Hundreds Of Projections Research Articles

Machine learning for detection of 3D features using sparse x-ray tomographic reconstruction.

In many inertial confinement fusion (ICF) experiments, the neutron yield and other parameters cannot be completely accounted for with one and two dimensional models. This discrepancy suggests that there are three dimensional effects that may be significant. Sources of these effects include defects in the shells and defects in shell interfaces, the fill tube of the capsule, and the joint feature in double shell targets. Due to their ability to penetrate materials, x rays are used to capture the internal structure of objects. Methods such as computational tomography use x-ray radiographs from hundreds of projections, in order to reconstruct a three dimensional model of the object. In experimental environments, such as the National Ignition Facility and Omega-60, the availability of these views is scarce, and in many cases only consists of a single line of sight. Mathematical reconstruction of a 3D object from sparse views is an ill-posed inverse problem. These types of problems are typically solved by utilizing prior information. Neural networks have been used for the task of 3D reconstruction as they are capable of encoding and leveraging this prior information. We utilize half a dozen, different convolutional neural networks to produce different 3D representations of ICF implosions from the experimental data. Deep supervision is utilized to train a neural network to produce high-resolution reconstructions. These representations are used to track 3D features of the capsules, such as the ablator, inner shell, and the joint between shell hemispheres. Machine learning, supplemented by different priors, is a promising method for 3D reconstructions in ICF and x-ray radiography, in general.

Two applications of number theory to discrete tomography

Tomography is the theory behind scans, e.g. MRI-scans. Most common is continuous tomography where an object is reconstructed from numerous projections. In some cases this is not applicable, because the object changes too quickly or is damaged by making hundreds of projections (by X-rays). In such cases discrete tomography may apply where only few projections are made. The present paper shows how number theory helps to provide insight in the application and structure of discrete tomography.

DeepOrganNet: On-the-Fly Reconstruction and Visualization of 3D / 4D Lung Models from Single-View Projections by Deep Deformation Network.

This paper introduces a deep neural network based method, i.e., DeepOrganNet, to generate and visualize fully high-fidelity 3D / 4D organ geometric models from single-view medical images with complicated background in real time. Traditional 3D / 4D medical image reconstruction requires near hundreds of projections, which cost insufferable computational time and deliver undesirable high imaging / radiation dose to human subjects. Moreover, it always needs further notorious processes to segment or extract the accurate 3D organ models subsequently. The computational time and imaging dose can be reduced by decreasing the number of projections, but the reconstructed image quality is degraded accordingly. To our knowledge, there is no method directly and explicitly reconstructing multiple 3D organ meshes from a single 2D medical grayscale image on the fly. Given single-view 2D medical images, e.g., 3D / 4D-CT projections or X-ray images, our end-to-end DeepOrganNet framework can efficiently and effectively reconstruct 3D / 4D lung models with a variety of geometric shapes by learning the smooth deformation fields from multiple templates based on a trivariate tensor-product deformation technique, leveraging an informative latent descriptor extracted from input 2D images. The proposed method can guarantee to generate high-quality and high-fidelity manifold meshes for 3D / 4D lung models; while, all current deep learning based approaches on the shape reconstruction from a single image cannot. The major contributions of this work are to accurately reconstruct the 3D organ shapes from 2D single-view projection, significantly improve the procedure time to allow on-the-fly visualization, and dramatically reduce the imaging dose for human subjects. Experimental results are evaluated and compared with the traditional reconstruction method and the state-of-the-art in deep learning, by using extensive 3D and 4D examples, including both synthetic phantom and real patient datasets. The efficiency of the proposed method shows that it only needs several milliseconds to generate organ meshes with 10K vertices, which has great potential to be used in real-time image guided radiation therapy (IGRT).

Large-area single-photon-counting CdTe detector for synchrotron radiation computed tomography: adedicated pre-processing procedure.

Large-area CdTe single-photon-counting detectors are becoming more and more attractive in view of low-dose imaging applications due to their high efficiency, low intrinsic noise and absence of a scintillating screen which affects spatial resolution. At present, however, since the dimensions of a single sensor are small (typically a few cm2), multi-module architectures are needed to obtain a large field of view. This requires coping with inter-module gaps and with close-to-edge pixels, which generally show a non-optimal behavior. Moreover, high-Z detectors often show gain variations in time due to charge trapping: this effect is detrimental especially in computed tomography (CT) applications where a single tomographic image requires hundreds of projections continuously acquired in several seconds. This work has been carried out at the SYRMEP beamline of the Elettra synchrotron radiation facility (Trieste, Italy), in the framework of the SYRMA-3D project, which aims to perform the world's first breast-CT clinical study with synchrotron radiation. An ad hoc data pre-processing procedure has been developed for the PIXIRAD-8 CdTe single-photon-counting detector, comprising an array of eight 30.7 mm × 24.8 mm modules tiling a 246 mm × 25 mm sensitive area, which covers the full synchrotron radiation beam. The procedure consists of five building blocks, namely dynamic flat-fielding, gap seaming, dynamic ring removal, projection despeckling and around-gap equalization. Each block is discussed and compared, when existing, with conventional approaches. The effectiveness of the pre-processing is demonstrated for phase-contrast CT images of a human breast specimen. The dynamic nature of the proposed procedure, which provides corrections dependent upon the projection index, allows the effective removal of time-dependent artifacts, preserving the main image features including phase effects.

