Prospective motion correction of X-ray images for coronary interventions

A method for prospective motion correction of X-ray imaging of the heart is presented. A 3D + t coronary model is reconstructed from a biplane coronary angiogram obtained during free breathing. The deformation field is parameterized by cardiac and respiratory phase, which enables the estimation of the state of the arteries at any phase of the cardiac-respiratory cycle. The motion of the three-dimensional (3-D) coronary model is projected onto the image planes and used to compute a dewarping function for motion correcting the images. The use of a 3-D coronary model facilitates motion correction of images acquired with the X-ray system at arbitrary orientations. The performance of the algorithm was measured by tracking the motion of selected left coronary landmarks using a template matching cross-correlation. In three patients, we motion corrected the same images used to construct their 3D + t coronary model. In this best case scenario, the algorithm reduced the motion of the landmarks by 84%-85%, from mean RMS displacements of 12.8-14.6 pixels to 2.1-2.2 pixels. Prospective motion correction was tested in five patients by building the coronary model from one dataset, and correcting a second dataset. The patient's cardiac and respiratory phase are monitored and used to calculate the appropriate correction parameters. The results showed a 48%-63% reduction in the motion of the landmarks, from a mean RMS displacement of 11.5-13.6 pixels to 4.4-7.1 pixels.     

A model-based, semi-global segmentation approach for automatic 3-D point landmark localization in neuroimages

The existing differential approaches for localization of 3-D anatomic point landmarks in 3-D images are sensitive to noise and usually extract numerous spurious landmarks. The parametric model-based approaches are not practically usable for localization of landmarks that can not be modeled by simple parametric forms. Some dedicated methods using anatomic knowledge to identify particular landmarks are not general enough to cope with other landmarks. In this paper, we propose a model-based, semi-global segmentation approach to automatically localize 3-D point landmarks in neuroimages. To localize a landmark, the semi-global segmentation (meaning the segmentation of a part of the studied structure in a certain neighborhood of the landmark) is first achieved by an active surface model, and then the landmark is localized by analyzing the segmented part only. The joint use of global model-to-image registration, semi-global structure registration, active surface-based segmentation, and point-anchored surface registration makes our method robust to noise and shape variation. To evaluate the method, we apply it to the localization of ventricular landmarks including curvature extrema, centerline intersections, and terminal points. Experiments with 48 clinical and 18 simulated magnetic resonance (MR) volumetric images show that the proposed approach is able to localize these landmarks with an average accuracy of 1 mm (i.e., at the level of image resolution). We also illustrate the use of the proposed approach to cortical landmark identification and discuss its potential applications ranging from computer-aided radiology and surgery to atlas registration with scans.      

Characterization of 3-D Coronary Tree Motion From MSCT Angiography

This paper describes a method for the characterization of coronary artery motion using multislice computed tomography (MSCT) volume sequences. Coronary trees are first extracted by a spatial vessel tracking method in each volume of MSCT sequence. A point-based matching algorithm, with feature landmarks constraint, is then applied to match the 3-D extracted centerlines between two consecutive instants over a complete cardiac cycle. The transformation functions and correspondence matrices are estimated simultaneously, and allow deformable fitting of the vessels over the volume series. Either point-based or branch-based motion features can be derived. Experiments have been conducted in order to evaluate the performance of the method with a matching error analysis.      

Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling

A novel method is introduced for the generation of landmarks for three-dimensional (3-D) shapes and the construction of the corresponding 3-D statistical shape models. Automatic landmarking of a set of manual segmentations from a class of shapes is achieved by 1) construction of an atlas of the class, 2) automatic extraction of the landmarks from the atlas, and 3) subsequent propagation of these landmarks to each example shape via a volumetric nonrigid registration technique using multiresolution B-spline deformations. This approach presents some advantages over previously published methods: it can treat multiple-part structures and requires less restrictive assumptions on the structure's topology. In this paper, we address the problem of building a 3-D statistical shape model of the left and right ventricle of the heart from 3-D magnetic resonance images. The average accuracy in landmark propagation is shown to be below 2.2 mm. This application demonstrates the robustness and accuracy of the method in the presence of large shape variability and multiple objects.     

