Martin G. Wagner

Noise reduction for curve-linear structures in real time fluoroscopy applications using directional binary masks.

PURPOSE Recent efforts in the reconstruction of interventional devices from two distinct views require the segmentation of the object in both fluoroscopic images. Noise might decrease the quality of the segmentation and cause artifacts in the reconstruction. The noise level depends on the x-ray dose the patient is exposed to. The proposed algorithm reduces the noise and enhances the separability of curvilinear devices in background subtracted fluoroscopic images to allow a more accurate segmentation. METHODS The algorithm uses a set of binary masks to estimate a line conformity measure that determines the best direction for a directional filter kernel. If the calculated value exceeds a certain threshold, the directional kernel is used to obtain the filtered value. Otherwise, an isotropic filter kernel is used. RESULTS The evaluation was performed on a set of 36 fluoroscopic images using a vascular head phantom with three different guidewires and nine different x-ray dosages from 6 nGy/pulse to 45 nGy/pulse as well as a clinical data set containing ten images. Compared with wavelet shrinkage and the bilateral filter, the proposed algorithm increased the average contrast to noise ratio by at least 17.8% for the phantom and 68.9% for the clinical images. The accuracy of the device segmentation was improved on average by at least 17.3% and 14.0%, respectively. CONCLUSIONS The proposed algorithm was able to significantly reduce the amount of noise in the images and therefore increase the quality of the device segmentations compared to both the bilateral filter and the wavelet thresholding approach for all acquired noise levels using rotating directional filter kernels near line structures and isotropic kernels for the background. The application of the proposed algorithm for the 3D reconstruction of curvilinear devices from two views would allow a more accurate reconstruction of the device.

Guidewire path tracking and segmentation in 2D fluoroscopic time series using device paths from previous frames

Recent efforts to perform a 3D reconstruction of interventional devices such as guidewires from monoplane and biplane fluoroscopic images require the segmentation of the exact device path in the respective 2D projection images. The segmentation of the device in low dose fluoroscopy images can be challenging since noise and motion artifacts degrade the image quality. Additionally, extracting the device path from the segmented region may result in ambiguous results due to overlapping device parts or discontinuities in the device segmentation. The purpose of this work is to present a novel guidewire tracking and segmentation algorithm, which segments the device region based on three different features based on a ridge detection filter, noise reduction for curvilinear structures as well as an a priori probability map. The features are calculated from background subtracted as well as unsubtracted fluoroscopic images. The device path extraction is based on a topology preserving thinning algorithm followed by a path search, which minimizes a cost function based on distance and directional difference between adjacent segments as well as the similarity to the device path extracted from the previous frame. The quantitative evaluation was performed using 7 data sets acquired from a canine study. Device shapes with different complexities are compared to semi-automatic segmentations. An average segmentation accuracy of 0.50 0.41 mm was achieved where each point along the device was compared to the point on the reference device centerline with the same distance to the device tip. In all cases the device path could be resolved correctly, which would allow a more accurate and reliable reconstruction of the 3D path of the device.

4D interventional device reconstruction from biplane fluoroscopy

PURPOSE Biplane angiography systems provide time resolved 2D fluoroscopic images from two different angles, which can be used for the positioning of interventional devices such as guidewires and catheters. The purpose of this work is to provide a novel algorithm framework, which allows the 3D reconstruction of these curvilinear devices from the 2D projection images for each time frame. This would allow creating virtual projection images from arbitrary view angles without changing the position of the gantries, as well as virtual endoscopic 3D renderings. METHODS The first frame of each time sequence is registered to and subtracted from the following frame using an elastic grid registration technique. The images are then preprocessed by a noise reduction algorithm using directional adaptive filter kernels and a ridgeness filter that emphasizes curvilinear structures. A threshold based segmentation of the device is then performed, followed by a flux driven topology preserving thinning algorithm to extract the segments of the device centerline. The exact device path is determined using Dijkstras algorithm to minimize the curvature and distance between adjacent segments as well as the difference to the device path of the previous frame. The 3D device centerline is then reconstructed using epipolar geometry. RESULTS The accuracy of the reconstruction was measured in a vascular head phantom as well as in a cadaver head and a canine study. The device reconstructions are compared to rotational 3D acquisitions. In the phantom experiments, an average device tip accuracy of 0.35 ± 0.09 mm, a Hausdorff distance of 0.65 ± 0.32 mm, and a mean device distance of 0.54 ± 0.33 mm were achieved. In the cadaver head and canine experiments, the device tip was reconstructed with an average accuracy of 0.26 ± 0.20 mm, a Hausdorff distance of 0.62 ± 0.08 mm, and a mean device distance of 0.41 ± 0.08 mm. Additionally, retrospective reconstruction results of real patient data are presented. CONCLUSIONS The presented algorithm is a novel approach for the time resolved 3D reconstruction of interventional devices from biplane fluoroscopic images, thus allowing the creation of virtual projection images from arbitrary view angles as well as virtual endoscopic 3D renderings. Availability of this technique would enhance the ability to accurately position devices in minimally invasive endovascular procedures.

