Identifying and tracking a guide wire in the coronary arteries during angioplasty from X-ray images

Template-based balloon-marker and guidewire detection for coronary stents in cardiac fluoroscopy

The placement and visualization of coronary stents during fluoroscopy depends mainly on the detection of balloon markers and their connecting guidewires. In this paper, a novel template-based approach is proposed to detect balloon markers and guidewires in cardiac fluoroscopic images. In particular, guidewires are detected based on balloon markers only, without prior knowledge of the background or guidewire elements. Also, while earlier techniques used circular models of balloon markers, we propose a more realistic elliptical model. Training and the testing datasets for balloon marker and guidewire detection were collected from different Cathlab systems and annotated by an application specialist with 10 years of experience in this field. The balloon-marker detector achieved a precision of 98.5%. Within 3-pixel tolerance, the guidewire detector achieved a matching percentage of 99.5% with the true guidewire using a customized evaluation method. Moreover, the guidewire detector achieved a mean Hausdorff distance of 3.3 pixels (0.6 mm) and a longest-common-substring (LCS) distance with a mean matching percentage of 87% within 1-pixel tolerance. Clinical Relevance- The proposed novel technique of detecting the guidewire offers a constant computational time and insensitivity to the body structures or the guidewire-like elements (such as the surgical wires). This leads to improved stent visualization and reasonable processing times.

Deep learning-based framework for motion-compensated image fusion in catheterization procedures

OBJECTIVE

Augmenting X-ray (XR) fluoroscopy with 3D anatomic overlays is an essential technique to improve the guidance of the catheterization procedures. Unfortunately, cardiac and respiratory motion compromises the augmented fluoroscopy. Motion compensation methods can be applied to update the overlay of a static model with regard to respiratory and cardiac motion. We investigate the feasibility of motion detection between two fluoroscopic frames by applying a convolutional neural network (CNN). Its integration in the existing open-source software framework 3D-XGuide is demonstrated, such extending its functionality to automatic motion detection and compensation.

METHODS

The CNN is trained on reference data generated from tracking of the rapid pacing catheter tip by applying template matching with normalized cross-correlation (CC). The developed CNN motion compensation model is packaged in a standalone web service, allowing for independent use via a REST API. For testing and demonstration purposes, we have extended the functionality of 3D-XGuide navigation framework by an additional motion compensation module, which uses the displacement predictions of the standalone CNN model service for motion compensation of the static 3D model overlay. We provide the source code on GitHub under BSD license.

RESULTS

The performance of the CNN motion compensation model was evaluated on a total of 1690 fluoroscopic image pairs from ten clinical datasets. The CNN model-based motion compensation method clearly overperformed the tracking of the rapid pacing catheter tip with CC with prediction frame rates suitable for live application in the clinical setting.

CONCLUSION

A novel CNN model-based method for automatic motion compensation during fusion of 3D anatomic models with XR fluoroscopy is introduced and its integration with a real software application demonstrated. Automatic motion extraction from 2D XR images using a CNN model appears as a substantial improvement for reliable augmentation during catheter interventions.

A GPU-Based Automatic Approach for Guide Wire Tracking in Fluoroscopic Sequences

Guide wire tracking in fluoroscopic images has done a significant task in assisting the physicians during radiology-aided interventions. Many groups have tried to detect the guide wire from the fluoroscopic images based on the image properties. The main challenge is that manual intervention is required during the detection. Other groups try to introduce localizers to track guide wires during intervention, which requires additional hardware equipment, and may intervene with the traditional clinical routines. Machine learning methods are also exploited. Although such methods may provide accurate tracking, they often require large amount of data and training time.

In this paper, we propose a GPU-based fast and automatic approach to track guide wires in fluoroscopic sequences. We propose a multi-scale filtering and gradient vector field-based real-time tracking method for guide wire tracking from fluoroscopic images. To improve calculation efficiency and meet real-time application requirement, we propose a GPU-based acceleration scheme, and also a Bayesian filter-like motion tracking method to limit the guide wire tracking to a smaller range to improve calculation efficiency.

