Simulating coronary arteries in x-ray angiograms

Clinical validation of quantitative coronary angiography (QCA) algorithms is difficult due to the lack of a simple alternative method for accurately measuring in vivo vessel dimensions. We address this problem by embedding simulated coronary artery segments with known geometry in clinical angiograms. Our vessel model accounts for the profile of the vessel, x-ray attenuation in the original background, and noise in the imaging system. We have compared diameter measurements of our computer simulated arteries with measurements of an x-ray Telescopic-Shaped Phantom (XTSP) with the same diameters. The results show that for both uniform and anthropomorphic backgrounds there is good agreement in the measured diameters of XTSP compared to the simulated arteries (Pearson's correlation coefficient 0.99). In addition, the difference in accuracy and precision of the true diameter measures compared to the XTSP and simulated artery diameters was small (mean absolute error across all diameters was < or = 0.11 mm +/- 0.09 mm).     

Model-based respiratory motion compensation for emission tomography image reconstruction

In emission tomography imaging, respiratory motion causes artifacts in lungs and cardiac reconstructed images, which lead to misinterpretations, imprecise diagnosis, impairing of fusion with other modalities, etc. Solutions like respiratory gating, correlated dynamic PET techniques, list-mode data based techniques and others have been tested, which lead to improvements over the spatial activity distribution in lungs lesions, but which have the disadvantages of requiring additional instrumentation or the need of discarding part of the projection data used for reconstruction. The objective of this study is to incorporate respiratory motion compensation directly into the image reconstruction process, without any additional acquisition protocol consideration. To this end, we propose an extension to the maximum likelihood expectation maximization (MLEM) algorithm that includes a respiratory motion model, which takes into account the displacements and volume deformations produced by the respiratory motion during the data acquisition process. We present results from synthetic simulations incorporating real respiratory motion as well as from phantom and patient data.     

Region of interest motion compensation for PET image reconstruction

A motion-incorporated reconstruction (MIR) method for gated PET imaging has recently been developed by several authors to correct for respiratory motion artifacts in PET imaging. This method however relies on a motion map derived from images (4D PET or 4D CT) of the entire field of view (FOV). In this study we present a region of interest (ROI)-based extension to this method, whereby only the motion map of a user-defined ROI is required and motion incorporation during image reconstruction is solely performed within the ROI. A phantom study and an NCAT computer simulation study were performed to test the feasibility of this method. The phantom study showed that the ROI-based MIR produced results that are within 1.26% of those obtained by the full image-based MIR approach when using the same accurate motion information. The NCAT phantom study on the other hand, further verified that motion of features of interest in an image can be estimated more efficiently and potentially more accurately using the ROI-based approach. A reduction of motion estimation time from 450 s to 30 and 73 s was achieved for two different ROIs respectively. In addition, the ROI-based approach showed a reduction in registration error of 43% for one ROI, which effectively reduced quantification bias by 44% and 32% using mean and maximum voxel values, respectively.     

4D rotational x-ray imaging of wrist joint dynamic motion

Current methods for imaging joint motion are limited to either two-dimensional (2D) video fluoroscopy, or to animated motions from a series of static three-dimensional (3D) images. 3D movement patterns can be detected from biplane fluoroscopy images matched with computed tomography images. This involves several x-ray modalities and sophisticated 2D to 3D matching for the complex wrist joint. We present a method for the acquisition of dynamic 3D images of a moving joint. In our method a 3D-rotational x-ray (3D-RX) system is used to image a cyclically moving joint. The cyclic motion is synchronized to the x-ray acquisition to yield multiple sets of projection images, which are reconstructed to a series of time resolved 3D images, i.e., four-dimensional rotational x ray (4D-RX). To investigate the obtained image quality parameters the full width at half maximum (FWHM) of the point spread function (PSF) via the edge spread function and the contrast to noise ratio between air and phantom were determined on reconstructions of a bullet and rod phantom, using 4D-RX as well as stationary 3D-RX images. The CNR in volume reconstructions based on 251 projection images in the static situation and on 41 and 34 projection images of a moving phantom were 6.9, 3.0, and 2.9, respectively. The average FWHM of the PSF of these same images was, respectively, 1.1, 1.7, and 2.2 mm orthogonal to the motion and parallel to direction of motion 0.6, 0.7, and 1.0 mm. The main deterioration of 4D-RX images compared to 3D-RX images is due to the low number of projection images used and not to the motion of the object. Using 41 projection images seems the best setting for the current system. Experiments on a postmortem wrist show the feasibility of the method for imaging 3D dynamic joint motion. We expect that 4D-RX will pave the way to improved assessment of joint disorders by detection of 3D dynamic motion patterns in joints.     