A real-time system for prosthetic valve tracking

Transcatheter aortic valve replacement is a minimally invasive technique for the treatment of valvular heart disease, where an artificial valve mounted on a balloon catheter is guided to the aortic valve annulus. The balloon catheter is then expanded and displaces the diseased valve. We recently proposed an algorithm to track the 3D position, orientation and shape of a prosthetic transcatheter aortic valve using biplane fluoroscopic imaging. In this work, we present a real time hardware and software implementation of this prosthetic valve tracking method. A prototype was implemented which gathers fluoroscopic images from the angiography system via a research interface. A dynamic point cloud model of the valve is then used to estimate the 3D position, orientation and shape by minimizing a cost function. The cost function is implemented using parallel processing on graphics processing units to improve the performance. The system includes 3D rendering of the valve model and additional anatomy for visualization. The timing performance of the system was evaluated using a plastic cylinder phantom and a prosthetic valve mounted on a balloon catheter. The total computation time per frame for tracking and visualization using two different valve models was 46.11 ms and 43.88 ms respectively. This would allow frame rates of up to 21.69 frames per second. The target registration error of the estimated valve model was 1.22 ± 0.29 mm. Combined with 3D echocardiographic imaging, this technique would enable real time image guidance in 3D, where both the prosthetic valve and the soft tissue of the heart are visible.

Clinical feasibility of x-ray based pose estimation of a transthoracic echo probe using attached fiducials

Co-registered display of x-ray fluoroscopy (XRF) and echocardiography during structural heart interventions can provide visualization of both catheter-based devices and soft tissue anatomy. For transesophageal echocardiography (TEE), registration can be achieved by estimating the probe pose in the x-ray image. This work investigated the potential clinical requirements for a similar approach using a transthoracic echocardiography (TTE) probe with attached x-ray-visible fiducials. Clinically, the limited number of acoustic windows for TTE dictates probe positioning on the chest, and the interventional task drives the positioning of the C-arm gantry of the x-ray system. A fiducial apparatus must be compatible with these positions and allow for accurate 3D probe pose estimation. TTE imaging of the aortic and mitral valves was performed on eight healthy subjects to determine typical 3D probe positioning in parasternal and apical acoustic windows. This data was incorporated into software that allowed for the simulation of different 3D configurations of fiducials relative to the probe, patient and x-ray system. Three candidate fiducial designs were identified, each consisting of two 40-mm diameter rings with 16 3-mm diameter spheres. X-ray imaging was simulated for C-arm angles of 30° RAO, PA, and 30° LAO, each with cranial-caudal angles typical of a TAVR procedure. Subjectively graded TTE image quality was highest for the parasternal long axis window. A fiducial configuration for the parasternal long window was identified which yielded median 3D TRE ranging from 0.44 mm to 1.04 mm in simulations. An experimental prototype of this design produced a measured 3D TRE of 1.25±0.19 mm.

Feasibility of intra-acquisition motion correction for 4D DSA reconstruction for applications in the thorax and abdomen.

