X-ray Projection Images Research Articles

A new method to assess the performance of anti-scatter grids in x-ray projection imaging.

This work proposes a new method to assess the performance of radiographic anti-scatter grids (ASGs) without the use of a narrow primary beam, which is difficult to achieve. Three general purpose ASGs were evaluated, two marketed ASGs and a low frequency and high ratio prototype ASG with molybdenum lamellae. A range of high scatter x-ray beams were used in a standardized geometry, with energies ranging from 60 kV to 121 kV, for five beam sizes between 10 x 10 and 30 x 30 cm2. The scatter fraction (SF) of each beam was measured in the image plane with and without ASG using the lead beam stop method with an extrapolation function derived from the scatter point spread function (PSF). The primary, scatter and total transmissions of the three ASGs measured for the different x-ray beams allowed the calculation of the grid factor, contrast improvement factor and detective quantum efficiency (DQE)as functions of the input SF. The results obtained for the three ASGs are consistent with those obtained with the standard narrow-beam method and data published in the literature, confirmed the prime importance of the ASG primary transmission and revealed important variations in ASG performance, especially as a function of the input SF and beam size. The break-even input SFs at which the imaging system efficiency was improved by the ASGranged between 0.18 and 0.52 for the different ASGs and beam characteristics. The methodis proposed as an alternative to current ASG characterization techniques.

Computer diffraction tomography: a comparative analysis of the use of controlled and wavelet filters for image processing

The paper provides digital processing of 2D X-ray projection images of a Coulomb-type point defect in a Si(111) crystal recorded by a detector against the background of statistical Gaussian noise. A managed filter and a wavelet filter with a 4th-order Daubechies function are used. The efficiency of filtering 2D images is determined by calculating the relative quadratic deviations of the intensities of filtered and reference (noiseless) 2D images averaged over all points. A comparison of the calculated values of the relative deviations of the intensities shows that the considered methods work quite well and both, in principle, can be effectively used in practice for noise processing of X-ray diffraction images used for 3D reconstruction of nanoscale defects of crystal structures.

Robust Real-Time Cancer Tracking via Dual-Panel X-Ray Images for Precision Radiotherapy.

Respiratory-induced tumor motion presents a critical challenge in lung cancer radiotherapy, potentially impacting treatment precision and efficacy. This study introduces an innovative, deep learning-based approach for real-time, markerless lung tumor tracking utilizing orthogonal X-ray projection images. It incorporates three key components: (1) a sophisticated data augmentation technique combining a hybrid deformable model with 3D thin-plate spline transformation, (2) a state-of-the-art Transformer-based segmentation network for precise tumor boundary delineation, and (3) a CNN regression network for accurate 3D tumor position estimation. We rigorously evaluated this approach using both patient data from The Cancer Imaging Archive and dynamic thorax phantom data, assessing performance across various noise levels and comparing it with current leading algorithms. For TCIA patient data, the average DSC and HD95 values were 0.9789 and 1.8423 mm, respectively, with an average centroid localization deviation of 0.5441 mm. On CIRS phantoms, DSCs were 0.9671 (large tumor) and 0.9438 (small tumor) with corresponding HD95 values of 1.8178 mm and 1.9679 mm. The 3D centroid localization accuracy was consistently below 0.33 mm. The processing time averaged 90 ms/frame. Even under high noise conditions (S2 = 25), errors for all data remained within 1 mm with tracking success rates mostly at 100%. In conclusion, the proposed markerless tracking method demonstrates superior accuracy, noise robustness, and real-time performance for lung tumor localization during radiotherapy. Its potential to enhance treatment precision, especially for small tumors, represents a significant step toward improving radiotherapy efficacy and personalizing cancer treatment.

