X-ray Image Sequences Research Articles

A key-point based real-time tracking of lung tumor in x-ray image sequence by using difference of Gaussians filtering and optical flow

In radiation therapy, for accurate radiation dose delivery to a target tumor and reduction of the extra exposure of normal tissues, real-time tumor tracking is typically an important technique in lung cancer treatment since lung tumors move with patients’ respiration. To observe a tumor motion in real time, x-ray fluoroscopic devices can be employed, and various tracking techniques have been proposed to track tumors. However, development of a fast and accurate tracking method for clinical use is still a challenging task since the obscured image of the tumor can cause decreased tracking accuracy and can result in additional processing time for remedying the accuracy. In this study, a new key-point-based tumor tracking method, which is sufficiently fast and accurate, is presented. Given an x-ray image sequence, the proposed method employs a difference-of-Gaussians filtering technique to detect key points in the tumor region of the first frame which are robust against noise and outliers in the subsequent frames. In the subsequent frames, these key points are tracked using a fast optical flow technique, and tumor motion is estimated via their movement. To evaluate the performance, the proposed method has been tested on several clinical kV and MV x-ray image sequences. The experimental results showed that the average of the root mean square errors of tracking were and for kV and MV x-ray image sequences, respectively. This tracking performance was more accurate than previous tracking methods. In addition, the average processing times for each frame were and for kV and MV image sequences, respectively, and the proposed method was faster than previous methods as well as shorter than frame acquisition interval. Therefore, the proposed method has the potential for both highly accurate and fast tumor tracking in clinical applications.

Signal separation from X-ray image sequence using singular value decomposition

Abstract This work proposes singular value decomposition (SVD) to separate the signal from a noisy X-ray image sequence without any prior knowledge of the noise. SVD is based on the theory that the noise is always uncorrelated to the signal in a noisy image, and SVD, which belongs to Blind Source Separation (BSS), can decorrelate the signal from the noise components. To apply this proposed denoising method, two groups of X-ray images produced at 25 kV & 20 mAs and 34 kV & 20 mAs are sampled. To measure the proposed denoising method, ROIs with differing glandularity are selected. This work supports the use of SVD in X-ray image denoising. Normally, the separated signal will be less noisy when more noisy images are included for separating signals. Compared with other classical denoising methods, SVD is superior in reducing noise and improving CNR or SNR.

Perceptual training to improve hip fracture identification in conventional radiographs.

Diagnosing certain fractures in conventional radiographs can be a difficult task, usually taking years to master. Typically, students are trained ad-hoc, in a primarily-rule based fashion. Our study investigated whether students can more rapidly learn to diagnose proximal neck of femur fractures via perceptual training, without having to learn an explicit set of rules. One hundred and thirty-nine students with no prior medical or radiology training were shown a sequence of plain film X-ray images of the right hip and for each image were asked to indicate whether a fracture was present. Students were told if they were correct and the location of any fracture, if present. No other feedback was given. The more able students achieved the same level of accuracy as board certified radiologists at identifying hip fractures in less than an hour of training. Surprisingly, perceptual learning was reduced when the training set was constructed to over-represent the types of images participants found more difficult to categorise. Conversely, repeating training images did not reduce post-training performance relative to showing an equivalent number of unique images. Perceptual training is an effective way of helping novices learn to identify hip fractures in X-ray images and should supplement the current education programme for students.

Reconstruction of the rolling contact fatigue cracks in rails using X-ray computed tomography

Rolling contact fatigue (RCF) defects are associated with complex crack networks at the subsurface. A computed tomographic (CT) scanning technique has been developed to reconstruct the 3D geometry of the RCF cracks in the railhead. Sample rails having squats of different severities were taken from the Dutch railway network. Four specimens of different sizes were prepared and investigated with the CT scanner. The detailed procedures of the CT experiment and post-processing work were described. A sequence of high-quality X-ray images was collected during each scan. These 2D images were combined to construct the 3D visualization of the specimen. Various image processing tools were applied to extract and rebuild the internal crack geometries, thus allowing the crack networks to be differentiated from the bulk steel. For validation, the CT results were compared with metallographic observations of the rail surface for all the defects and the vertical section when needed. Discussions were made regarding the proper size of the rail samples and severity of the squats. According to the results, CT allows for a 3D visualization of RCF defects, providing high-quality data on the geometry of the internal cracks. By choosing the appropriate settings and specimen size, CT could accurately reconstruct the squat cracks at different growth stages. This research shows the potential of the CT technique as an intermediate detection and characterization tool among the methods for detecting macro cracks and those for characterizing micro/nano cracks. Finally, a practical specimen design and a detailed scanning procedure are proposed, based on the CT experiments performed in this research.

