Real-time X-ray Images Research Articles

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.

GVirtualXray (gVXR): Simulating X-ray radiographs and CT volumes of anthropomorphic phantoms

gVirtualXray (gVXR) is an open-source framework that relies on the Beer–Lambert law to simulate X-ray images in realtime on a graphics processor unit (GPU) using triangular meshes. We produced four Jupyter Notebooks to compare images simulated with gVXR and ground truth image of an anthropomorphic phantom: (i) an X-ray projection generated using a Monte Carlo simulation code, (ii) real digitally reconstructed radiographs (DRRs), (iii) computed tomography (CT) slices, and (iv) a real radiograph acquired with a clinical X-ray imaging system. Image registration was deployed in two Notebooks to align the simulated image on the corresponding ground truth image. We demonstrated that accurate images can be generated in milliseconds with gVirtualXray when scattering can be ignored.

High-Speed Real-Time X-Ray Image Recognition Based on a Pixelated SiPM-Coupled Scintillator Detector With Radiation Photoelectric Neural Network Structure

In monitoring scenarios for material processing and accident status evaluation, high-speed X-ray imaging at the microsecond or even nanosecond level is utilized. Real-time, long-term high speed x-ray image processing is difficult to realize with existing technology; main limitations are detector system structure and the need for high-speed transfer and storage of a large number of digital signals. Therefore, this paper proposes an irradiated photoelectric neural network for high-speed real-time X-ray image recognition based on a pixelated radiation detector. We use a SiPM coupled scintillator to realize this pixelated radiation detector. The weights of a neural network are mapped to the bias voltage of SiPM pixels, so that the SiPM array itself constitutes a neural network. By analyzing the output signals of an array of SiPMs, X-ray image can be recognized in real-time. The whole process does not involve complicated digital circuits and digital signal processing. The feasibility of the method was verified by experiments. As the initial stage of the study, we achieved an X-ray image recognition speed of 500,000 frames per second. The simplicity and innovation of this detector fully demonstrate its future application prospects. It is especially suitable for applications that require long-term continuous monitoring of radiographic images and immediate feedback of results, such as online monitoring of high-speed material processing, observation of the cosmic environment and other continuously changing scenes of X-ray images.

Simulation of X-ray projections on GPU: Benchmarking gVirtualXray with clinically realistic phantoms

Background and objectives: This study provides a quantitative comparison of images created using gVirtualXray (gVXR) to both Monte Carlo (MC) and real images of clinically realistic phantoms. gVirtualXray is an open-source framework that relies on the Beer-Lambert law to simulate X-ray images in realtime on a graphics processor unit (GPU) using triangular meshes.Methods: Images are generated with gVirtualXray and compared with a corresponding ground truth image of an anthropomorphic phantom: (i) an X-ray projection generated using a Monte Carlo simulation code, (ii) real digitally reconstructed radiographs (DRRs), (iii) computed tomography (CT) slices, and (iv) a real radiograph acquired with a clinical X-ray imaging system. When real images are involved, the simulations are used in an image registration framework so that the two images are aligned.Results: The mean absolute percentage error (MAPE) between the images simulated with gVirtualXray and MC is 3.12%, the zero-mean normalised cross-correlation (ZNCC) is 99.96% and the structural similarity index (SSIM) is 0.99. The run-time is 10 days for MC and 23 ms with gVirtualXray. Images simulated using surface models segmented from a CT scan of the Lungman chest phantom were similar to (i) DRRs computed from the CT volume and (ii) an actual digital radiograph. CT slices reconstructed from images simulated with gVirtualXray were comparable to the corresponding slices of the original CT volume.Conclusions: When scattering can be ignored, accurate images that would take days using MC can be generated in milliseconds with gVirtualXray. This speed of execution enables the use of repetitive simulations with varying parameters, e.g. to generate training data for a deep-learning algorithm, and to minimise the objective function of an optimisation problem in image registration. The use of surface models enables the combination of X-ray simulation with real-time soft-tissue deformation and character animation, which can be deployed in virtual reality applications.