Synthesis of low-dimensional metal halide CsAgCl2 for X-ray scintillation applications

Low-dimensional metal halides, which possess efficient luminescent properties, have garnered significant interest in the fields of optoelectronics and radiation detection. This study introduces novel lead-free silver-based metal halide material, CsAgCl2 microcrystals (MCs). These MCs have a one-dimensional atomic chain structure and a unique [AgCl5]4- tetragonal shared triangular configuration. CsAgCl2 MCs exhibit excellent performance as X-ray scintillators due to their bright broadband yellow emission and fast decay time in the microsecond range. The large Stokes shift of 350 nm is produced by self-trapped exciton emission. In X-ray scintillation applications, CsAgCl2 MCs demonstrate a high light yield of 13280 photons/MeV and a wide range of linear scintillation response. It is noteworthy that the CsAgCl2 MCs maintain good stability under both atmospheric conditions and continuous X-ray irradiation. The sample was used in an X-ray imaging screen, which clearly presented the X-ray projection image of a wooden stick. As a result, this research not only contributes to the collection of lead-free metal halide scintillators but also indicates that CsAgCl2 MCs have a wide range of future applications and potential in areas such as medical imaging, scientific research, and diagnostic and detection technologies.

Fast and efficient hard X-ray projection imaging below 10 nm resolution.

High-resolution X-ray imaging of noncrystalline objects is often achieved through the approach of scanning coherent diffractive imaging known as ptychography. The imaging resolution is usually limited by the scattering properties of the sample, where weak diffraction signals at the highest scattering angles compete with parasitic scattering. Here, we demonstrate that X-ray multilayer Laue lenses with a high numerical aperture (NA) can be used to create a strong reference beam that holographically boosts weak scattering from the sample over a large range of scattering angles, enabling high-resolution imaging that is tolerant of such background. An imaging resolution of sub-10 nm was achieved at a photon energy of 17.4 keV with lenses of 0.014 NA from a Siemens star test object and a sample of hierarchical nanoporous gold, recording projection holograms at an effective magnification of more than 30,000 directly on a pixel-array detector. A numerical study compared this approach to low-NA far-field ptychography, indicating significant advantages for using high-NA lenses in the presence of background noise. This imaging modality is particularly fast and efficient at recording high-resolution transmission phase-contrast images over large fields of view in a facile manner.

Image up-sampling method for dual-radiation nondestructive cargo inspection system

In this paper, an image up-sampling method for a dual-radiation nondestructive cargo inspection system, that uses x-ray and neutron, is proposed. The inspection system is assumed to have a larger neutron detector pixel size than that of x-ray detector pixel, which is usually the case to address the lower neutron source flux and the lower detector efficiency than those in the x-ray imaging system. The proposed technique adopts a cluster map of an x-ray projection image with high spatial resolution to up-sample the corresponding neutron projection image with low spatial resolution. The cluster map is acquired by use of a modified k-means clustering algorithm. The up-sampling process is formulated by a linear least-square problem of mapping the neutron projection image onto the cluster map. Consequently, the neutron projection image would have the same dimension with the x-ray projection image array allowing R-value based material decomposition. We have conducted a numerical study using GATE simulation to demonstrate the efficacy of the proposed method. The proposed method was compared with a linear interpolation method and also with an up-sampling technique that does not use the cluster map. The outperformance of the proposed method over those reference techniques has been successfully presented.

Identification of Root Canal Morphology in Fused-rooted Mandibular Second Molars From X-ray Images Based on Deep Learning

Introduction: Understanding the intricate anatomical morphology of fused-rooted mandibular second molars (MSMs) is essential for root canal treatment. The present study utilized a deep learning approach to identify the three-dimensional root canal morphology of MSMs from two-dimensional X-ray images. MethodsA total of 271 fused-rooted MSMs were included in the study. Micro-computed tomography reconstruction images and two-dimensional X-ray projection images were obtained. The ground truth of three-dimensional root canal morphology was determined through micro-computed tomography images, which were classified into merging, symmetrical, and asymmetrical types. To amplify the X-ray image dataset, traditional augmentation techniques from the python package Augmentor and a multiangle projection method were employed. Identification of root canal morphology was conducted using the pretrained VGG19, ResNet18, ResNet50, and EfficientNet-b5 on X-ray images. The classification results from convolutional neural networks (CNNs) were then compared with those performed by endodontic residents. ResultsThe multiangle projection augmentation method outperformed the traditional approach in all CNNs except for EfficientNet-b5. ResNet18 combined with the multiangle projection method outperformed all other combinations, with an overall accuracy of 79.25%. In specific classifications, accuracies of 81.13%, 86.79%, and 90.57% were achieved for merging, symmetrical, and asymmetrical types, respectively. Notably, CNNs surpassed endodontic residents in classification performance; the average accuracy for endodontic residents was only 60.38% (P < .05). ConclusionsCNNs were more effective than endodontic residents in identifying the three-dimensional root canal morphology of MSMs. The result indicates that CNNs possess the capacity to employ two-dimensional images effectively in aiding three-dimensional diagnoses.

