A Tensor-Based Catheter and Wire Detection and Tracking Framework and Its Clinical Applications.

OBJECTIVE: Catheters and wires are used extensively in cardiac catheterization procedures. Detecting their positions in fluoroscopic X-ray images is important for several clinical applications such as motion compensation and co-registration between 2D and 3D imaging modalities. Detecting the complete length of a catheter or wire object as well as electrode positions on the catheter or wire is a challenging task. METHOD: In this paper, an automatic detection framework for catheters and wires is developed. It is based on path reconstruction from image tensors, which are eigen direction vectors generated from a multiscale vessel enhancement filter. A catheter or a wire object is detected as the smooth path along those eigen direction vectors. Furthermore, a real-time tracking method based on a template generated from the detection method was developed. RESULTS: The proposed framework was tested on a total of 7,754 X-ray images. Detection errors for catheters and guidewires are 0.56 ± 0.28 mm and 0.68 ± 0.33 mm, respectively. The proposed framework was also tested and validated in two clinical applications. For motion compensation using catheter tracking, the 2D target registration errors (TRE) of 1.8 mm ± 0.9 mm was achieved. For co-registration between 2D X-ray images and 3D models from MRI images, a TRE of 2.3 ± 0.9 mm was achieved. CONCLUSION: A novel and fully automatic detection framework and its clinical applications are developed. SIGNIFICANCE: The proposed framework can be applied to improve the accuracy of image-guidance systems for cardiac catheterization procedures.

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.

Real-time registration of 3D echo to x-ray fluoroscopy based on cascading classifiers and image registration.

Three-dimensional (3D) transesophageal echocardiography (TEE) is one of the most significant advances in cardiac imaging. Although TEE provides real-time 3D visualization of heart tissues and blood vessels and has no ionizing radiation, x-ray fluoroscopy still dominates in guidance of cardiac interventions due to TEE having a limited field of view and poor visualization of surgical instruments. Therefore, fusing 3D echo with live x-ray images can provide a better guidance solution. This paper proposes a novel framework for image fusion by detecting the pose of the TEE probe in x-ray images in real-time. The framework does not require any manual initialization. Instead it uses a cascade classifier to compute the position and in-plane rotation angle of the TEE probe. The remaining degrees of freedom are determined by fast marching against a template library. The proposed framework is validated on phantoms and patient data. The target registration error for the phantom was 2.1 mm. In addition, 10 patient datasets, seven of which were acquired from cardiac electrophysiology procedures and three from trans-catheter aortic valve implantation procedures, were used to test the clinical feasibility as well as accuracy. A mean registration error of 2.6 mm was achieved, which is well within typical clinical requirements.

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.

LV function validation of computer-assisted interventional system for cardiac resyncronisation therapy.

PURPOSE: Cardiac resynchronisation therapy (CRT) is an established treatment for symptomatic patients with heart failure, a prolonged QRS duration, and impaired left ventricular (LV) function; however, non-response rates remain high. Recently proposed computer-assisted interventional platforms for CRT provide new routes to improving outcomes. Interventional systems must process information in an accurate, fast and highly automated way that is easy for the interventional cardiologists to use. In this paper, an interventional CRT platform is validated against two offline diagnostic tools to demonstrate that accurate information processing is possible in the time critical interventional setting. METHODS: The study consisted of 3 healthy volunteers and 16 patients with heart failure and conventional criteria for CRT. Data analysis included the calculation of end-diastolic volume, end-systolic volume, stroke volume and ejection fraction; computation of global volume over the cardiac cycle as well as time to maximal contraction expressed as a percentage of the total cardiac cycle. RESULTS: The results showed excellent correlation ([Formula: see text] values of [Formula: see text] and Pearson correlation coefficient of [Formula: see text]) with comparable offline diagnostic tools. CONCLUSION: Results confirm that our interventional system has good accuracy in everyday clinical practice and can be of clinical utility in identification of CRT responders and LV function assessment.

Standardized unfold mapping: a technique to permit left atrial regional data display and analysis.