Extract-and-match geometric corner and step pattern approach for registration of fluoroscopic X-ray sequences.

This paper presents a method to extract-and-match robust corner features based on connecting edges from the edge maps, mainly formed by coronary vascular junctions in fluoroscopic x-ray sequence images. Such images are challenging due to the aperture problem. To overcome this, existing approaches attempt to extract vessels for registration. However, they are ineffective in poor quality images. Our approach describes the extracted robust corner features in a rotation invariant manner using step patterns, followed by matching them effectively. Experimental results show that our approach performs very well (above 80%) in a dataset of poor quality fluoroscopic x-ray image sequences without extensive processing such as segmentation or learning.

Noise estimation in cardiac x-ray imaging: a machine vision approach

We propose a method to automatically parameterize noise in cardiac x-ray image sequences. The aim was to provide context-sensitive imaging information for use in regulating dose control feedback systems that relates to the experience of human observers. The algorithm locates and measures noise contained in areas of approximately equal signal level. A single noise metric is derived from the dominant noise components based on their magnitude and spatial location in relation to clinically relevant structures. The output of the algorithm was compared to noise and clinical acceptability ratings from 28 observers viewing 40 different cardiac x-ray imaging sequences. Results show good agreement and that the algorithm has the potential to augment existing control strategies to deliver x-ray dose to the patient on an individual basis.

A Hidden Markov Model for 3D Catheter Tip Tracking With 2D X-ray Catheterization Sequence and 3D Rotational Angiography.

In minimal invasive image guided catheterization procedures, physicians require information of the catheter position with respect to the patient's vasculature. However, in fluoroscopic images, visualization of the vasculature requires toxic contrast agent. Static vasculature roadmapping, which can reduce the usage of iodine contrast, is hampered by the breathing motion in abdominal catheterization. In this paper, we propose a method to track the catheter tip inside the patient's 3D vessel tree using intra-operative single-plane 2D X-ray image sequences and a peri-operative 3D rotational angiography (3DRA). The method is based on a hidden Markov model (HMM) where states of the model are the possible positions of the catheter tip inside the 3D vessel tree. The transitions from state to state model the probabilities for the catheter tip to move from one position to another. The HMM is updated following the observation scores, based on the registration between the 2D catheter centerline extracted from the 2D X-ray image, and the 2D projection of 3D vessel tree centerline extracted from the 3DRA. The method is extensively evaluated on simulated and clinical datasets acquired during liver abdominal catheterization. The evaluations show a median 3D tip tracking error of 2.3 mm with optimal settings in simulated data. The registered vessels close to the tip have a median distance error of 4.7 mm with angiographic data and optimal settings. Such accuracy is sufficient to help the physicians with an up-to-date roadmapping. The method tracks in real-time the catheter tip and enables roadmapping during catheterization procedures.

How much image noise can be added in cardiac x-ray imaging without loss in perceived image quality?

Cardiologists use x-ray image sequences of the moving heart acquired in real-time to diagnose and treat cardiac patients. The amount of radiation used is proportional to image quality; however, exposure to radiation is damaging to patients and personnel. The amount by which radiation dose can be reduced without compromising patient care was determined. For five patient image sequences, increments of computer-generated quantum noise (white + colored) were added to the images, frame by frame using pixel-to-pixel addition, to simulate corresponding increments of dose reduction. The noise adding software was calibrated for settings used in cardiac procedures, and validated using standard objective and subjective image quality measurements. The degraded images were viewed next to corresponding original (not degraded) images in a two-alternative-forced-choice staircase psychophysics experiment. Seven cardiologists and five radiographers selected their preferred image based on visualization of the coronary arteries. The point of subjective equality, i.e., level of degradation where the observer could not perceive a difference between the original and degraded images, was calculated; for all patients the median was 33%±15% dose reduction. This demonstrates that a 33%±15% increase in image noise is feasible without being perceived, indicating potential for 33%±15% dose reduction without compromising patient care.

Blind source separation based x-ray image denoising from an image sequence.

Blind source separation (BSS) based x-ray image denoising from an image sequence is proposed. Without priori knowledge, the useful image signal can be separated from an x-ray image sequence, for original images are supposed as different combinations of stable image signal and random image noise. The BSS algorithms such as fixed-point independent component analysis and second-order statistics singular value decomposition are used and compared with multi-frame averaging which is a common algorithm for improving image's signal-to-noise ratio (SNR). Denoising performance is evaluated in SNR, standard deviation, entropy, and runtime. Analysis indicates that BSS is applicable to image denoising; the denoised image's quality will get better when more frames are included in an x-ray image sequence, but it will cost more time; there should be trade-off between denoising performance and runtime, which means that the number of frames included in an image sequence is enough.