We test our proposed method on two test data sets of fluoroscopic sequences of 102 frames and 72 frames. We achieve an average guide wire detection rate of 96.7%, a false detection rate of 0.0011% and an error distance measure of 0.83 pixels for the first sequence, and 98.8%, 0.000069% and 0.85 pixels, respectively, for the second sequence. With the proposed acceleration method, we finish calculation for the first sequence in nine seconds, thus, efficiency is enhanced by 100 times with the unaccelerated algorithm.

A novel real-time computational framework for detecting catheters and rigid guidewires in cardiac catheterization procedures

Purpose

Catheters and guidewires are used extensively in cardiac catheterization procedures such as heart arrhythmia treatment (ablation), angioplasty, and congenital heart disease treatment. Detecting their positions in fluoroscopic X‐ray images is important for several clinical applications, for example, motion compensation, coregistration between 2D and 3D imaging modalities, and 3D object reconstruction.

Methods

For the generalized framework, a multiscale vessel enhancement filter is first used to enhance the visibility of wire‐like structures in the X‐ray images. After applying adaptive binarization method, the centerlines of wire‐like objects were extracted. Finally, the catheters and guidewires were detected as a smooth path which is reconstructed from centerlines of target wire‐like objects. In order to classify electrode catheters which are mainly used in electrophysiology procedures, additional steps were proposed. First, a blob detection method, which is embedded in vessel enhancement filter with no additional computational cost, localizes electrode positions on catheters. Then the type of electrode catheters can be recognized by detecting the number of electrodes and also the shape created by a series of electrodes. Furthermore, for detecting guiding catheters or guidewires, a localized machine learning algorithm is added into the framework to distinguish between target wire objects and other wire‐like artifacts. The proposed framework were tested on total 10,624 images which are from 102 image sequences acquired from 63 clinical cases.

Results

Detection errors for the coronary sinus (CS) catheter, lasso catheter ring and lasso catheter body are 0.56 ± 0.28 mm, 0.64 ± 0.36 mm, and 0.66 ± 0.32 mm, respectively, as well as success rates of 91.4%, 86.3%, and 84.8% were achieved. Detection errors for guidewires and guiding catheters are 0.62 ± 0.48 mm and success rates are 83.5%.

Conclusion

The proposed computational framework do not require any user interaction or prior models and it can detect multiple catheters or guidewires simultaneously and in real‐time. The accuracy of the proposed framework is sub‐mm and the methods are robust toward low‐dose X‐ray fluoroscopic images, which are mainly used during procedures to maintain low radiation dose.

Automatic detection of selective arterial devices for advanced visualization during abdominal aortic aneurysm endovascular repair

Here we address the automatic segmentation of endovascular devices used in the endovascular repair (EVAR) of abdominal aortic aneurysms (AAA) that deform vascular tissues. Using this approach, the vascular structure is automatically reshaped solving the issue of misregistration observed on 2D/3D image fusion for EVAR guidance. The endovascular devices we considered are the graduated pigtail catheter (PC) used for contrast injection and the stent-graft delivery device (DD). The segmentation of the DD was enhanced using an asymmetric Frangi filter. The segmented geometries were then analysed using their specific features to remove artefacts. The radiopaque markers of the PC were enhanced using a fusion of Hessian and newly introduced gradient norm shift filters. Extensive experiments were performed using a database of images taken during 28 AAA-EVAR interventions. This dataset was divided into two parts: the first half was used to optimize parameters and the second to compile performances using optimal values obtained. The radiopaque markers of the PC were detected with a sensitivity of 88.3% and a positive predictive value (PPV) of 96%. The PC can therefore be positioned with a majority of its markers localized while the artefacts were all located inside the vessel lumen. The major parts of the DD, the dilatator tip and the pusher surfaces, were detected accurately with a sensitivity of 85.9% and a PPV of 88.7%. The less visible part of the DD, the stent enclosed within the sheath, was segmented with a sensitivity of 63.4% because the radiopacity of this region is low and uneven. The centreline of the DD in this stent region was alternatively traced within a 0.74 mm mean error. The automatic segmentation of endovascular devices during EVAR is feasible and accurate; it could be useful to perform elastic registration of the vascular lumen during endovascular repair.