PURPOSE: Left atrial arrhythmia substrate assessment can involve multiple imaging and electrical modalities, but visual analysis of data on 3D surfaces is time-consuming and suffers from limited reproducibility. Unfold maps (e.g., the left ventricular bull's eye plot) allow 2D visualization, facilitate multimodal data representation, and provide a common reference space for inter-subject comparison. The aim of this work is to develop a method for automatic representation of multimodal information on a left atrial standardized unfold map (LA-SUM). METHODS: The LA-SUM technique was developed and validated using 18 electroanatomic mapping (EAM) LA geometries before being applied to ten cardiac magnetic resonance/EAM paired geometries. The LA-SUM was defined as an unfold template of an average LA mesh, and registration of clinical data to this mesh facilitated creation of new LA-SUMs by surface parameterization. RESULTS: The LA-SUM represents 24 LA regions on a flattened surface. Intra-observer variability of LA-SUMs for both EAM and CMR datasets was minimal; root-mean square difference of 0.008 ± 0.010 and 0.007 ± 0.005 ms (local activation time maps), 0.068 ± 0.063 gs (force-time integral maps), and 0.031 ± 0.026 (CMR LGE signal intensity maps). Following validation, LA-SUMs were used for automatic quantification of post-ablation scar formation using CMR imaging, demonstrating a weak but significant relationship between ablation force-time integral and scar coverage (R 2 = 0.18, P < 0.0001). CONCLUSIONS: The proposed LA-SUM displays an integrated unfold map for multimodal information. The method is applicable to any LA surface, including those derived from imaging and EAM systems. The LA-SUM would facilitate standardization of future research studies involving segmental analysis of the LA.

3D/2D Registration with superabundant vessel reconstruction for cardiac resynchronization therapy.

A key component of image guided interventions is the registration of preoperative and intraoperative images. Classical registration approaches rely on cross-modality information; however, in modalities such as MRI and X-ray there may not be sufficient cross-modality information. This paper proposes a fundamentally different registration approach which uses adjacent anatomical structures with superabundant vessel reconstruction and dynamic outlier rejection. In the targeted clinical scenario of cardiac resynchronization therapy (CRT) delivery, preoperative, non contrast-enhanced, MRI is registered to intraoperative, contrasted X-ray fluoroscopy. The adjacent anatomical structures are the left ventricle (LV) from MRI and the coronary veins reconstructed from two contrast-enhanced X-ray images. The novel concept of superabundant vessel reconstruction is introduced to bypass the standard reconstruction problem of establishing one-to-one correspondences. Furthermore, a new dynamic outlier rejection method is proposed, to enable globally optimal point set registration. The proposed approach has been qualitatively and quantitatively evaluated on phantom, clinical CT angiography with ground truth and clinical CRT data. A novel evaluation method is proposed for clinical CRT data based on previously implanted artificial aortic and mitral valves. The registration accuracy in 3D was 2.94 mm for the aortic and 3.86 mm for the mitral valve. The results are below the required accuracy identified by clinical partners to be the half-segment size (16.35 mm) of a standard American Heart Association (AHA) 16 segment model of the LV.

ECG imaging of ventricular tachycardia: evaluation against simultaneous non-contact mapping and CMR-derived grey zone.

ECG imaging is an emerging technology for the reconstruction of cardiac electric activity from non-invasively measured body surface potential maps. In this case report, we present the first evaluation of transmurally imaged activation times against endocardially reconstructed isochrones for a case of sustained monomorphic ventricular tachycardia (VT). Computer models of the thorax and whole heart were produced from MR images. A recently published approach was applied to facilitate electrode localization in the catheter laboratory, which allows for the acquisition of body surface potential maps while performing non-contact mapping for the reconstruction of local activation times. ECG imaging was then realized using Tikhonov regularization with spatio-temporal smoothing as proposed by Huiskamp and Greensite and further with the spline-based approach by Erem et al. Activation times were computed from transmurally reconstructed transmembrane voltages. The results showed good qualitative agreement between the non-invasively and invasively reconstructed activation times. Also, low amplitudes in the imaged transmembrane voltages were found to correlate with volumes of scar and grey zone in delayed gadolinium enhancement cardiac MR. The study underlines the ability of ECG imaging to produce activation times of ventricular electric activity-and to represent effects of scar tissue in the imaged transmembrane voltages.