Investigation of 3D glenohumeral displacements from 3D reconstruction using biplane X-ray images: Accuracy and reproducibility of the technique and preliminary analysis in rotator cuff tear patients

Rotator cuff (RC) tears may be associated with increased glenohumeral instability; however, this instability is difficult to quantify using currently available diagnostic tools. Recently, the three-dimensional (3D) reconstruction and registration method of the scapula and humeral head, based on sequences of low-dose biplane X-ray images, has been proposed for glenohumeral displacement assessment. This research aimed to evaluate the accuracy and reproducibility of this technique and to investigate its potential with a preliminary application comparing RC tear patients and asymptomatic volunteers. Accuracy was assessed using CT scan model registration on biplane X-ray images for five cadaveric shoulder specimens and showed differences ranging from 0.6 to 1.4mm depending on the direction of interest. Intra- and interobserver reproducibility was assessed through two operators who repeated the reconstruction of five subjects three times, allowing defining 95% confidence interval ranging from ±1.8 to ±3.6mm. Intraclass correlation coefficient varied between 0.84 and 0.98. Comparison between RC tear patients and asymptomatic volunteers showed differences of glenohumeral displacements, especially in the superoinferior direction when shoulder was abducted at 20° and 45°. This study thus assessed the accuracy of the low-dose 3D biplane X-ray reconstruction technique for glenohumeral displacement assessment and showed potential in biomechanical and clinical research.

WE-AB-303-02: A Real-Time Homography-Based Algorithm for Markerless Deformable Lung Tumor Motion Tracking Using KV X-Ray Fluoroscopy

Purpose: To develop a fast and robust tracking algorithm that does not require implanting fiducial markers for tracking deformable respiration-induced lung tumor motion using kV x-ray fluoroscopy. Methods: Four kV X-ray image sequences were acquired in lung cancer treatment by using On-Board Imaging (OBI) system (Varian Medical Systems, Palo Alto, CA) at frame rate 15 Hz. Given an OBI image sequence, a histogram equalization processing was employed to enhance the tumor contrast in advance. A template image containing the tumor target was then delineated manually in the first frame. Deformable tumor motion in the subsequent frames was represented by a non-linear homographic transformation. The parameters of the homographic transformation were estimated by minimizing a sum-of-squared-difference (SSD) between the template image and the observed frame. To improve the computational efficiency, an efficient second-order minimization (ESM) method was utilized to minimize the SSD. Results: We evaluated the performance of the proposed method in terms of the tracking accuracy and computational time. For the clinical kV image sequence, the absolute means and standard deviations of tracking error in superior-inferior (SI) and left-right (LR) directions were 0.41±0.43 mm and 0.08±0.29 mm, respectively. The computational time is about 0.04 sec/frame. Experimental results also demonstrated that the proposed method is superior to conventional template matching-based methods both in tracking accuracy and computational cost. Conclusion: In this study, we developed a novel markerless tracking algorithm to track deformable lung tumor motion. By using the homographic transformation and ESM method, the proposed method is capable of robustly tracking deformable tumor motion in real-time. The experimental results conducted on clinical kV image sequences demonstrated the effectiveness of the proposed method. In addition, compared with conventional template matching-based methods, our method achieved a higher tracking accuracy and lower computational cost.

3D multimodal cardiac data reconstruction using angiography and computerized tomographic angiography registration.

BackgroundComputerized tomographic angiography (3D data representing the coronary arteries) and X-ray angiography (2D X-ray image sequences providing information about coronary arteries and their stenosis) are standard and popular assessment tools utilized for medical diagnosis of coronary artery diseases. At present, the results of both modalities are individually analyzed by specialists and it is difficult for them to mentally connect the details of these two techniques. The aim of this work is to assist medical diagnosis by providing specialists with the relationship between computerized tomographic angiography and X-ray angiography.MethodsIn this study, coronary arteries from two modalities are registered in order to create a 3D reconstruction of the stenosis position. The proposed method starts with coronary artery segmentation and labeling for both modalities. Then, stenosis and relevant labeled artery in X-ray angiography image are marked by a specialist. Proper control points for the marked artery in both modalities are automatically detected and normalized. Then, a geometrical transformation function is computed using these control points. Finally, this function is utilized to register the marked artery from the X-ray angiography image on the computerized tomographic angiography and get the 3D position of the stenosis lesion.ResultsThe result is a 3D informative model consisting of stenosis and coronary arteries’ information from the X-ray angiography and computerized tomographic angiography modalities. The results of the proposed method for coronary artery segmentation, labeling and 3D reconstruction are evaluated and validated on the dataset containing both modalities.ConclusionsThe advantage of this method is to aid specialists to determine a visual relationship between the correspondent coronary arteries from two modalities and also set up a connection between stenosis points from an X-ray angiography along with their 3D positions on the coronary arteries from computerized tomographic angiography. Moreover, another benefit of this work is that the medical acquisition standards remain unchanged, which means that no calibration in the acquisition devices is required. It can be applied on most computerized tomographic angiography and angiography devices.