Clinical evaluation of respiratory motion compensation for anatomical roadmap guided cardiac electrophysiology procedures

X-ray fluoroscopically guided cardiac electrophysiological procedures are routinely carried out for diagnosis and treatment of cardiac arrhythmias. X-ray images have poor soft tissue contrast and, for this reason, overlay of static 3-D roadmaps derived from preprocedural volumetric data can be used to add anatomical information. However, the registration between the 3-D roadmap and the 2-D X-ray image can be compromised by patient respiratory motion. Three methods were designed and evaluated to correct for respiratory motion using features in the 2-D X-ray images. The first method is based on tracking either the diaphragm or the heart border using the image intensity in a region of interest. The second method detects the tracheal bifurcation using the generalized Hough transform and a 3-D model derived from 3-D preoperative volumetric data. The third method is based on tracking the coronary sinus (CS) catheter. This method uses blob detection to find all possible catheter electrodes in the X-ray image. A cost function is applied to select one CS catheter from all catheter-like objects. All three methods were applied to X-ray images from 18 patients undergoing radiofrequency ablation for the treatment of atrial fibrillation. The 2-D target registration errors (TRE) at the pulmonary veins were calculated to validate the methods. A TRE of 1.6 mm ± 0.8 mm was achieved for the diaphragm tracking; 1.7 mm ± 0.9 mm for heart border tracking, 1.9 mm ± 1.0 mm for trachea tracking, and 1.8 mm ± 0.9 mm for CS catheter tracking. We present a comprehensive comparison between the techniques in terms of robustness, as computed by tracking errors, and accuracy, as computed by TRE using two independent approaches.     

Landmark-based elastic registration using approximating thin-platesplines

We consider elastic image registration based on a set of corresponding anatomical point landmarks and approximating thin-plate splines. This approach is an extension of the original interpolating thin-plate spline approach and allows to take into account landmark localization errors. The extension is important for clinical applications since landmark extraction is always prone to error. Our approach is based on a minimizing functional and can cope with isotropic as well as anisotropic landmark errors. In particular, in the latter case it is possible to include different types of landmarks, e.g., unique point landmarks as well as arbitrary edge points. Also, the scheme is general with respect to the image dimension and the order of smoothness of the underlying functional. Optimal affine transformations as well as interpolating thin-plate splines are special cases of this scheme. To localize landmarks we use a semi-automatic approach which is based on three-dimensional (3-D) differential operators. Experimental results are presented for two-dimensional as well as 3-D tomographic images of the human brain.     

Consistent landmark and intensity-based image registration

Two new consistent image registration algorithms are presented: one is based on matching corresponding landmarks and the other is based on matching both landmark and intensity information. The consistent landmark and intensity registration algorithm produces good correspondences between images near landmark locations by matching corresponding landmarks and away from landmark locations by matching the image intensities. In contrast to similar unidirectional algorithms, these new consistent algorithms jointly estimate the forward and reverse transformation between two images while minimizing the inverse consistency error-the error between the forward (reverse) transformation and the inverse of the the reverse (forward) transformation. This reduces the ambiguous correspondence between the forward and reverse transformations associated with large inverse consistency errors. In both algorithms a thin-plate spline (TPS) model is used to regularize the estimated transformations. Two-dimensional (2-D) examples are presented that show the inverse consistency error produced by the traditional unidirectional landmark TPS algorithm can be relatively large and that this error is minimized using the consistent landmark algorithm. Results using 2-D magnetic resonance imaging data are presented that demonstrate that using landmark and intensity information together produce better correspondence between medical images than using either landmarks or intensity information alone.     

Constrained registration for motion compensation in atrial fibrillation ablation procedures

Fluoroscopic overlay images rendered from preoperative volumetric data can provide additional anatomical details to guide physicians during catheter ablation procedures for treatment of atrial fibrillation (AFib). As these overlay images are often compromised by cardiac and respiratory motion, motion compensation methods are needed to keep the overlay images in sync with the fluoroscopic images. So far, these approaches have either required simultaneous biplane imaging for 3-D motion compensation, or in case of monoplane X-ray imaging, provided only a limited 2-D functionality. To overcome the downsides of the previously suggested methods, we propose an approach that facilitates a full 3-D motion compensation even if only monoplane X-ray images are available. To this end, we use a training phase that employs a biplane sequence to establish a patient specific motion model. Afterwards, a constrained model-based 2-D/3-D registration method is used to track a circumferential mapping catheter. This device is commonly used for AFib catheter ablation procedures. Based on the experiments on real patient data, we found that our constrained monoplane 2-D/3-D registration outperformed the unconstrained counterpart and yielded an average 2-D tracking error of 0.6 mm and an average 3-D tracking error of 1.6 mm. The unconstrained 2-D/3-D registration technique yielded a similar 2-D performance, but the 3-D tracking error increased to 3.2 mm mostly due to wrongly estimated 3-D motion components in X-ray view direction. Compared to the conventional 2-D monoplane method, the proposed method provides a more seamless workflow by removing the need for catheter model re-initialization otherwise required when the C-arm view orientation changes. In addition, the proposed method can be straightforwardly combined with the previously introduced biplane motion compensation technique to obtain a good trade-off between accuracy and radiation dose reduction.     