Derivation of spatial information from biplane multidirectional coronary angiograms

Modern biplane multidirectional isocentric X-ray equipment delivers the image information necessary for spatial computations from two simultaneous 2-dimensional coronary angiographic pictures. Using the tools of analytical geometry, the spatial position of well definable points in the fields of view of the two image-intensifiers can be calculated from their corresponding projections knowing the geometrical properties of the system stands. The method developed is independent of the angle between the projections and is applicable even if hemiaxial views are used. The mathematical formulas necessary for these spatial computations are derived. By means of calculating the radiological magnification factor, the method was validated using a wire with known diameter as reference object. 360-diameter measurements of the wire filmed in 18 different simultaneous biplane projections resulted in a mean error of 3.14%. In addition, catheter measurements of routine coronary angiograms yielded a mean diameter of 2.64 +/- 0.19 mm (mean +/- SD, real diameter 2.66 mm). Conclusion: Using this algorithm, a reliable determination of spatial coordinates of distinct points of interest is possible as prerequisite for absolute quantitative measurements from biplane angiograms.     

Best estimate of luminal cross-sectional area of coronary arteries from angiograms

We have reexamined the problem of estimating the luminal area of an elliptically shaped coronary artery cross section from two or more radiographic diameter measurements. The expected error is found to be much smaller than the maximum potential error. In the cases of two orthogonal views, closed form expressions have been derived for calculating the area and the uncertainty. Assuming that the underlying ellipse has limited ellipticity (major/minor axis ratio less than five), it is shown that the average uncertainty in the area is less than 14%. When more than two views are available, we suggest using a least-squares fit method to extract all available information from the data.     

Ordered subset reconstruction for x-ray CT

Statistical methods for image reconstruction such as the maximum likelihood expectation maximization are more robust and flexible than analytical inversion methods and allow for accurate modelling of the counting statistics and photon transport during acquisition of projection data. Statistical reconstruction is prohibitively slow when applied to clinical x-ray CT due to the large data sets and the high number of iterations required for reconstructing high-resolution images. Recently, however, powerful methods for accelerating statistical reconstruction have been proposed which, instead of accessing all projections simultaneously for updating an image estimate, are based on accessing a subset of projections at the time during iterative reconstruction. In this paper we study images generated by the convex algorithm accelerated by the use of ordered subsets (the OS convex algorithm (OSC)) for data sets with sizes, noise levels and spatial resolution representative of x-ray CT imaging. It is only in the case of extremely high acceleration factors (higher than 50, corresponding to fewer than 20 projections per subset), that areas with incorrect grey values appear in the reconstructed images, and that image noise increases compared with the standard convex algorithm. These image degradations can be adequately corrected for by running the final iteration of OSC with a reduced number of subsets. Even by applying such a relatively slow final iteration, OSC produces almost an equal resolution and lesion contrast as the standard convex algorithm, but more than two orders of magnitude faster.     

A motion-incorporated reconstruction method for gated PET studies

Cardiac and respiratory motion artefacts in PET imaging have been traditionally resolved by acquiring the data in gated mode. However, gated PET images are usually characterized by high noise content due to their low photon statistics. In this paper, we present a novel 4D model for the PET imaging system, which can incorporate motion information to generate a motion-free image with all acquired data. A computer simulation and a phantom study were conducted to test the performance of this approach. The computer simulation was based on a digital phantom that was continuously scaled during data acquisition. The phantom study, on the other hand, used two spheres in a tank of water, all of which were filled with (18)F water. One of the spheres was stationary while the other moved in a sinusoidal fashion to simulate tumour motion in the thorax. Data were acquired using both 4D CT and gated PET. Motion information was derived from the 4D CT images and then used in the 4D PET model. Both studies showed that this 4D PET model had a good motion-compensating capability. In the phantom study, this approach reduced quantification error of the radioactivity concentration by 95% when compared to a corresponding static acquisition, while signal-to-noise ratio was improved by 210% when compared to a corresponding gated image.     