Real-time 3D position reconstruction of guidewire for monoplane X-ray

We present a novel real-time method for the 3D reconstruction of the guidewire using a monoplane X-ray. The method consists of two steps: (1) the backprojection step to reconstruct a 3D surface that contains the guidewire and (2) the optimization step to select a curve on the surface that is the best match under the pre-specified constraints. The proposed method utilizes a priori knowledge in the form of a volume that indicates positions of the blood vessels and thus restricts the reconstruction. The reconstruction precision is limited by the local thickness of the vessels. The method is quantitatively evaluated on five phantom datasets and qualitatively on two patient datasets. For the phantom datasets the average reconstruction error is resolution limited to 1-2 voxels and is biased in the depth direction. The worst-case reconstruction error for any point, including the guidewire tip, is not larger than the local vessel thickness. A visual inspection of results for the patient datasets shows the guidewire is always placed in the proper vessel and is aligned with the 2D image, which is sufficient for the guidewire navigation. The developed implementation achieves the processing speed of 12 fps using Core™i7 CPU 920 at 2.67 GHz.

Interventional tool tracking using discrete optimization

This work presents a novel scheme for tracking of motion and deformation of interventional tools such as guide-wires and catheters in fluoroscopic X-ray sequences. Being able to track and thus to estimate the correct positions of these tools is crucial in order to offer guidance enhancement during interventions. The task of estimating the apparent motion is particularly challenging due to the low signal-to-noise ratio (SNR) of fluoroscopic images and due to combined motion components originating from patient breathing and tool interactions performed by the physician. The presented approach is based on modeling interventional tools with B-splines whose optimal configuration of control points is determined through efficient discrete optimization. Each control point corresponds to a discrete random variable in a Markov random field (MRF) formulation where a set of labels represents the deformation space. In this context, the optimal curve corresponds to the maximum a posteriori (MAP) estimate of the MRF energy. The main motivation for employing a discrete approach is the possibility to incorporate a multi-directional search space which is robust to local minima. This is of particular interest for curve tracking under large deformation. This work analyzes feasibility of employing efficient first-order MRFs for tracking. In particular it shows how to achieve a good compromise between energy approximations and computational efficiency. Experimental results suggest to define both the external and internal energy in terms of pairwise potential functions. The method was successfully applied to the tracking of guide-wires in fluoroscopic X-ray sequences of several hundred frames which requires extremely robust techniques. Comparisons with state-of-the-art guide-wire tracking algorithms confirm the effectiveness of the proposed method.

Towards a new image guidance system for assisting transapical minimally invasive aortic valve implantation

We propose a new image guidance system for assisting transapical minimally invasive aortic valve implantation. The goal is to define the exact positioning of aortic valve prosthesis, preventing the misplacement of the valve. The proposed system consists of two stand-alone modules. First, preoperative planning software uses DynaCT images with manual anatomical landmarks to calculate the size and optimal position of the prosthesis. Second, an intraoperative system is developed for tracking of the prosthesis and the coronary ostia in 2-D fluoroscopic images. Then the safe area of implantation is defined. The preliminary experimental results of preoperative planning and intraoperative tracking system are promising.

Variational guidewire tracking using phase congruency

We present a novel method to track a guidewire in cardiac x-ray video. Using variational calculus, we derive differential equations that deform a spline, subject to intrinsic and extrinsic forces, so that it matches the image data, remains smooth, and preserves an a priori length. We analytically derive these equations from first principles, and show how they include tangential terms, which we include in our model. To address the poor contrast often observed in x-ray video, we propose using phase congruency as an image-based feature. Experimental results demonstrate the success of the method in tracking guidewires in low contrast x-ray video.

An algorithm for tracking microcatheters in fluoroscopy

Currently, a large number of endovascular interventions are performed for treatment of intracranial aneurysms. For these treatments, correct positioning of microcatheter tips, microguide wire tips, or coils is essential. Techniques to detect such devices may facilitate endovascular interventions. In this paper, we describe an algorithm for tracking of microcatheter tips during fluoroscopically guided neuroendovascular interventions. A sequence of fluoroscopic images (1,024 x 1,024 x 12 bits) was acquired using a C-arm angiography system as a microcatheter was passed through a carotid phantom which was on top of a head phantom. The carotid phantom was a silicone cylinder containing a simulated vessel with the shape and curvatures of the internal carotid artery. The head phantom consisted of a human skull and tissue-equivalent material. To detect the microcatheter in a given fluoroscopic frame, a background image consisting of an average of the four previous frames is subtracted from the current frame, the resulting image is filtered using a matched filter, and the position of maximum intensity in the filtered image is taken as the catheter tip position in the current frame. The distance between the tracked position and the correct position (error distance) was measured in each of the fluoroscopic images. The mean and standard deviation of the error distance values were 0.277 mm (1.59 pixels) and 0.26 mm (1.5 pixels), respectively. The error distance was less than 3 pixels in the 93.0% frames. Although the algorithm intermittently failed to correctly detect the catheter, the algorithm recovered the catheter in subsequent frames.