Initial validation of a diaphragm tracking system for multiple breath-hold volumetric modulated arc therapy of abdominal tumors: A phantom study.

The breath-hold radiotherapy has been increasingly used to mitigate interfractional and intrafractional breathing impact on treatment planning and beam delivery. Previous techniques include body surface measurements or radiopaque metal markers, each having known disadvantages. We recently proposed a new markerless technique without the disadvantages, where diaphragm was registered between DRR and fluoroscopic x-ray projection images every 180ms during VMAT delivery. An initial validation of the proposed diaphragm tracking system (DiaTrak) was performed using a chest phantom to evaluate its characteristics. Diaphragm registration was performed between DRR and projection streaming kV x-ray images of a chest phantom during VMAT delivery. Streaming data including the projection images and the beam angles were transferred from a linac system to an external PC, where the diaphragm registration accuracy and beam-off latency were measured based on image cross correlation between the DRR and the projection images every 180ms. It was shown that the average of the beam-off latency was 249.5ms and the average of the diaphragm registration error was 0.84mm CONCLUSIONS: Initial validation of the proposed DiaTrak system for multiple breath-hold VMAT of abdominal tumors has been successfully completed with a chest phantom. The resulting beam-off latency and the diaphragm registration error were regarded clinically acceptable.

Volumetric tumor tracking from a single cone-beam X-ray projection image enabled by deep learning

Radiotherapy serves as a pivotal treatment modality for malignant tumors. However, the accuracy of radiotherapy is significantly compromised due to respiratory-induced fluctuations in the size, shape, and position of the tumor. To address this challenge, we introduce a deep learning-anchored, volumetric tumor tracking methodology that employs single-angle X-ray projection images. This process involves aligning the intraoperative two-dimensional (2D) X-ray images with the pre-treatment three-dimensional (3D) planning Computed Tomography (CT) scans, enabling the extraction of the 3D tumor position and segmentation. Prior to therapy, a bespoke patient-specific tumor tracking model is formulated, leveraging a hybrid data augmentation, style correction, and registration network to create a mapping from single-angle 2D X-ray images to the corresponding 3D tumors. During the treatment phase, real-time X-ray images are fed into the trained model, producing the respective 3D tumor positioning. Rigorous validation conducted on actual patient lung data and lung phantoms attests to the high localization precision of our method at lowered radiation doses, thus heralding promising strides towards enhancing the precision of radiotherapy.

Stereo X-Ray Tomography

X-ray tomography is a powerful volumetric imaging technique, but detailed three dimensional (3D) imaging requires the acquisition of a large number of individual X-ray images, which is time consuming. For applications where spatial information needs to be collected quickly, for example, when studying dynamic processes, standard X-ray tomography is therefore not applicable. Inspired by stereo vision, in this paper, we develop X-ray imaging methods that work with two X-ray projection images. In this setting, without the use of additional strong prior information, we no longer have enough information to fully recover the 3D tomographic images. However, up to a point, we are nevertheless able to extract spatial locations of point and line features. From stereo vision, it is well known that, for a known imaging geometry, once the same point is identified in two images taken from different directions, then the point’s location in 3D space is exactly specified. The challenge is the matching of points between images. As X-ray transmission images are fundamentally different from the surface reflection images used in standard computer vision, we here develop a different feature identification and matching approach. In fact, once point like features are identified, if there are limited points in the image, then they can often be matched exactly. In fact, by utilising a third observation from an appropriate direction, matching becomes unique. Once matched, point locations in 3D space are easily computed using geometric considerations. Linear features, with clear end points, can be located using a similar approach.

