graph coarsening based on a selective view-sampling operation. To deal with rotation sensitivity, we further propose view-GCN++ with local attentional graph convolution operation and rotation robust view-sampling operation for graph coarsening. By these designs, view-GCN++ achieves invariance to transformations under the finite subgroup of rotation group SO(3). Extensive experiments on benchmark datasets (i.e., ModelNet40, ScanObjectNN, RGBD and ShapeNet Core55) show that view-GCN and view-GCN++ achieve state-of-the-art results for 3D shape classification and retrieval tasks under aligned and rotated settings.

GRAPE: A Stochastic Geometrical 3D Model for Aggregates of Particles With Tunable 2D Morphological Projected Properties

The main goal of this paper is to propose a method for the 3D morphological characterization of compact aggregates using 2D image analysis. The problem at hand is the 3D morphometric characterization of latex nanoparticle aggregates. The only available information is 2D projection images of these aggregates, one projection per aggregate. In this context, a method to estimate the 3D morphological characteristics of an aggregate such as the Volume, Surface Area or Solidity from a single projection is proposed. This method is based on a stochastic geometric model called GRAPE (Geometrical Random Aggregation of Particles Emulation) and requires some strong assumptions, and in particular prior estimation of the volume. The model is based on an iterative packing of spheres of identical radii. For each iteration, a fitting function allows to reach objectives corresponding to the desired 2D properties (Area, Perimeter, Aspect Ratio, ...). In order to implement the method, an optimization process must be performed on two parameters of the model: the radius of the elementary particles r and an overlapping distance di. As a validation, this process will be applied to synthetic aggregates, themselves generated from the GRAPE model, then to a population of 104 synthetic aggregates, and finally to 3D printed aggregates whose 3D morphological properties are known thanks to an STL file, and whose projected images have been produced using a morphogranulometer. The results obtained show an excellent approximation of 2D properties by the GRAPE model, and very good results for 3D properties, with less than 5% error on average and less than 2% error in most cases.