Filter calculation for x-ray tomosynthesis reconstruction

Filtered backprojection reconstruction is an efficient image reconstruction method which is widely used in CT and 3D x-ray imaging. The way data have to be filtered depends on the acquisition geometry and the number of projections (views) which were acquired. For standard geometries like circle or helix it is known how to effectively filter the data. But there are acquisition geometries for which the application of standard filters yields poor results, e.g. in situations where the number of views is very small or for a limited angular range. In tomosynthesis, both conditions apply, i.e. the number of projections is typically very small and, moreover, the angular coverage is much less than 180°. This paper proposes a new method to design effective filters which are specific for the acquisition geometry. Examples from x-ray tomosynthesis are utilized to demonstrate the excellent performance of the proposed method.     

Adaptive prediction of respiratory motion for motion compensation radiotherapy

One potential application of image-guided radiotherapy is to track the target motion in real time, then deliver adaptive treatment to a dynamic target by dMLC tracking or respiratory gating. However, the existence of a finite time delay (or a system latency) between the image acquisition and the response of the treatment system to a change in tumour position implies that some kind of predictive ability should be included in the real-time dynamic target treatment. If diagnostic x-ray imaging is used for the tracking, the dose given over a whole image-guided radiotherapy course can be significant. Therefore, the x-ray beam used for motion tracking should be triggered at a relatively slow pulse frequency, and an interpolation between predictions can be used to provide a fast tracking rate. This study evaluates the performance of an autoregressive-moving average (ARMA) model based prediction algorithm for reducing tumour localization error due to system latency and slow imaging rate. For this study, we use 3D motion data from ten lung tumour cases where the peak-to-peak motion is greater than 8 mm. Some strongly irregular traces with variation in amplitude and phase were included. To evaluate the prediction accuracy, the standard deviations between predicted and actual motion position are computed for three system latencies (0.1, 0.2 and 0.4 s) at several imaging rates (1.25-10 Hz), and compared against the situation of no prediction. The simulation results indicate that the implementation of the prediction algorithm in real-time target tracking can improve the localization precision for all latencies and imaging rates evaluated. From a common initial setting of model parameters, the predictor can quickly provide an accurate prediction of the position after collecting 20 initial data points. In this retrospective analysis, we calculate the standard deviation of the predicted position from the twentieth position data to the end of the session at 0.1 s interval. For both regular and irregular lung tumour motions, with prediction the range of average errors is 0.4-2.5 mm in the SI direction from shorter to longer latency, corresponding to a range of 0.8-4.3 mm without prediction; for the AP direction a range of 0.3-1.6 mm is obtained with prediction, corresponding to a range of 0.6-3.0 mm without prediction. For 0.2 s and 0.4 s system latency, with prediction the localization based on a relatively slow imaging rate (2.5 Hz) can achieve a better or similar precision compared with no prediction but on a fast imaging rate (10 Hz). This means that precise localization can be realized at a slow imaging rate. This is important for the application of kV x-ray imaging systems and EPID-based systems in image-guided radiotherapy. In conclusion, the adaptive predictor can successfully predict irregular respiratory motion, and the adaptive prediction of respiration motion can effectively improve the delivery precision of real-time motion compensation radiotherapy.     

Automated cardiac motion compensation in PET/CT for accurate reconstruction of PET myocardial perfusion images

Error-free reconstruction of PET data with a registered CT attenuation map is essential for accurate quantification and interpretation of cardiac perfusion. Misalignment of the CT and PET data can produce an erroneous attenuation map that projects lung attenuation parameters onto the heart wall, thereby underestimating the attenuation and creating artifactual areas of hypoperfusion that can be misinterpreted as myocardial ischemia or infarction. The major causes of misregistration between CT and PET images are the respiratory motion, cardiac motion and gross physical motion of the patient. The misalignment artifact problem is overcome with automated cardiac registration software that minimizes the alignment error between the two modalities. Results show that the automated registration process works equally well for any respiratory phase in which the CT scan is acquired. Further evaluation of this procedure on 50 patients demonstrates that the automated registration software consistently aligns the two modalities, eliminating artifactual hypoperfusion in reconstructed PET images due to PET/CT misregistration. With this registration software, only one CT scan is required for PET/CT imaging, which reduces the radiation dose required for CT-based attenuation correction and improves the clinical workflow for PET/CT.     