Feature shape inspection of metal parts by matching X-ray projection images with CAD model projections

X-ray computed tomography (CT) provides a three-dimensional (3D) volume image of the scanned object and can be applied for nondestructive inspection of industrial materials. The object surface extracted from the X-ray CT volume is examined by comparing it with the pre-obtained computer-aided design (CAD) mesh model of the scanned object. However, industrial materials often contain heavy metals that significantly attenuate X-rays and cause CT artifacts in CT images, preventing an accurate shape extraction. This study proposes a method that extracts the geometric features of two-dimensional (2D) X-ray projection images and matches them with pre-obtained 3D CAD meshes. The proposed method is based on the principle that most of the geometric features of the object, such as edges, in the X-ray 2D projection images are captured and can be directly compared with CAD meshes. We first extract the features in each of the 2D projection images as X-ray feature projections. Subsequently, CAD mesh contours are projected onto 2D mesh feature projections and matched with the X-ray feature projections. Finally, 3D feature shapes of the CAD mesh are obtained by back-projecting the matching results. The proposed method requires only a small number of projection images, thus reducing computational time while maintaining shape accuracy.

2D/3D Non-Rigid Image Registration via Two Orthogonal X-ray Projection Images for Lung Tumor Tracking

Two-dimensional (2D)/three-dimensional (3D) registration is critical in clinical applications. However, existing methods suffer from long alignment times and high doses. In this paper, a non-rigid 2D/3D registration method based on deep learning with orthogonal angle projections is proposed. The application can quickly achieve alignment using only two orthogonal angle projections. We tested the method with lungs (with and without tumors) and phantom data. The results show that the Dice and normalized cross-correlations are greater than 0.97 and 0.92, respectively, and the registration time is less than 1.2 seconds. In addition, the proposed model showed the ability to track lung tumors, highlighting the clinical potential of the proposed method.

Feasibility study of deep learning-based markerless real-time lung tumor tracking with orthogonal X-ray projection images.

The feasibility of a deep learning-based markerless real-time tumor tracking (RTTT) method was retrospectively studied with orthogonal kV X-ray images and clinical tracking records acquired during lung cancer treatment. Ten patients with lung cancer treated with marker-implanted RTTT were included. The prescription dose was 50Gy in four fractions, using seven- to nine-port non-coplanar static beams. This corresponds to 14-18 X-ray tube angles for an orthogonal X-ray imaging system rotating with the gantry. All patients underwent 10 respiratory phases four-dimensional computed tomography. After a data augmentation approach, for each X-ray tube angle of a patient, 2250 digitally reconstructed radiograph (DRR) images with gross tumor volume (GTV) contour labeled were obtained. These images were adopted to train the patient and X-ray tube angle-specific GTV contour prediction model. During the testing, the model trained with DRR images predicted GTV contour on X-ray projection images acquired during treatment. The predicted three-dimensional (3D) positions of the GTV were calculated based on the centroids of the contours in the orthogonal images. The 3D positions of GTV determined by the marker-implanted RTTT during the treatment were considered as the ground truth. The 3D deviations between the prediction and the ground truth were calculated to evaluate the performance of the model. The median GTV volume and motion range were 7.42 (range, 1.18-25.74) cm3 and 22 (range, 11-28) mm, respectively. In total, 8993 3D position comparisons were included. The mean calculation time was 85ms per image. The overall median value of the 3D deviation was 2.27 (interquartile range: 1.66-2.95) mm. The probability of the 3D deviation smaller than 5mm was 93.6%. The evaluation results and calculation efficiency show the proposed deep learning-based markerless RTTT method may be feasible for patients with lung cancer.

Academic program recommendations for graduate degrees in medical physics: AAPM Report No. 365 (Revision of Report No. 197).

Academic program recommendations for graduate degrees in medical physics: AAPM Report No. 365 (Revision of Report No. 197).