Regression-based cardiac motion prediction from single-phase CTA

State of the art cardiac computed tomography (CT) enables the acquisition of imaging data of the heart over the entire cardiac cycle at concurrent high spatial and temporal resolution. However, in clinical practice, acquisition is increasingly limited to 3-D images. Estimating the shape of the cardiac structures throughout the entire cardiac cycle from a 3-D image is therefore useful in applications such as the alignment of preoperative computed tomography angiography (CTA) to intra-operative X-ray images for improved guidance in coronary interventions. We hypothesize that the motion of the heart is partially explained by its shape and therefore investigate the use of three regression methods for motion estimation from single-phase shape information. Quantitative evaluation on 150 4-D CTA images showed a small, but statistically significant, increase in the accuracy of the predicted shape sequences when using any of the regression methods, compared to shape-independent motion prediction by application of the mean motion. The best results were achieved using principal component regression resulting in point-to-point errors of 2.3±0.5 mm, compared to values of 2.7±0.6 mm for shape-independent motion estimation. Finally, we showed that this significant difference withstands small variations in important parameter settings of the landmarking procedure.     

Tracking a dynamic set of feature points

We address the problems of tracking a set of feature points over a long sequence of monocular images as well as how to include and track new feature points detected in successive frames. Due to the 3-D movement of the camera, different parts of the images exhibit different image motion. Tracking discrete features can therefore be decomposed into several independent and local problems. Accordingly, we propose a localized feature tracking algorithm. The trajectory of each feature point is described by a 2-D kinematic model. Then to track a feature point, an interframe motion estimation scheme is designed to obtain the estimates of interframe motion parameters. Subsequently, using the estimates of motion parameters, corresponding points are identified to subpixel accuracy. Afterwards, the temporal information is processed to facilitate the tracking scheme. Since different feature points are tracked independently, the algorithm is able to handle the image motion arising from general 3-D camera movements. On the other hand, in addition to tracking feature points detected at the beginning, an efficient way to dynamically include new points extracted in subsequent frames is devised so that the information in a sequence is preserved. Experimental results for several image sequences are also reported.     

A comprehensive cardiac motion estimation framework using both untagged and 3-D tagged MR images based on nonrigid registration

In this paper, we present a novel technique based on nonrigid image registration for myocardial motion estimation using both untagged and 3-D tagged MR images. The novel aspect of our technique is its simultaneous usage of complementary information from both untagged and 3-D tagged MR images. To estimate the motion within the myocardium, we register a sequence of tagged and untagged MR images during the cardiac cycle to a set of reference tagged and untagged MR images at end-diastole. The similarity measure is spatially weighted to maximize the utility of information from both images. In addition, the proposed approach integrates a valve plane tracker and adaptive incompressibility into the framework. We have evaluated the proposed approach on 12 subjects. Our results show a clear improvement in terms of accuracy compared to approaches that use either 3-D tagged or untagged MR image information alone. The relative error compared to manually tracked landmarks is less than 15% throughout the cardiac cycle. Finally, we demonstrate the automatic analysis of cardiac function from the myocardial deformation fields.     

无人机视觉导航算法

为保证无人机着陆精度和安全性,提出了一种无人机自主着陆视觉导航位姿解算方法。首先对机载相机进行标定,获取相机参数;然后综合考虑地标形状和尺寸、地标角点几何分布和角点数量对位姿估计精度的影响,设计了T型着陆地标形状和尺寸参数,将地标轮廓提取和角点检测算法相结合,得到几何分布好、数量适中的8个角点用于位姿解算,保证了位姿解算精度;为减少LK （Lucas-Kanade）光流法稳定跟踪地标的处理时间,直接将提取的这8个角点作为LK光流法检测和跟踪的输入,保证了算法实时性;最后利用三维空间到二维像平面投影关系对飞行位姿参数进行实时解算。实验结果表明:算法具有较高估计精度,算法平均周期为76.756 ms （约13帧/s）,在速度较低的着陆阶段基本满足自主着陆视觉导航的实时性要求。     