High-speed synchrotron X-ray phase-contrast imaging for evaluating microscale damage mechanisms and tracking cracking behaviors inside cross-ply GFRCs

We integrated the high-speed synchrotron X-ray phase-contrast imaging (PCI) with a modified Kolsky compression bar loading platform to visualize the dynamic failure processes of cross-ply glass fiber reinforced composites (GFRCs). Four S-2 glass/SC-15 composite specimens were prepared, having similar thicknesses but different stacking sequences, namely as [012/9012], [9012/012], [08/908/08], and [908/08/908]. Three-dimensional synchrotron X-ray computed tomography and scanning electron microscopy (SEM) were employed to examine the microstructures and quantify the fiber volume fractions. Each specimen was notched and subjected to a dynamic three-point flexural loading. The onset of cracking close to the notch tip, crack propagation in 0° or 90° plies and their interface, crack opening, and ultimately failure of the specimen were captured by high-speed synchrotron X-ray PCI. Additional dynamic experiments were performed to determine the average time when the stress wave propagated through the specimen and correlate the X-ray images with the specimen's force-deflection response. Finally, the surface morphology of each specimen after the dynamic loading was imaged by SEM. Comparison between real-time X-ray images and post-fracture SEM images demonstrated the capability of the X-ray method to record damage evolution inside composites. Furthermore, the high spatial and temporal resolutions of the X-ray setup and edge enhancement by PCI enabled the identification of microscale damage features within 1 μs. Two damaging processes were identified, crack growth was quantified, and fracture toughness of the composites was evaluated. The method is deemed useful to reveal microscale damage mechanisms and track cracking behaviors inside cross-ply GFRCs under dynamic loading in real time.

Artificial Intelligence and Robotics in Regional Anaesthesia: Do they have a role?

Artificial Intelligence and Robotics in Regional Anaesthesia: Do they have a role?

Optimized CyberKnife Lung Treatment: Effect of Fractionated Tracking Volume Change on Tracking Results.

Objectives To explore the impact of volume change in the fractionated tracking of stereotactic radiotherapy on the results of synchronous, respiratory tracking algorithm using CyberKnife. Methods A total of 38 lung tumor patients receiving stereotactic radiotherapy at our center from March 2018 to October 2019 were counted. Photoshop CS4 image processing software was used to obtain the pixels and the average value of brightness of the tracking volume in the image and calculate the grayscale within the contour of the tracking volume on the real-time X-ray image. At the same time, parameters of the synchronous respiratory tracking algorithm of the fractional CyberKnife were extracted for comparison between the volume of image-guided image tracking and the number of fractions during stereotactic radiotherapy. We also analyzed the relationship between fraction tumor location and characteristics and the calculated results of synchronous respiratory tracking by CyberKnife. Results There were no significant differences between the first four fractions (p > 0.05) for left lung lesions and no significant differences between the first five fractions for right lung lesions (p ≥ 0.05). For peripheral lung cancer, longer fractional treatment led to greater variation in grayscale (G-A: >4 fractions p < 0.05), while for central lung cancer, longer fractional treatment led to greater variation in parameters of the synchronous respiratory tracking algorithm (Uncertainty A and Uncertainty B: >4 fractions p < 0.05). There was a significant correlation between radiotherapy-graded tumor density and relevant parameters, and the correlation was strong (>0.7, p < 0.05). Conclusion With the increase of treatment fractions, the gray value in the patient tracking volume decreased. Patients of >4 fractions were advised to reevaluate with simulated CT and replan. For tumors with small diameter and low density, the imaging changes of volume should be closely followed during treatment. For left lung and central lung cancer, carefully select the synchronous tracking treatment with 2-view.

Comparative performance assessment of beam hardening correction algorithms applied on simulated data sets.