Clinical apparatus for the reduction of dose area product for patients undergoing x-ray catheterization.

The authors describe a design for prepatient region of interest attenuators (ROIAs) to reduce dose area product (DAP) for clinical use. The authors describe a model to predict DAP values from x-ray technique parameters recorded during a clinical procedure for image sequences obtained in the presence or absence of ROIAs. The model was developed primarily to determine what the DAP to a patient undergoing cardiac catheterization with a ROIA would have been if no ROIA had been used allowing a determination of DAP reduction. Copper ROIAs with thicknesses that vary gradually so as not to cause significant image artifacts were constructed. X-ray image sequences were acquired on a clinical catheterization system with and without ROIAs with varying x-ray technique parameters. DAP values were measured for all said exposures using an ionization chamber and compared to a model the authors developed. The model can predict DAP values within 3.5% on average with or without ROIAs when compared to ionization chamber measurements. The proposed experimental design is adequate for measuring DAP reductions on the order of 1.5-3.5 that are expected when introducing a ROIA during patient catheterization imaging.

CT imaging method with varying energy based on logarithm demodulation

For complicated structural components with wider X-ray attenuation ranges, the projection information is short of single-energy computed tomography (CT) imaging because the attenuation thickness of the components along the orientation of X-ray penetration exceeds the limit of the dynamic range of the CT imaging system. Then under this circumstance, in the conventional imaging mode of fixed ray energy the phenomenon of overexposure and underexposure easily occurs. The whole structure information cannot be obtained, and the projection information is seriously lacking, hence it will affect the quality of CT reconstruction. This paper proposes a CT imaging method with varying energy based on logarithm demodulation.#br#For the incomplete projection due to the limited dynamic range, we collect images at various rotation angles, as in standard CT; also, at each angle we collect a series of images at different energies and fuse them into a single high dynamic range (HDR) image. Based on this HDR image, the conventional CT reconstruction methods can get the full construction information. However, the CT quality is poor, because of noise amplification in the fusion process of X-ray image sequences which are gathered during varying the X-ray energy. To solve this problem, this paper proposes to use logarithm transformation to reduce the dynamic range for the varying energy image sequences. This can change the weight coefficients' access in the projection sequences to suppress the noise. Also in the transformation sequences, we can also use the fusion method, which is based on image gray consistency, to compute the weighting coefficient, to obtain the HDR projection, because the logarithm transformation does not change the image structure, and the overlap area between image sequences is definite. Finally we use the conventional CT reconstruction algorithm to make the CT imaging with the complicated structural components.#br#An accompanying experiment with a complicated instrument demonstrates that the new method can extend the dynamic range of X-ray imaging system and provide a complete representation of the internal structure of the complicated structural components as compared with the conventional CT imaging method. So, it will be of important significance and may be applied to the potential system, so as to improve the detecting sensitivity of the system, and make the high-quality reconstruction about complex products.

Temperature variability in X-ray bright points observed with Hinode/XRT

Aims. Our aim is to investigate the variability in the temperature as a function of time among a sample of coronal X-ray bright points (XBPs). Methods. We analysed a 7-hour (17:00 UT - 24:00 UT) long time sequence of soft X-ray images observed almost simultaneously in two filters (Ti poly and Al mesh) on April 14, 2007 with XRay Telescope (XRT) on-board the Hinode mission. We identifi ed and selected a sample of 14 XBPs and 2 background coronal regions for a detailed analysis. The light curves of XBPs have been derived using SolarSoft library in IDL. The temperature of XBPs was determined using the calibrated temperature response curves of the two filters by intensity ratio method. Results. We find that the XBPs show a high variability in their temperat ure and the average temperature ranges from 1.1 MK to 3.4 MK. The variations in the temperature are related to the different X-ray emission fluxes. These results suggest that XBPs of different temperatures may be present at the same height in the corona. It is evident from the results of time series that the heating rate of XBPs is highly variable on short time scales, and these variations provide a support for magnetic reconnection as the origin of XBPs.