High-resolution micro-CT with 3D image analysis for porosity characterization of historic bricks

The study of pores in historic bricks is important for characterizing and comparing brick materials, evaluating the degree of deterioration, predicting behavior in future weathering conditions, studying the effectiveness of protective measures, and analyzing the potential effects of cleaning treatments. High-resolution micro-CT coupled with 3D image analysis is a promising new approach for studying porosity and pore systems in bricks. In this technique, hundreds or even thousands of X-ray projection images are acquired at 360 degrees around a sample. The X-radiation passing through the sample is absorbed, with radiation attenuated to varying degrees depending on the varying densities of phases within the object. The 3D volume is reconstructed by a computer algorithm, producing images where each voxel has a grayscale intensity value associated with the component it represents. Recent new instrument designs allow fast scanning with good spatial resolution. In this research, we present a set of protocols for creating optimal images of brick pores in micro-CT scans and for conducting 3D image analysis to extract both qualitative and quantitative data from those scans. Small samples give better spatial resolution for imaging of pores, so given the typical heterogeneity of bricks, scanning multiple samples from each brick ensures that the results are more likely to be representative. Machine learning and deep learning with convolutional neural networks were found to be important tools for better distinguishing pores from the surrounding matrix in the segmentation process, especially at the very limits of spatial resolution. Statistical analyses revealed which of the many parameters that can be measured are potentially most significant for characterizing the pore systems of bricks. These significant pore variables came from a multi-staged image analysis approach and include the total volume percent occupied by pores, the percentage of those pores accessible to the surface versus isolated interior ones, a variety of statistical properties of individual pores related to their size and shape, the average number of connections that pores have to other pores, and the length, diameter, and directness of those connections.Graphical

Image contrast enhancement algorithm for X-ray observation of space materials in situ

Chinese Space Station has planned a high-temperature material science experiment rack, equipped with an X-ray projection imaging module, to support the development of material experiments and research in space. BiFeO3 has been selected as the first batch of experimental materials for Chinese Space Station. The melting and solidification process of BiFeO3, an opaque, high-temperature material, is observed by X-ray observation module in situ. X-ray is the dominant way to observe opaque materials due to its penetrability. In-situ observation of materials is the top priority of this study, so we have strict requirements on image quality, and high-quality images can better analyze the properties and properties of materials. Limited by narrow size and high temperature conditions, the X-ray images collected have low contrast, serious noise pollution, and poor imaging quality. To enhance the contrast and improve the edge details of such images, a grayscale weighted histogram equalization combined with high-frequency enhancement (GWHE-HFE) algorithm is proposed. First, we add a mask to the input image to obtain the region of interest (ROI), and then filter out the low-frequency components of the image by Gaussian high-pass filter to preserve high-frequency details. Second, the image obtained in the previous and the X-ray image of ROI are respectively multiplied by a coefficient and added to obtain the edge-emphasized X-ray image. And then, we use grayscale weighted histogram equalization (GWHE) to process the image obtained in the second step to obtain the contrast enhanced X-ray image. The enhanced image shows the crystal grains and the thin bands where the solid and the melt intersect, and it is helpful to accurately locate the solid solution interface. Experiments on X-ray images of BiFeO3 growth demonstrate that this combined method outperforms existing ones both qualitatively and quantitatively, providing an in-depth and effective analysis method for in high-temperature material-science experiments.

Novel-view X-ray projection synthesis through geometry-integrated deep learning.

X-ray imaging is a widely used approach to view the internal structure of a subject for clinical diagnosis, image-guided interventions and decision-making. The X-ray projections acquired at different view angles provide complementary information of patient's anatomy and are required for stereoscopic or volumetric imaging of the subject. In reality, obtaining multiple-view projections inevitably increases radiation dose and complicates clinical workflow. Here we investigate a strategy of obtaining the X-ray projection image at a novel view angle from a given projection image at a specific view angle to alleviate the need for actual projection measurement. Specifically, a Deep Learning-based Geometry-Integrated Projection Synthesis (DL-GIPS) framework is proposed for the generation of novel-view X-ray projections. The proposed deep learning model extracts geometry and texture features from a source-view projection, and then conducts geometry transformationon the geometry features to accommodate the change of view angle.At the final stage, the X-ray projection in the target view is synthesized from the transformed geometry and the shared texture features via an image generator. The feasibility and potential impact of the proposed DL-GIPS model are demonstrated using lung imaging cases.The proposed strategy can be generalized to a general case of multiple projections synthesis from multiple input views and potentially provides a new paradigm for various stereoscopic and volumetric imaging with substantially reduced efforts in data acquisition.