Beam hardening artefacts deteriorate the reconstructed image quality in industrial computed tomography. The appearances of beam hardening artefacts can be cupping effects or streaks. They impair the image fidelity to the object being scanned. This work aims at comparing a variety of commonly used beam hardening correction algorithms in the context of industrial computed tomography metrology. We choose four beam hardening correction algorithms of different types for the comparison. They are a single-material linearization algorithm, a multimaterial linearization algorithm, a dual-energy algorithm and an iterative reconstruction algorithm. Each beam hardening correction algorithm is applied to simulated data sets of a dual-material phantom consisting of multiple rods. The comparison is performed on data sets simulated both under ideal conditions and with the addition of quantum noise. The performance of each algorithm is assessed with respect to its effect on the final image quality (contrast-to-noise ratio, spatial resolution), artefact reduction (streaks, cupping effects) and dimensional measurement deviations. The metrics have been carefully designed in order to achieve a robust and quantifiable assessment. The results suggest that the single-material linearization algorithm can reduce beam hardening artefacts in the vicinity of one material. The multimaterial linearization algorithm can further reduce beam hardening artefacts induced by the other material and improve the dimensional measurement accuracy. The dual-energy method can eliminate beam hardening artefacts, and improve the low contrast visibility and dimensional measurement accuracy. The iterative algorithm is able to eliminate beam hardening streaks. However, it induces aliasing patterns around the object edge, and its performance depends critically upon computational power. The contrast-to-noise ratio and spatial resolution are declined by noise. Noise also increases the difficulty of image segmentation and quantitative analysis. LAY DESCRIPTION: X-ray computed tomography (CT) is a major breakthrough in digital imaging technology in the late 20th century. First used as an important tool in medical imaging, CT has gradually introduced to the nonmedical areas (e.g. industrial nondestructive testing). Inherently CT is more prone to artefacts comparing to the conventional real-time X-ray image. Beam hardening artefacts caused by the polychromatic nature of X-ray spectra are known to deteriorate the reconstructed image quality in industrial CT. A number of beam hardening correction algorithms exist and are used across medical CT. However, there is a lack of research on their effectiveness on industrial CT. This study presents an in-depth beam hardening correction algorithm comparison in industrial CT. Since this study takes various factors of the algorithm performance into account, it provides insights of the advantages and disadvantages of each algorithm and assists the choice of algorithm to meet specific needs of industry. Existing beam hardening correction algorithms are divided into the following four categories: linearization, segmentation based linearization, dual-energy and iterative methods. Since the linearization method can only correct single-material objects, we did not include it in the comparative study. Among the remaining categories, we chose one from each category for comparison, for methods in one peer category share similar physical and mathematical principles. The methods are polynomial fit, Joseph segmentation, dual energy and IMPACT iterative method. This study uses a simulated polychromatic data set of a multimaterial phantom. The central slice of the corrected reconstructions is then assessed and the results are presented. In this study, we will compare beam hardening correction methods with respect to their performance on image quality, the removal of image artefacts and the influence on dimensional accuracy.

Development and control of a robotic arm for percutaneous surgery

PurposeThe purpose of this paper is to propose a robotic system for percutaneous surgery. The key component in the system, a robotic arm that can manipulate a puncture needle is presented. The mechanical design, the motion control and the force control method of the robotic arm are discussed in the paper.Design/methodology/approachThe arm with an arc mechanism placed on a 3D Cartesian stage is developed as a puncture needle manipulator to locate the position of the needle tip, tune the needle’s posture and actuate the puncture motion under the visual guidance of two orthogonal X-ray images of a patient by a surgeon. A focusing method by using two laser spots is proposed to automatically move the needle tip to a surgery entry point on the skin. A dynamics model is developed to control the position of the needle mechanism and an explicit force control strategy is utilized to perform the needle insertion.FindingsWith the surgical system, a surgeon can easily perform puncture operation by taking two orthogonal real-time X-ray images as a visual feedback and accurately navigating the needle insertion. The laser-guided focusing method is efficient in placement of the needle tip. The explicit force control strategy is proved to be effective for holding constant and stable puncture force in experiments.Originality/valueThe robotic arm has an advantage in easy redirection of the needle because the rotation and the translation are decoupled in the mechanism. By adopting simple laser pens and a well-developed kinematics model, the system can handle the entry point, locating task automatically. The focusing method and the force control method proposed in the paper are useful for the present system and could be intuitive for similar surgical robots.

X-ray online detection for laser welding T-joint of Al–Li alloy

In order to detect weld defects in laser welding T-joint of Al–Li alloy, a real-time X-ray image system is set up for quality inspection. Experiments on real-time radiography procedure of the weldment are conducted by using this system. Twin fillet welding seam radiographic arrangement is designed according to the structural characteristics of the weldment. The critical parameters including magnification times, focal length, tube current and tube voltage are studied to acquire high quality weld images. Through the theoretical and data analysis, optimum parameters are settled and expected digital images are captured, which is conductive to automatic defect detection.

Real-Time X-ray Imaging Reveals Interfacial Growth, Suppression, and Dissolution of Zinc Dendrites Dependent on Anions of Ionic Liquid Additives for Rechargeable Battery Applications.