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.

Joint Motion Estimation and Layer Segmentation in Transparent Image Sequences—Application to Noise Reduction in X-Ray Image Sequences

This paper is concerned with the estimation of the motions and the segmentation of the spatial supports of the different layers involved in transparent X-ray image sequences. Classical motion estimation methods fail on sequences involving transparent effects since they do not explicitly model this phenomenon. We propose a method that comprises three main steps: initial block-matching for two-layer transparent motion estimation, motion clustering with 3D Hough transform, and joint transparent layer segmentation and parametric motion estimation. It is validated on synthetic and real clinical X-ray image sequences. Secondly, we derive an original transparent motion compensation method compatible with any spatiotemporal filtering technique. A direct transparent motion compensation method is proposed. To overcome its limitations, a novel hybrid filter is introduced which locally selects which type of motion compensation is to be carried out for optimal denoising. Convincing experiments on synthetic and real clinical images are also reported.

Three-dimensional imaging of aerosol particles with scanning proton microprobe in a confocal arrangement

We have reconstructed three-dimensional (3D) element-specific distributions of aerosol microparticles captured in a thick quartz filter. The characteristic x rays were induced by a proton microprobe and detected by a Si(Li) spectrometer equipped with a polycapillary lens. Combining a fine microbeam scanning in the lateral plane with the sample movement parallel to the beam axis, the aerosol particles were driven across the sensitive microvolume and for Fe, Ca, S, and Si a sequence of two-dimensional lateral x-ray images was recorded. This is the first example of an x-ray element selective 3D imaging of a few micrometer sized objects with a proton beam.

3-D multimodal cardiac data superimposition using 2-D image registration and 3-D reconstruction from multiple views

Tomoscintigraphy (3-D data representing the myocardium perfusion) and coronarography (X-ray image sequences acquired from several viewpoints and providing information about the coronary artery tree state) are widespread examinations used standardly for the diagnosis of cardiovascular diseases. Currently, the data of both examinations are separately analyzed by the specialists who are often not able to visually link the information of the two modalities. The aim of this work is to facilitate the diagnosis by providing the specialist with 3-D representations highlighting the relationship between stenoses (artery narrowing, disease cause) and perfusion defaults (muscle necrosis, consequence). In such representations, the structure of a schematic artery tree and the stenosis positions are 3-D reconstructed and set onto the perfusion volume. The reconstruction algorithm starts with the projection of the perfusion volume on planes parallel to the X-ray images. In this way, 2-D tomographic images are generated for each viewpoint of the coronarography. The borders of the myocardium shadow visible in the X-ray images are then segmented with an active contour method. These contours are registered with their homologuous structures from the corresponding 2-D tomographic images. The geometrical transformations obtained for each coronarographic viewpoint are used to place artery points marked by cardiologists in the X-ray images (stenoses, artery beginnings, ends and bifurcations) in their corresponding positions in the tomographic images. Finally, the schematic artery tree is set on the perfusion volume using the homologous points of the different viewpoints and a parallel projection model. The main advantage of this method is that the clinical acquisition protocols remain unchanged, no calibration of the acquisition systems being needed. Results are given for a phantom and a database of patients in order to validate the proposed method.

Intensity oscillations and heating of the coronal X-ray bright points from Hinode/XRT

Our aim is to investigate the intensity oscillations in coronal X-ray Bright Points (XBPs). We analysed a 7-hours long time sequence of the soft X-ray images obtained on April 14, 2007 with 2-min cadence using X-Ray Telescope (XRT) on-board the Hinode mission. We use SSW in IDL to derive the time series of 14 XBPs and 2 background regions. For the first time, we have tried to use power spectrum analysis on XBPs data to determine the periods of intensity oscillations. coronal X-ray Bright Points (XBPs). The power spectra of XBPs show several significant peaks at different frequencies corresponding to a wide variety of time scales which range from a few minutes to hours. The light curves of all the XBPs give the impression that the XBPs can be grouped into three classes depending on emission levels: (i) weak XBPs; (ii) bright XBPs; and (iii) very strong XBPs. The periods of intensity oscillation are consistent in all the XBPs and are independent of their brightness level, suggesting that the heating mechanisms in all the three groups of XBPs are similar. The different classes of XBPs may be related to the different strengths of the magnetic field with which they have been associated.