Dynamic mapping of ventricular function from cardiovascular magnetic resonance imaging.

Heart failure is associated with substantial mortality and morbidity and remains the most common diagnosis in older patients. Based on experimental electrophysiologic studies, cardiac resynchronization therapy (CRT) for heart failure results in a maximum resynchronization effect when applied to the most delayed left ventricular (LV) site. Current clinical practice is to identify the optimal site using separate visualisation of scar and activation information. These must be mentally mapped into 3D, which is challenging and time-consuming for the electrophysiologist. The aim of this work is to improve patient planning for CRT by mapping propagation of mechanical activation from cardiac magnetic resonance (CMR) onto a three-dimensional plus time (3D+t) model map to assist the cardiologist in determining the optimal LV pacing site. Automatic motion analysis of the 16-segment patient-specific LV anatomical model, automatically segmented from cine MR data, was done and regional volume change curves as a function of the cardiac cycle along with intraventricular dyssynchrony indices were extracted. The regional volume information computed was then mapped onto all phases of the 3D+t CMR data, which provides a 3D+t mechanical activation map over the whole cardiac cycle. This workflow was tested on 7 patients and 3 healthy volunteers. This mapping of the regional change of volume across the LV during ventricular pacing could facilitate the selection of the optimum pacing segment at the planning stage of the procedure, and consequently decrease the number of inadequate responders to CRT.

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.

Benchmark for Algorithms Segmenting the Left Atrium From 3D CT and MRI Datasets.

Knowledge of left atrial (LA) anatomy is important for atrial fibrillation ablation guidance, fibrosis quantification and biophysical modelling. Segmentation of the LA from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images is a complex problem. This manuscript presents a benchmark to evaluate algorithms that address LA segmentation. The datasets, ground truth and evaluation code have been made publicly available through the http://www.cardiacatlas.org website. This manuscript also reports the results of the Left Atrial Segmentation Challenge (LASC) carried out at the STACOM'13 workshop, in conjunction with MICCAI'13. Thirty CT and 30 MRI datasets were provided to participants for segmentation. Each participant segmented the LA including a short part of the LA appendage trunk and proximal sections of the pulmonary veins (PVs). We present results for nine algorithms for CT and eight algorithms for MRI. Results showed that methodologies combining statistical models with region growing approaches were the most appropriate to handle the proposed task. The ground truth and automatic segmentations were standardised to reduce the influence of inconsistently defined regions (e.g., mitral plane, PVs end points, LA appendage). This standardisation framework, which is a contribution of this work, can be used to label and further analyse anatomical regions of the LA. By performing the standardisation directly on the left atrial surface, we can process multiple input data, including meshes exported from different electroanatomical mapping systems.

Repeat left atrial catheter ablation: cardiac magnetic resonance prediction of endocardial voltage and gaps in ablation lesion sets.

BACKGROUND: Studies have reported an inverse relationship between late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) signal intensity and left atrial (LA) endocardial voltage after LA ablation. However, there is controversy regarding the reproducibility of atrial LGE CMR and its ability to identify gaps in ablation lesions. Using systematic and objective techniques, this study examines the correlation between atrial CMR and endocardial voltage. METHODS AND RESULTS: Twenty patients who had previous ablation for atrial fibrillation and represented with paroxysmal atrial fibrillation or atrial tachycardia underwent preablation LGE CMR. During the ablation procedure, high-density point-by-point Carto voltage maps were acquired. Three-dimensional CMR reconstructions were registered with the Carto anatomies to allow comparison of voltage and LGE signal intensity. Signal intensities around the left and right pulmonary vein antra and along the LA roof and mitral lines on the CMR-segmented LA shells were extracted to examine differences between electrically isolated and reconnected lesions. There were a total of 6767 data points across the 20 patients. Only 119 (1.8%) of the points were ≤ 0.05 mV. There was only a weak inverse correlation between either unipolar (r = -0.18) or bipolar (r = -0.17) voltage and LGE CMR signal intensities with low voltage occurring across a large range of signal intensities. Signal intensities were not statistically different for electrically isolated and reconnected lesions. CONCLUSIONS: This study demonstrates that there is only a weak point-by-point relationship between LGE CMR and endocardial voltage in patients undergoing repeat LA ablation. Using an objective method of assessing gaps in ablation lesions, LGE CMR is unable to reliably predict sites of electrical conduction.