Matching of CAD model projections and X-ray projection images for shape inspection of metal assemblies

X-ray computed tomography (CT) facilitates non-destructive inspection of the interior of mechanical assemblies. However, industrial materials often contains heavy metals; which causes in CT artifacts in CT images and prevents accurate segmentation or shape measurement. To overcome this issue, in this study, we proposed a new inspection method that uses X-ray projection images and CAD meshes. We extracted characteristic features as edges and curved surfaces as feature images and used those for component alignments and shape measurements. The idea behind this is that these characteristic 3D features are captured as crease pixels in 2D projection images; thus, they can be extracted and compared with CAD meshes directly without conducting actual CT reconstructions. Furthermore, we conducted preliminary tests with some metal parts, which indicated that our approach can effectively inspect multi-material mechanical assemblies.

Pulsed voltage driving enhanced electron emission in ZnO nanowire cold cathode flat-panel X-ray source

A fully vacuum-sealed flat-panel X-ray source was fabricated composed of molybdenum metal anode and ZnO nanowire field emitters array cathode. The cathode current and X-ray radiation dose rate of the device were measured when the anode is applied with direct current (DC) and pulsed voltage, respectively. It is found that the peak emission current and corresponding dose rate under pulsed voltage driving are much higher than those under DC voltage driving. The phenomena may be explained by a plasma induced field emission enhancement related to the electron bombardment induced gas desorption. Clear projection X-ray imaging of moving objects was obtained using pulsed voltage driving. The result shows that owing to the enhanced emission current driven by the pulsed voltage, the flat-panel X-ray source can achieve fast and clear imaging of high-speed moving objects. Our results are of significance for the application of large area flat-panel X-ray source in dynamic imaging.

Novel-View X-Ray Projection Synthesis Through Geometry-Integrated Deep Learning

X-ray imaging is a widely used approach to view the internal structure of a subject for clinical diagnosis, image-guided interventions and decision-making. The X-ray projections acquired at different view angles provide complementary information of patient's anatomy and are required for stereoscopic or volumetric imaging of the subject. In reality, obtaining multiple-view projections inevitably increases radiation dose and complicates clinical workflow. Here we investigate a strategy of obtaining the X-ray projection image at a novel view angle from a given projection image at a specific view angle to alleviate the need for actual projection measurement. Specifically, we propose a deep learning-based geometry-integrated projection synthesis framework for generating novel-view X-ray projections.We for the first time investigate the novel-view projection synthesis problem for X-ray imaging. We propose a deep learning-based geometry-integrated projection synthesis model to generate novel-view X-ray projections through feature disentanglement and geometry transformation. The proposed deep learning model extracts geometry and texture features from a source-view projection, and then conducts geometry transformation on the geometry features to accommodate the change of view angle. Then the X-ray projection in target view is synthesized from the transformed geometry and shared texture features via an image generator. We validate the proposed approach by experimenting on one-to-one and multi-to-multi X-ray projection synthesis using lung imaging cases across various patients. We conduct experiments on a public dataset containing 1018 lung CT images, where 80% and 20% data are used for training and testing. The projection images are digitally produced from CT images using geometry consistent with a clinical on-board cone-beam CT system for radiation therapy.We deploy the trained model on the held-out testing set for novel-view projection synthesis. The generated results are compared with ground truth qualitatively and quantitatively. For AP-to-lateral and lateral-to-AP view synthesis, average NRMSE / SSIM / PSNR values over all testing data are 0.185 / 0.880 / 22.646 and 0.173 / 0.911 / 23.908, respectively. Promising results are also obtained for multi-to-multi view synthesis. By visualizing the generated projections, we observe the proposed model can generate images closely to the targets despite the various anatomic structures of different patients, indicating the potential of the proposed model for X-ray projection synthesis.This work investigates the synthesis of novel-view X-ray projections and presents a robust model combining the deep learning and geometry transformation. It is shown that the generated X-ray projections reveal the internal anatomy distribution from new viewpoints, which potentially provides a new paradigm for volumetric imaging with substantially reduced efforts in data acquisition.