Nonrigid motion modeling of the liver from 3-D undersampled self-gated golden-radial phase encoded MRI

Magnetic resonance imaging (MRI) has been commonly used for guiding and planning image guided interventions since it provides excellent soft tissue visualization of anatomy and allows motion modeling to predict the position of target tissues during the procedure. However, MRI-based motion modeling remains challenging due to the difficulty of acquiring multiple motion-free 3-D respiratory phases with adequate contrast and spatial resolution. Here, we propose a novel retrospective respiratory gating scheme from a 3-D undersampled high-resolution MRI acquisition combined with fast and robust image registrations to model the nonrigid deformation of the liver. The acquisition takes advantage of the recently introduced golden-radial phase encoding (G-RPE) trajectory. G-RPE is self-gated, i.e., the respiratory signal can be derived from the acquired data itself, and allows retrospective reconstructions of multiple respiratory phases at any arbitrary respiratory position. Nonrigid motion modeling is applied to predict the liver deformation of an average breathing cycle. The proposed approach was validated on 10 healthy volunteers. Motion model accuracy was assessed using similarity-, surface-, and landmark-based validation methods, demonstrating precise model predictions with an overall target registration error of TRE = 1.70 ± 0.94 mm which is within the range of the acquired resolution.     

Compensation of sound speed deviations in 3-D B-mode ultrasound for intraoperative determination of the anterior pelvic plane

An accurate determination of the pelvic orientation is inevitable for the correct cup prosthesis placement of navigated total hip arthroplasties. Conventionally, this step is accomplished by percutaneous palpation of anatomic landmarks. Sterility issues and an increased landmark localization error for obese patients lead to the application of B-mode ultrasound imaging in the field of computer-assisted orthopedic surgery. Many approaches have been proposed in the literature to replace the percutaneous digitization by 3-D B-mode ultrasound imaging. However, the correct depth localization of the pelvic landmarks could be significantly affected by the acoustic properties of the penetrated tissues. Imprecise depth estimation could lead to a miscalculation of the pelvic orientation and subsequently to a misalignment of the acetabular cup implant. But so far, no solution has been presented, which compensates for acoustic property differences for correct depth estimation. In this paper, we present a novel approach to determine pelvic orientation from ultrasound images by applying a hierarchical registration scheme based on patch statistical shape models to compensate for differences in speed of sound. The method was validated based on plastic bones and a cadaveric specimen.     

Spatiotemporal Visualizations for the Measurement of Oropharyngeal Transit Time From Videofluoroscopy

Videofluoroscopy remains one of the mainstay methods for clinical swallowing assessment, yet its interpretation is both complex and subjective. This, in part, reflects the difficulties associated with estimation of bolus transit time through the oral and pharyngeal regions by visual inspection, and problems with consistent repeatability. This paper introduces a software-only framework that automatically determines the time taken for the bolus to cross 1-D anatomical landmarks representing the oral and pharyngeal region boundaries ( Fig. 1). The user-steered delineation algorithm live-wire and straight-line annotators are used to demarcate the landmark on a frame prior to the swallow action. The rate of change of intensity of the pixels in each landmark is used as the detection feature for bolus presence that can be visualized on a spatiotemporal plot. Artifacts introduced by head and neck movement are removed by updating the landmark coordinates using affine parameters optimized by a genetic-algorithm-based registration method. Heuristics are applied to the spatiotemporal plot to identify the frames during which the bolus passes the landmark. Correlation coefficients between three observers visually inspecting twenty-four 5-mL single swallow clips did not exceed 0.42. Yet the same measurements taken using this framework on the same clips had correlation coefficients exceeding 0.87.      

Adaptive Fuzzy Filtering for Artifact Reduction in Compressed Images and Videos

A fuzzy filter adaptive to both sample's activity and the relative position between samples is proposed to reduce the artifacts in compressed multidimensional signals. For JPEG images, the fuzzy spatial filter is based on the directional characteristics of ringing artifacts along the strong edges. For compressed video sequences, the motion compensated spatiotemporal filter (MCSTF) is applied to intraframe and interframe pixels to deal with both spatial and temporal artifacts. A new metric which considers the tracking characteristic of human eyes is proposed to evaluate the flickering artifacts. Simulations on compressed images and videos show improvement in artifact reduction of the proposed adaptive fuzzy filter over other conventional spatial or temporal filtering approaches.      