A statistical method for retrospective cardiac and respiratory motion gating of interventional cardiac x-ray images.

PURPOSE: Image-guided cardiac interventions involve the use of fluoroscopic images to guide the insertion and movement of interventional devices. Cardiorespiratory gating can be useful for 3D reconstruction from multiple x-ray views and for reducing misalignments between 3D anatomical models overlaid onto fluoroscopy. METHODS: The authors propose a novel and potentially clinically useful retrospective cardiorespiratory gating technique. The principal component analysis (PCA) statistical method is used in combination with other image processing operations to make our proposed masked-PCA technique suitable for cardiorespiratory gating. Unlike many previously proposed techniques, our technique is robust to varying image-content, thus it does not require specific catheters or any other optically opaque structures to be visible. Therefore, it works without any knowledge of catheter geometry. The authors demonstrate the application of our technique for the purposes of retrospective cardiorespiratory gating of normal and very low dose x-ray fluoroscopy images. RESULTS: For normal dose x-ray images, the algorithm was validated using 28 clinical electrophysiology x-ray fluoroscopy sequences (2168 frames), from patients who underwent radiofrequency ablation (RFA) procedures for the treatment of atrial fibrillation and cardiac resynchronization therapy procedures for heart failure. The authors established end-systole, end-expiration, and end-inspiration success rates of 97.0%, 97.9%, and 97.0%, respectively. For very low dose applications, the technique was tested on ten x-ray sequences from the RFA procedures with added noise at signal to noise ratio (SNR) values of √50, √10, √8, √6, √5, √2 and √1 to simulate the image quality of increasingly lower dose x-ray images. Even at the low SNR value of √2, representing a dose reduction of more than 25 times, gating success rates of 89.1%, 88.8%, and 86.8% were established. CONCLUSIONS: The proposed technique can therefore extract useful information from interventional x-ray images while minimizing exposure to ionizing radiation.

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.

Patient-specific modeling of atrial fibrosis increases the accuracy of sinus rhythm simulations and may explain maintenance of atrial fibrillation.

Left atrial fibrosis is thought to contribute to the manifestation of atrial fibrillation (AF). Late Gadolinium enhancement (LGE) MRI has the potential to image regions of low perfusion, which can be related to fibrosis. We show that a simulation with a patient-specific model including left atrial regional fibrosis derived from LGE-MRI reproduces local activation in the left atrium more precisely than the regular simulation without fibrosis. AF simulations showed a spontaneous termination of the arrhythmia in the absence of fibrosis and a stable rotor center in the presence of fibrosis. The methodology may provide a tool for a deeper understanding of the mechanisms maintaining AF and eventually also for the planning of substrate-guided ablation procedures in the future.

Cardiac magnetic resonance and electroanatomical mapping of acute and chronic atrial ablation injury: a histological validation study.

AIMS: To provide a comprehensive histopathological validation of cardiac magnetic resonance (CMR) and endocardial voltage mapping of acute and chronic atrial ablation injury. METHODS AND RESULTS: 16 pigs underwent pre-ablation T2-weighted (T2W) and late gadolinium enhancement (LGE) CMR and high-density voltage mapping of the right atrium (RA) and both were repeated after intercaval linear radiofrequency ablation. Eight pigs were sacrificed following the procedure for pathological examination. A further eight pigs were recovered for 8 weeks, before chronic CMR, repeat RA voltage mapping and pathological examination. Signal intensity (SI) thresholds from 0 to 15 SD above a reference SI were used to segment the RA in CMR images and segmentations compared with real lesion volumes. The SI thresholds that best approximated histological volumes were 2.3 SD for LGE post-ablation, 14.5 SD for T2W post-ablation and 3.3 SD for LGE chronically. T2-weighted chronically always underestimated lesion volume. Acute histology showed transmural injury with coagulative necrosis. Chronic histology showed transmural fibrous scar. The mean voltage at the centre of the ablation line was 3.3 mV pre-ablation, 0.6 mV immediately post-ablation, and 0.3 mV chronically. CONCLUSION: This study presents the first histopathological validation of CMR and endocardial voltage mapping to define acute and chronic atrial ablation injury, including SI thresholds that best match histological lesion volumes. An understanding of these thresholds may allow a more informed assessment of the underlying atrial substrate immediately after ablation and before repeat catheter ablation for atrial arrhythmias.

