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, co-registration 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

Edge-Enhancement DenseNet for X-ray Fluoroscopy Image Denoising in Cardiac Electrophysiology Procedures

PURPOSE: Reducing X‐ray dose increases safety in cardiac electrophysiology procedures but also increases image noise and artifacts which may affect the discernibility of devices and anatomical cues. Previous denoising methods based on convolutional neural networks (CNNs) have shown improvements in the quality of low‐dose X‐ray fluoroscopy images but may compromise clinically important details required by cardiologists. METHODS: In order to obtain denoised X‐ray fluoroscopy images whilst preserving details, we propose a novel deep‐learning‐based denoising framework, namely edge‐enhancement densenet (EEDN), in which an attention‐awareness edge‐enhancement module is designed to increase edge sharpness. In this framework, a CNN‐based denoiser is first used to generate an initial denoising result. Contours representing edge information are then extracted using an attention block and a group of interacted ultra‐dense blocks for edge feature representation. Finally, the initial denoising result and enhanced edges are combined to generate the final X‐ray image. The proposed denoising framework was tested on a total of 3262 clinical images taken from 100 low‐dose X‐ray sequences acquired from 20 patients. The performance was assessed by pairwise voting from five cardiologists as well as quantitative indicators. Furthermore, we evaluated our technique's effect on catheter detection using 416 images containing coronary sinus catheters in order to examine its influence as a pre‐processing tool. RESULTS: The average signal‐to‐noise ratio of X‐ray images denoised with EEDN was 24.5, which was 2.2 times higher than that of the original images. The accuracy of catheter detection from EEDN denoised sequences showed no significant difference compared with their original counterparts. Moreover, EEDN received the highest average votes in our clinician assessment when compared to our existing technique and the original images. CONCLUSION: The proposed deep learning‐based framework shows promising capability for denoising interventional X‐ray fluoroscopy images. The results from the catheter detection show that the network does not affect the results of such an algorithm when used as a pre‐processing step. The extensive qualitative and quantitative evaluations suggest that the network may be of benefit to reduce radiation dose when applied in real time in the catheter laboratory

Image-based view-angle independent cardiorespiratory motion gating and coronary sinus catheter tracking for x-ray-guided cardiac electrophysiology procedures

Determination of the cardiorespiratory phase of the heart has numerous applications during cardiac imaging. In this article we propose a novel view-angle independent near-real time cardiorespiratory motion gating and coronary sinus (CS) catheter tracking technique for x-ray fluoroscopy images that are used to guide cardiac electrophysiology procedures. The method is based on learning CS catheter motion using principal component analysis and then applying the derived motion model to unseen images taken at arbitrary projections, using the epipolar constraint. This method is also able to track the CS catheter throughout the x-ray images in any arbitrary subsequent view. We also demonstrate the clinical application of our model on rotational angiography sequences. We validated our technique in normal and very low dose phantom and clinical datasets. For the normal dose clinical images we established average systole, end-expiration and end-inspiration gating success rates of 100%, 85.7%, and 92.3%, respectively. For very low dose applications, the technique was able to track the CS catheter with median errors not exceeding 1 mm for all tracked electrodes. Average gating success rates of 80.3%, 71.4%, and 69.2% were established for the application of the technique on clinical datasets, even with a dose reduction of more than 10 times. In rotational sequences at normal dose, CS tracking median errors were within 1.2 mm for all electrodes, and the gating success rate was 100%, for view angles from RAO 90° to LAO 90°. This view-angle independent technique can extract clinically useful cardiorespiratory motion information using x-ray doses significantly lower than those currently used in clinical practice

Radiation Damping of a BPS Monopole; an Implication to S-duality

The radiation reaction of a BPS monopole in the presence of incident electromagnetic waves as well as massless Higgs waves is analyzed classically. The reactive forces are compared to those of $W$ boson that is interpreted as a dual partner of the BPS monopole. It is shown that the damping of acceleration is dual to each other, while in the case of finite size effects the duality is broken explicitly. Their implications on the duality are discussed.Comment: 20 pages, uses revtex, changes in reference

Surface flattening of the human left atrium and proof-of-concept clinical applications

Surface flattening in medical imaging has seen widespread use in neurology and more recently in cardiology to describe the left ventricle using the bull's-eye plot. The method is particularly useful to standardize the display of functional information derived from medical imaging and catheter-based measurements. We hypothesized that a similar approach could be possible for the more complex shape of the left atrium (LA) and that the surface flattening could be useful for the management of patients with atrial fibrillation (AF). We implemented an existing surface mesh parameterization approach to flatten and unfold 3D LA models. Mapping errors going from 2D to 3D and the inverse were investigated both qualitatively and quantitatively using synthetic data of regular shapes and computer tomography scans of an anthropomorphic phantom. Testing of the approach was carried out using data from 14 patients undergoing ablation treatment for AF. 3D LA meshes were obtained from magnetic resonance imaging and electroanatomical mapping systems. These were unfolded using the developed approach and used to demonstrate proof-of-concept applications, such as the display of scar information, electrical information and catheter position. The work carried out shows that the unfolding of complex cardiac structures, such as the LA, is feasible and has several potential clinical uses for the management of patients with AF.</p

3D/2D Registration with Superabundant Vessel Reconstruction for Cardiac Resynchronization Therapy

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Novel system for real-time integration of 3-D echocardiography and fluoroscopy for image-guided cardiac interventions: Preclinical validation and clinical feasibility evaluation

© 2015 IEEE. Real-time imaging is required to guide minimally invasive catheter-based cardiac interventions. While transesophageal echocardiography allows for high-quality visualization of cardiac anatomy, X-ray fluoroscopy provides excellent visualization of devices. We have developed a novel image fusion system that allows real-time integration of 3-D echocardiography and the X-ray fluoroscopy. The system was validated in the following two stages: 1) preclinical to determine function and validate accuracy; and 2) in the clinical setting to assess clinical workflow feasibility and determine overall system accuracy. In the preclinical phase, the system was assessed using both phantom and porcine experimental studies. Median 2-D projection errors of 4.5 and 3.3 mm were found for the phantom and porcine studies, respectively. The clinical phase focused on extending the use of the system to interventions in patients undergoing either atrial fibrillation catheter ablation (CA) or transcatheter aortic valve implantation (TAVI). Eleven patients were studied with nine in the CA group and two in the TAVI group. Successful real-time view synchronization was achieved in all cases with a calculated median distance error of 2.2 mm in the CA group and 3.4 mm in the TAVI group. A standard clinical workflow was established using the image fusion system. These pilot data confirm the technical feasibility of accurate real-time echo-fluoroscopic image overlay in clinical practice, which may be a useful adjunct for real-time guidance during interventional cardiac procedures

Automatic Segmentation of Left Atrial Scar from Delayed-Enhancement Magnetic Resonance Imaging

Abstract. Delayed-enhancement magnetic resonance imaging is an effective technique for imaging left atrial (LA) scars both pre-and post-radio-frequency ablation for the treatment of atrial fibrillation. Existing techniques for LA scar segmentation require expert manual interaction making them tedious and prone to high observer variability. In this paper, we propose a novel automatic segmentation algorithm for segmenting LA scar based on a probabilistic tissue intensity model. This is implemented as a Markov random field-based energy formulation and solved using graph-cuts. It was evaluated against an existing semi-automatic approach and expert manual segmentations using 9 patient data sets. Surface representations were used to compare the methods. The segmented LA scar was expressed as a percentage of the total LA surface. Statistical analysis showed that the novel algorithm was not significantly different to the manual method and that it compared more favorably with this than the semi-automatic approach