The dynamic interfacial growth, suppression, and dissolution of zinc dendrites have been studied with the imidazolium ionic liquids (ILs) as additives on the basis of in situ synchrotron radiation X-ray imaging. The phase contrast difference of real-time images indicates that zinc dendrites are preferentially developed on the substrate surface in the ammoniacal electrolytes. After adding imidazolium ILs, both nucleation overpotential and polarization extent increase in the order of additive-free < EMI-Cl < EMI-PF6 < EMI-TFSA < EMI-DCA. The real-time X-ray images show that the EMI-Cl can suppress zinc dendrites, but result in the formation of the loose deposits. The EMI-PF6 and EMI-TFSA additives can smooth the deposit morphology through suppressing the initiation and growth of dendritic zinc. The addition of EMI-DCA increases the number of dendrite initiation sites, whereas it decreases the growth rate of dendrites. Furthermore, the dissolution behaviors of zinc deposits are compared. The zinc dendrites show a slow dissolution process in the additive-free electrolyte, whereas zinc deposits are easily detached from the substrate in the presence of EMI-Cl, EMI-PF6, or EMI-TFSA due to the formation of the loose structure. Hence, the dependence of zinc dendrites on anions of imidazolium IL additives during both electrodeposition and dissolution processes has been elucidated. These results could provide the valuable information in perfecting the performance of zinc-based rechargeable batteries.

레이저를 이용한 소재가공기술 동향 - ICALEO 2014를 중심으로 -

New lasers such as high power, high brightness and short wavelength laser are using diverse industry. Also new technologies are developing actively to solve various issues such as spattering, process monitoring, deep penetration and key-hole stability. ICALEO is the international congress where recent technology for laser material processing and laser system are present. At 2014, it was held at San Diego in USA and more than 260 papers were presented from 28 country. The effect of the laser beam shape such as Gaussian like and top-hat was investigated on acoustic emission signal and pore formation in welding. Inline penetration depth was measured with ICI(Inline Coherent Imaging) technique and the data was verified with real time X-ray image on laser welding. The laser welding performance at low pressure environment was evaluated for the thick plate alloy steel. UV laser was used to weld various metals such as Cu, Aluminum, steel and stainless steel. The effect of the wavelength of the laser on the formation of the wave at the wall of the key-hole front and the absorptivity was investigated.

Real-time X-ray microradiographic imaging and image correlation for local strain mapping in single trabecula under mechanical load

X-ray microradiography was employed to quantify the strains in loaded human trabecula. Samples of isolated trabeculae from human proximal femur were extracted and glued in a loading machine specially designed and manufactured for testing small specimens. The samples were then tested in tension and three-point bending until complete fracture of the specimen occured. To assess the deformation in the very small samples (thickness 100μm, length 1—2mm) a real-time microradiography in conjunction with digital image correlation (DIC) has been employed. Loaded samples were irradiated continuously by X-rays (Hamamatsu L8601-01 with 5μm spot) during the test. Radiographs were acquired using 0.25s exposure time with hybrid single-photon counting silicon pixel detector Medipix2. The distance between the source and detector was kept small to ensure radiographs of good quality for such a short exposure time.Design of the experimental loading device enables for precise control of the applied displacement which is important for the post-yield behavior assessment of trabeculae. Large dynamic range, high sensitivity and high contrast of the Medipix2 enables measuring even very small strains with DIC. Tested experimental setup enables to combine micromechanical testing of the basic building block of trabecular bone with time-lapse X-ray radiography to measure the strains and to assess the mechanical properties of single human trabecula as well as to capture the softening curve with sufficient precision.

Automatic weld recognition and extraction from real-time X-ray images using quadratic curve fitting and multi-order differences analysis of intensity profile

Automatic weld recognition and extraction from real-time X-ray images using quadratic curve fitting and multi-order differences analysis of intensity profile

Registration of real-time X-ray image sequences for weld inspection

Real-time X-ray inspection is one of the important nondestructive testing methods used for long consecutive weld inspection. Registration of the image sequences is the foundation for spatio-temporal image processing methods in which the position relationship between image sequences is needed, such as spatio-temporal filtering, motion blur removing and defect tracking in image sequences. However, the image registration is difficult because the signal-to-noise ratio of real-time X-ray weld image is rather low and the images are quite similar along the weld direction. In this paper, the image registration processing is separated into rotation registration and translation registration. The slope angle of weld, which is calculated by weld edges, is used to implement the rotation registration. And the translation registration is implemented based on phase correlation by extracting features from difference image. The experimental results show that the proposed method is stable and precise.