Hybrid reconstruction algorithm for x-ray computed tomography

A hybrid reconstruction algorithm (HRA) is derived for fan beam geometry for use with systems utilizing a linear array of detectors. The algorithm uses fan beam geometry with a modified parallel beam reconstruction formula. The parallel beam algorithm is used with fan beam geometry by a simple geometric conversion and a different back projection formula. Correction for center of rotation shift is done during the back projection. Computer simulations are shown, both with the center of rotation shift uncorrected and corrected.     

多普勒组织成像技术对冠心病室壁运动的价值探讨

目的探讨多普勒组织成像技术（DTI）对冠心病室壁运动评分中的应用价值。方法55例临床确诊的冠心病患者和31例健康体检的正常对照者左室壁节段长轴及短轴方向运动速度进行检测,并与常规二维超声心动图（2DE）检查结果对照分析。结果冠心病组前间隔与左室后壁运动异常节段短轴方向运动速度与正常组相应节段波群比较,除心尖水平、后壁二尖瓣口水平和低位乳头肌水平的A波无显著差异外,其余各指标均有明显的减低（P〈0．01）;冠心病室壁运动异常节段长轴方向的收缩速度（S）及舒张速度（E和A）也明显低于正常组相应节段的速度（P〈0．01）。结论DTI将M型超声与多普勒超声技术结合起来,不仅可以评价心脏整体功能,而且还能够对局部室壁的运动速度进行分析,是一有价值的显像技术,值得进一步研究。     

Super-global distortion correction for a rotational C-arm x-ray image intensifier.

Image intensifier (II) distortion changes as a function of C-arm rotation angle because of changes in the orientation of the II with the earth's or other stray magnetic fields. For cone-beam computed tomography (CT), distortion correction for all angles is essential. The new super-global distortion correction consists of a model to continuously correct II distortion not only at each location in the image but for every rotational angle of the C arm. Calibration bead images were acquired with a standard C arm in 9 in. II mode. The super-global (SG) model is obtained from the single-plane global correction of the selected calibration images with given sampling angle interval. The fifth-order single-plane global corrections yielded a residual rms error of 0.20 pixels, while the SG model yielded a rms error of 0.21 pixels, a negligibly small difference. We evaluated the accuracy dependence of the SG model on various factors, such as the single-plane global fitting order, SG order, and angular sampling interval. We found that a good SG model can be obtained using a sixth-order SG polynomial fit based on the fifth-order single-plane global correction, and that a 10 degrees sampling interval was sufficient. Thus, the SG model saves processing resources and storage space. The residual errors from the mechanical errors of the x-ray system were also investigated, and found comparable with the SG residual error. Additionally, a single-plane global correction was done in the cylindrical coordinate system, and physical information about pincushion distortion and S distortion were observed and analyzed; however, this method is not recommended due to a lack of calculational efficiency. In conclusion, the SG model provides an accurate, fast, and simple correction for rotational C-arm images, which may be used for cone-beam CT.     

A comparison of beam characteristics for gated and nongated clinical x-ray beams.

Respiratory gating has only recently been applied to conventional external beam radiotherapy. In order for respiratory gating to be used clinically, an evaluation of the dosimetric effects of small units of delivered dose must be performed. The purpose of this study is to systematically evaluate the effect of various gating sequences on x-ray central axis output, ionization ratios (nominal accelerating potential), beam flatness, and beam symmetry. Measurements were taken for 6 and 18 MV photons on a linear accelerator that generates the gate by using a gridded electron gun to stop the electron flow to the wave-guide. The beam output, energy, flatness, and symmetry did not vary by more than 0.8 percent in most of the gating sequences. The maximum output deviations (0.8 percent), flatness deviations (1.9 percent), and symmetry deviations (0.8 percent) occurred when a low number of monitor units (<5 MU) were delivered in the gating window. Although these deviations are not clinically significant, each linear accelerator should be evaluated carefully before clinical implementation.