Microcatheter tip enhancement in fluoroscopy: a comparison of techniques

We compared three techniques for enhancement of microcatheter tips in fluoroscopic images: conventional subtraction technique (CST); averaged image subtraction technique (AIST), which we have developed; and double average filtering (DAF) technique, which uses nonlinear background estimates. A pulsed fluoroscopic image sequence was obtained as a microcatheter was passed through a carotid phantom that was on top of a head phantom. The carotid phantom was a silicone cylinder containing a simulated vessel with the shape and curvatures of the internal carotid artery. The three techniques were applied to the images of the sequence, then the catheter tip was manually identified in each image, and 100 x 100 pixel images, centered at the indicated microcatheter tip positions, were extracted for the evaluations. The signal-to-noise ratio (SNR) was calculated in each of the extracted images from which the mean value of the SNR and its standard deviation (SD) were calculated for each technique. The mean values and the standard deviations were 4.36 (SD 3.40) for CST, 6.34 (SD 3.62) for AIST, and 3.55 (SD 1.27) for DAF. AIST had a higher SNR compared to CST in almost all frames. Although DAF yielded the smallest mean SNR value, it yielded the best SNR in those frames in which the microcatheter tip did not move between frames. We conclude that AIST provides the best SNR for a moving microcatheter tip and that DAF is optimal for a temporarily stationary microcatheter tip.

Guide-wire tracking during endovascular interventions

A method is presented to extract and track the position of a guide wire during endovascular interventions under X-ray fluoroscopy. The method can be used to improve guide-wire visualization in low-quality fluoroscopic images and to estimate the position of the guide wire in world coordinates. A two-step procedure is utilized to track the guide wire in subsequent frames. First, a rough estimate of the displacement is obtained using a template-matching procedure. Subsequently, the position of the guide wire is determined by fitting a spline to a feature image. The feature images that have been considered enhance line-like structures on: 1) the original images; 2) subtraction images; and 3) preprocessed images in which coherent structures are enhanced. In the optimization step, the influence of the scale at which the feature is calculated and the additional value of using directional information is investigated. The method is evaluated on 267 frames from ten clinical image sequences. Using the automatic method, the guide wire could be tracked in 96% of the frames, with a similar accuracy to three observers, although the position of the tip was estimated less accurately.

In vivo finite element model-based image analysis of pacemaker lead mechanics

BACKGROUND

Fractures of implanted pacemaker leads are currently identified by inspecting radiographic images without making full use of a priori known material and structural information. Moreover, lead designers are unable to incorporate clinical image data into analyses of lead mechanics.

METHODS

A novel finite element/active contour method was developed to quantify the in vivo mechanics of implanted leads by estimating the distributions of stress, strain, and traction using biplane videoradiographic images. The nonlinear equilibrium equations governing a thin elastic beam undergoing 3-D large rotation were solved using one-dimensional isoparametric finite elements. External forces based on local image greyscale values were computed from each pair of images using a perspective transformation governing the relationship between the image planes.

RESULTS

Cantilever beam forward solution results were within 0.2% of the analytic solution for a wide range of applied loads. The finite element/active contour model was able to reproduce the principal curvatures of a synthetic helix within 3% of the analytic solution and estimates of the helix's geometric torsion were within 20% of the analytic solution. Applying the method to biplane videoradiographic images of a lead acutely implanted in an anesthetized dog resulted in expected variations in curvature and bending stress between compliant and rigid segments of the lead.

CONCLUSIONS

By incorporating knowledge about lead geometric and material properties, the 3-D finite element/active contour method regularizes the image reconstruction problem and allows for more quantitative and automatic assessment of implanted lead mechanics.