The recently proposed 4D DSA technique enables reconstruction of time resolved 3D volumes from two C-arm CT acquisitions. This provides information on the blood flow in neurovascular applications and can be used for the diagnosis and treatment of vascular diseases. For applications in the thorax and abdomen, respiratory motion can prevent successful 4D DSA reconstruction and cause severe artifacts. The purpose of this work is to propose a novel technique for motion compensated 4D DSA reconstruction to enable applications in the thorax and abdomen. The approach uses deformable 2D registration to align the projection images of a non-contrast and a contrast enhanced scan. A subset of projection images is then selected, which are acquired in a similar respiratory state and an iterative simultaneous multiplicative algebraic reconstruction is applied to determine a 3D constraint volume. A 2D-3D registration step then aligns the remaining projection images with the 3D constraint volume. Finally, a constrained back-projection is performed to create a 3D volume for each projection image. A pig study has been performed, where 4D DSA acquisitions were performed with and without respiratory motion to evaluate the feasibility of the approach. The dice similarity coefficient between the reference 3D constraint volume and the motion compensated reconstruction was 51.12 % compared to 35.99 % without motion compensation. This technique could improve the workflow for procedures in interventional radiology, e.g. liver embolizations, where changes in blood flow have to be monitored carefully.

Localization of cardiac volume and patient features in inverse geometry x-ray fluoroscopy

The scanning-beam digital x-ray (SBDX) system is an inverse geometry x-ray fluoroscopy technology that performs real-time tomosynthesis at planes perpendicular to the source-detector axis. The live display is a composite image which portrays sharp features (e.g. coronary arteries) extracted from a 16 cm thick reconstruction volume. We present a method for automatically determining the position of the cardiac volume prior to acquisition of a coronary angiogram. In the algorithm, a single non-contrast frame is reconstructed over a 44 cm thickness using shift-and-add digital tomosynthesis. Gradient filtering is applied to each plane to emphasize features such as the cardiomediastinal contour, diaphragm, and lung texture, and then sharpness vs. plane position curves are generated. Three sharpness metrics were investigated: average gradient in the bright field, maximum gradient, and the number of normalized gradients exceeding 0.5. A model correlating the peak sharpness in a non-contrast frame and the midplane of the coronary arteries in a contrast-enhanced frame was established using 37 SBDX angiographic loops (64-136 kg human subjects, 0-30° cranial- caudal). The average gradient in the bright field (primarily lung) and the number of normalized gradients >0.5 each yielded peaks correlated to the coronary midplane. The rms deviation between the predicted and true midplane was 1.57 cm. For a 16 cm reconstruction volume and the 5.5-11.5 cm thick cardiac volumes in this study, midplane estimation errors of 2.25-5.25 cm were tolerable. Tomosynthesis-based localization of cardiac volume is feasible. This technique could be applied prior to coronary angiography, or to assist in isocentering the patient for rotational angiography.

Feature-based respiratory motion tracking in native fluoroscopic sequences for dynamic roadmaps during minimally invasive procedures in the thorax and abdomen.

Fluoroscopic image guidance for minimally invasive procedures in the thorax and abdomen suffers from respiratory and cardiac motion, which can cause severe subtraction artifacts and inaccurate image guidance. This work proposes novel techniques for respiratory motion tracking in native fluoroscopic images as well as a model based estimation of vessel deformation. This would allow compensation for respiratory motion during the procedure and therefore simplify the workflow for minimally invasive procedures such as liver embolization. The method first establishes dynamic motion models for both the contrast-enhanced vasculature and curvilinear background features based on a native (non-contrast) and a contrast-enhanced image sequence acquired prior to device manipulation, under free breathing conditions. The model of vascular motion is generated by applying the diffeomorphic demons algorithm to an automatic segmentation of the subtraction sequence. The model of curvilinear background features is based on feature tracking in the native sequence. The two models establish the relationship between the respiratory state, which is inferred from curvilinear background features, and the vascular morphology during that same respiratory state. During subsequent fluoroscopy, curvilinear feature detection is applied to determine the appropriate vessel mask to display. The result is a dynamic motioncompensated vessel mask superimposed on the fluoroscopic image. Quantitative evaluation of the proposed methods was performed using a digital 4D CT-phantom (XCAT), which provides realistic human anatomy including sophisticated respiratory and cardiac motion models. Four groups of datasets were generated, where different parameters (cycle length, maximum diaphragm motion and maximum chest expansion) were modified within each image sequence. Each group contains 4 datasets consisting of the initial native and contrast enhanced sequences as well as a sequence, where the respiratory motion is tracked. The respiratory motion tracking error was between 1.00 % and 1.09 %. The estimated dynamic vessel masks yielded a Sørensen-Dice coefficient between 0.94 and 0.96. Finally, the accuracy of the vessel contours was measured in terms of the 99th percentile of the error, which ranged between 0.64 and 0.96 mm. The presented results show that the approach is feasible for respiratory motion tracking and compensation and could therefore considerably improve the workflow of minimally invasive procedures in the thorax and abdomen