Sparse-view Reconstruction of Dynamic Processes by Neutron Tomography

As for neutron tomography, hundreds of projections over the range of 0-180 degrees are required to reconstruct the attenuation matrix with the traditional filtered back projection (FBP) algorithm, and the total acquisition time can reach several hours. This poor temporal resolution constrains that neutron tomography is only feasible to investigate static or quasi-static process. Reducing the number of projections is a possible way to improve the temporal resolution, which however highly relies on sparse-view reconstruction algorithms. To assess the feasibility of sparse-view reconstruction for neutron tomography, both simulation and an experiment of water uptake from a piece of wood composite were studied, and the results indicated that temporal resolution of neutron tomography can be improved when combining the Golden Ratio scan strategy with a prior image-constrained sparse-view reconstruction algorithm-PICCS.

WE‐C‐WAB‐09: A Novel Volumetric Imaging Method Using a Sparse Subset of CBCT Projections

Purpose: : Over the preceding decade, onboard volumetric imaging utilizing cone beam CT (CBCT) has become an indispensible tool for image‐guided radiotherapy. A novel method has been developed to deform the planning CT to maximize the spatial congruence between the digitally reconstructed radiographs (DRRs) and CBCT projections. This method maps the planning CT images to the current anatomy using a sparse subset of CBCT projections. Methods: A finite element modeling methodology was developed to automatically generate adaptive meshes for a given region of interest without the need for segmentation. In this methodology, more control points are generated at organs boundaries, special tissues, contour boundaries, or high intensity gradients. The displacement of boundaries and important features is directly represented by the displacement of control points, instead of interpolation on regular grids. A splatting method was developed to generate DRRs of the deformed CT. These DRRs were compared with corresponding 2D projections from CBCT scans; as a Result, the displacement vector fields on the CT were optimized iteratively to obtain a final deformed CT image. A digital phantom was used to evaluate this method. Results: Typically 60 projections were needed to reconstruct a set of 3D images while several hundreds of projections were required for a conventional CBCT reconstruction. The feature‐based meshing method led to better results than either regular orthogonal or regular tetrahedral meshes. The image intensity results of the feature‐based meshing method were nearly identical to those of the voxel‐based method (normalized cross correlation efficient (NCC) ∼ 0.9858 vs 0.9872), while the feature‐based meshing method led to much better consistency of displacement vector fields relative to ground truth than voxel‐based method (NCC ∼ 0.8265 vs 0.6261). Conclusion: This feature‐based meshing method is capable of reconstructing volumetric images with significantly fewer projections while retaining the image quality of planning CT. This research is supported by CPRIT Individual Investigator Award RP110329.

Radiothérapie guidée par la tomographie conique ( cone beam computed tomography) : mise en œuvre et applications cliniques

The kV cone beam CT (CBCT) consists of an X-ray tube and a flat panel detector placed perpendicularly to the treatment beam, allowing the acquisition of hundreds of projections in one rotation of the gantry about the patient. Available in all new linear accelerators, the CBCT provides volumetric imaging in treatment position proving the realization of image- and dose-guided radiotherapy (IGRT and DGRT). The clinical indications correspond to mobile tumours irradiating with high precision required techniques, such as stereotactic, hypofractionated or high dose radiotherapy. The clinical experience is still very limited and concerns mainly prostate, head and neck and lung tumours. The registration and treatment protocols are briefly described. Quality control and training are major issues. CBCT based IGRT is a new technique which needs to be optimized. However, it should provide significant clinical benefit in combination with intensity modulated radiotherapy and new imaging modalities for target delineation.

Adhesive force of a single gecko foot-hair.

Geckos are exceptional in their ability to climb rapidly up smooth vertical surfaces. Microscopy has shown that a gecko's foot has nearly five hundred thousand keratinous hairs or setae. Each 30-130 microm long seta is only one-tenth the diameter of a human hair and contains hundreds of projections terminating in 0.2-0.5 microm spatula-shaped structures. After nearly a century of anatomical description, here we report the first direct measurements of single setal force by using a two-dimensional micro-electromechanical systems force sensor and a wire as a force gauge. Measurements revealed that a seta is ten times more effective at adhesion than predicted from maximal estimates on whole animals. Adhesive force values support the hypothesis that individual seta operate by van der Waals forces. The gecko's peculiar behaviour of toe uncurling and peeling led us to discover two aspects of setal function which increase their effectiveness. A unique macroscopic orientation and preloading of the seta increased attachment force 600-fold above that of frictional measurements of the material. Suitably orientated setae reduced the forces necessary to peel the toe by simply detaching above a critical angle with the substratum.