Motion estimation in the 3-D Gabor domain.

Motion estimation methods can be broadly classified as being spatiotemporal or frequency domain in nature. The Gabor representation is an analysis framework providing localized frequency information. When applied to image sequences, the 3-D Gabor representation displays spatiotemporal/spatiotemporal-frequency (st/stf) information, enabling the application of robust frequency domain methods with adjustable spatiotemporal resolution. In this work, the 3-D Gabor representation is applied to motion analysis. We demonstrate that piecewise uniform translational motion can be estimated by using a uniform translation motion model in the st/stf domain. The resulting motion estimation method exhibits both good spatiotemporal resolution and substantial noise resistance compared to existing spatiotemporal methods. To form the basis of this model, we derive the signature of the translational motion in the 3-D Gabor domain. Finally, to obtain higher spatiotemporal resolution for more complex motions, a dense motion field estimation method is developed to find a motion estimate for every pixel in the sequence.     

Differences between pelvic skin and bone landmark identification in different seated positions on spinal-cord injured subjects

The purpose of this paper was to determine the differences between internal and external pelvic landmark locations in different seating positions. A computer tool developed for the registration of two series of images was used to obtain the internal geometry. First, images of the pelvis were acquired by magnetic resonance imaging (MRI) for each subject, in a supine position; internal landmarks were then identified on the images. Second, ultrasound images of the iliac crests were acquired in four seated positions. A registration algorithm was applied to obtain the transformation matrix between the two image reference systems. The MRI anatomical landmarks were, therefore, transferred into the ultrasound referential, to obtain their three-dimensional (3-D) location in the different seating positions. The external landmarks in those seated positions were identified with a 3-D digitizer. The results revealed that generally the internal and external coordinates of corresponding landmarks are statistically different. The differences are not only due to soft tissue thickness but also to different interpretations of the landmarks' locations between the supine and the seated postures. However, these differences generally did not affect significantly the accuracy with which orientation indexes can be estimated (pelvic tilt, obliquity, transverse rotation). Correlations were found between the internal and external coordinates, implying that linear regressions can be established.     

Three-dimensional motion tracking of coronary arteries in biplane cineangiograms

A three-dimensional (3-D) method for tracking the coronary arteries through a temporal sequence of biplane X-ray angiography images is presented. A 3-D centerline model of the coronary vasculature is reconstructed from a biplane image pair at one time frame, and its motion is tracked using a coarse-to-fine hierarchy of motion models. Three-dimensional constraints on the length of the arteries and on the spatial regularity of the motion field are used to overcome limitations of classical two-dimensional vessel tracking methods, such as tracking vessels through projective occlusions. This algorithm was clinically validated in five patients by tracking the motion of the left coronary tree over one cardiac cycle. The root mean square reprojection errors were found to be submillimeter in 93% (54/58) of the image pairs. The performance of the tracking algorithm was quantified in three dimensions using a deforming vascular phantom. RMS 3-D distance errors were computed between centerline models tracked in the X-ray images and gold-standard centerline models of the phantom generated from a gated 3-D magnetic resonance image acquisition. The mean error was 0.69 (+/- 0.06) mm over eight temporal phases and four different biplane orientations.     

The Application of a Statistical Shape Model to Diaphragm Tracking in Respiratory-Gated Cardiac PET Images

Respiratory-induced diaphragm mismatch between positron emission tomography (PET) and computed tomography (CT) has been identified as a source of attenuation-correction artifact in cardiac PET. Diaphragm tracking in gated PET could therefore form part of a mismatch correction technique, where a single CT is transformed to match each PET frame. To investigate the feasibility of such a technique, a statistical shape model of the diaphragm was constructed from gated CT and applied to two gated $^{18}$ F-FDG PET-CT datasets. A poor level of accuracy was obtained when the model was fitted to landmarks obtained from PET, with errors of 3.6 and 5.0 mm per landmark for the two patients, despite inclusion of the data within the model construction. However, errors were reduced to 2.4 and 1.9 mm with the incorporation of a single frame of CT landmarks. These values are closer to the baseline measure of fitting solely to CT landmarks, found to be 2.2 and 1.2 mm in this case. Excluding the datasets from the model yielded similar trends but with higher overall residual errors, indicating the need for a larger training set. Therefore, a highly trained diaphragm model could negate the need for a gated CT for diaphragm tracking, provided that information from a static CT is incorporated.