Dynamic tracking of prosthetic valve motion and deformation from bi-plane x-ray views: feasibility study

Transcatheter aortic valve replacement (TAVR) requires navigation and deployment of a prosthetic valve within the aortic annulus under fluoroscopic guidance. To support improved device visualization in this procedure, this study investigates the feasibility of frame-by-frame 3D reconstruction of a moving and expanding prosthetic valve structure from simultaneous bi-plane x-ray views. In the proposed method, a dynamic 3D model of the valve is used in a 2D/3D registration framework to obtain a reconstruction of the valve. For each frame, valve model parameters describing position, orientation, expansion state, and deformation are iteratively adjusted until forward projections of the model match both bi-plane views. Simulated bi-plane imaging of a valve at different signal-difference-to-noise ratio (SDNR) levels was performed to test the approach. 20 image sequences with 50 frames of valve deployment were simulated at each SDNR. The simulation achieved a target registration error (TRE) of the estimated valve model of 0.93 ± 2.6 mm (mean ± S.D.) for the lowest SDNR of 2. For higher SDNRs (5 to 50) a TRE of 0.04 mm ± 0.23 mm was achieved. A tabletop phantom study was then conducted using a TAVR valve. The dynamic 3D model was constructed from high resolution CT scans and a simple expansion model. TRE was 1.22 ± 0.35 mm for expansion states varying from undeployed to fully deployed, and for moderate amounts of inter-frame motion. Results indicate that it is feasible to use bi-plane imaging to recover the 3D structure of deformable catheter devices.

Biplane reconstruction and visualization of virtual endoscopic and fluoroscopic views for interventional device navigation

Biplane fluoroscopic imaging is an important tool for minimally invasive procedures for the treatment of cerebrovascular diseases. However, finding a good working angle for the C-arms of the angiography system as well as navigating based on the 2D projection images can be a difficult task. The purpose of this work is to propose a novel 4D reconstruction algorithm for interventional devices from biplane fluoroscopy images and to propose new techniques for a better visualization of the results. The proposed reconstruction methods binarizes the fluoroscopic images using a dedicated noise reduction algorithm for curvilinear structures and a global thresholding approach. A topology preserving thinning algorithm is then applied and a path search algorithm minimizing the curvature of the device is used to extract the 2D device centerlines. Finally, the 3D device path is reconstructed using epipolar geometry. The point correspondences are determined by a monotonic mapping function that minimizes the reconstruction error. The three dimensional reconstruction of the device path allows the rendering of virtual fluoroscopy images from arbitrary angles as well as 3D visualizations like virtual endoscopic views or glass pipe renderings, where the vessel wall is rendered with a semi-transparent material. This work also proposes a combination of different visualization techniques in order to increase the usability and spatial orientation for the user. A combination of synchronized endoscopic and glass pipe views is proposed, where the virtual endoscopic camera position is determined based on the device tip location as well as the previous camera position using a Kalman filter in order to create a smooth path. Additionally, vessel centerlines are displayed and the path to the target is highlighted. Finally, the virtual endoscopic camera position is also visualized in the glass pipe view to further improve the spatial orientation. The proposed techniques could considerably improve the workflow of minimally invasive procedures for the treatment of cerebrovascular diseases.