새 구조의 액정 엑스선 감지기

새 구조의 액정 엑스선 감지기를 만들었다. 이것은 액정판을 만들고 유리판을 얇게 식각한 다음, 그 유리판 위에 반사막과 광전도층을 연속하여 입힌 구조이다. 새 구조의 액정엑스선 감지기는 공정의 안정성, 대면적화, 감도 등에서 이미 상품화된 엑스선 감지기와도 충분히 경쟁할 수 있으며, 따라서 성공적으로 상용화 할 수 있음을 확인했다. We developed a new x-ray image sensor utilizing a reflection-mode liquid crystal panel as its sensitive element, and tested its functionality by using it to obtain an x-ray image of a printed circuit board. In the liquid crystal x-ray image sensors hitherto reported, the liquid crystal layer is in direct contact with the photoconductive film which is deposited on a glass substrate. In the fabrication of the new x-ray image sensor, a liquid crystal panel is fabricated in the first step by using a pair of glass plates of a few centimeters thicknrss. Then one of the glass substrates is ground until its thickness is reduced to about <TEX>$60\;{\mu}m$</TEX>. After polishing the glass plate, dielectric films for high reflectance at 630 nm, a film of amorphous selenium for photoconduction, and a transparent conductive film for electrode are deposited in sequence. The new x-ray image sensor has several merits: primarily, fabrication of a large area sensor is more easily compared with the old fashioned x-ray image sensors. Since the reflection type liquid crystal panel has a very steep response curve, the new x-ray sensor has much more sensitivity to x-rays compared with the conventional x-ray area sensor, and the radiation dosage can be reduced down to less then 20%. By combining the new x-ray sensor with CCD camera technology, real-time x-ray images can be easily captured. We report the structure, fabrication process and characteristics of the new x-ray image sensor.

Frameless Intracranial Stereotactic Radiosurgery Using Image Guided Targeting: Clinical Outcomes for Brain Metastases

Stereotactic radiosurgery (SRS) for lesions in the brain typically uses an invasive, stereotactic frame for target localization and immobilization. Recent technologic advances in LINAC radiosurgery units allow for near real-time x-ray image guided target localization, raising the possibility of performing SRS without an external stereotactic localization device. Beginning in 2005, we began performing “frameless” SRS with a non-invasive mask and image-guidance using the BrainLab Novalis treatment unit.

New approaches to catheter navigation for interventional radiology simulation

For over 20 years, interventional methods have improved the outcomes of patients with cardiovascular disease. However, these procedures require an intricate combination of visual and tactile feedback and extensive training. In this paper, we describe a series of novel approaches that have led to the development of a high-fidelity simulation system for interventional neuroradiology. In particular, we focus on a new approach for real-time deformation of devices such as catheters and guidewires during navigation inside complex vascular networks. This approach combines a real-time incremental Finite Element Model (FEM), an optimization strategy based on substructure decomposition, and a new method for handling collision response in situations where the number of contact points is very large. We also briefly describe other aspects of the simulation system, from patient-specific segmentation to the simulation of contrast agent propagation and fast volume-rendering techniques for generating synthetic X-ray images in real time. Although currently targeted at stroke therapy, our results are applicable to the simulation of any interventional radiology procedure.

New training strategies for RBF neural networks for X-ray agricultural product inspection

Classification of real-time X-ray images of pistachio nuts is discussed. The goal is to reduce the percentage of infested nuts while not rejecting more than a few percent of the good nuts. Radial basis function (RBF) neural network classifiers are emphasized. New training procedures are developed that allow samples such as those that are near decision boundaries to be treated differently from other samples. New clustering methods and new cluster classes are advanced to select and separately control various RBF parameters. These advancements are shown to be of use in this application.

DETECTION AND SEGMENTATION OF ITEMS IN X–RAY IMAGERY

Processing of realtime Xray images of randomly oriented and touching pistachio nuts for product inspectionis considered. Processing to isolate individual nuts (segmentation) is emphasized. The processing consisted of a blob coloringalgorithm, filters, and watershed transforms to segment touching nuts, and morphological processing to produce an imageof only the nutmeat. Each operation is detailed and quantitative data for each are presented. These techniques are useful formany different product inspection problems in agriculture and other areas.