Novel System for Real-Time Integration of 3-D Echocardiography and Fluoroscopy for Image-Guided Cardiac Interventions: Preclinical Validation and Clinical Feasibility Evaluation.

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.

Real-time x-ray fluoroscopy-based catheter detection and tracking for cardiac electrophysiology interventions.

PURPOSE: X-ray fluoroscopically guided cardiac electrophysiology (EP) procedures are commonly carried out to treat patients with arrhythmias. X-ray images have poor soft tissue contrast and, for this reason, overlay of a three-dimensional (3D) roadmap derived from preprocedural volumetric images can be used to add anatomical information. It is useful to know the position of the catheter electrodes relative to the cardiac anatomy, for example, to record ablation therapy locations during atrial fibrillation therapy. Also, the electrode positions of the coronary sinus (CS) catheter or lasso catheter can be used for road map motion correction. METHODS: In this paper, the authors present a novel unified computational framework for image-based catheter detection and tracking without any user interaction. The proposed framework includes fast blob detection, shape-constrained searching and model-based detection. In addition, catheter tracking methods were designed based on the customized catheter models input from the detection method. Three real-time detection and tracking methods are derived from the computational framework to detect or track the three most common types of catheters in EP procedures: the ablation catheter, the CS catheter, and the lasso catheter. Since the proposed methods use the same blob detection method to extract key information from x-ray images, the ablation, CS, and lasso catheters can be detected and tracked simultaneously in real-time. RESULTS: The catheter detection methods were tested on 105 different clinical fluoroscopy sequences taken from 31 clinical procedures. Two-dimensional (2D) detection errors of 0.50 ± 0.29, 0.92 ± 0.61, and 0.63 ± 0.45 mm as well as success rates of 99.4%, 97.2%, and 88.9% were achieved for the CS catheter, ablation catheter, and lasso catheter, respectively. With the tracking method, accuracies were increased to 0.45 ± 0.28, 0.64 ± 0.37, and 0.53 ± 0.38 mm and success rates increased to 100%, 99.2%, and 96.5% for the CS, ablation, and lasso catheters, respectively. Subjective clinical evaluation by three experienced electrophysiologists showed that the detection and tracking results were clinically acceptable. CONCLUSIONS: The proposed detection and tracking methods are automatic and can detect and track CS, ablation, and lasso catheters simultaneously and in real-time. The accuracy of the proposed methods is sub-mm and the methods are robust toward low-dose x-ray fluoroscopic images, which are mainly used during EP procedures to maintain low radiation dose.

In-silico modeling of atrial repolarization in normal and atrial fibrillation remodeled state.

Atrial fibrillation (AF) is the most common cardiac arrhythmia, and the total number of AF patients is constantly increasing. The mechanisms leading to and sustaining AF are not completely understood yet. Heterogeneities in atrial electrophysiology seem to play an important role in this context. Although some heterogeneities have been used in in-silico human atrial modeling studies, they have not been thoroughly investigated. In this study, the original electrophysiological (EP) models of Courtemanche et al., Nygren et al. and Maleckar et al. were adjusted to reproduce action potentials in 13 atrial regions. The parameter sets were validated against experimental action potential duration data and ECG data from patients with AV block. The use of the heterogeneous EP model led to a more synchronized repolarization sequence in a variety of 3D atrial anatomical models. Combination of the heterogeneous EP model with a model of persistent AF-remodeled electrophysiology led to a drastic change in cell electrophysiology. Simulated Ta-waves were significantly shorter under the remodeling. The heterogeneities in cell electrophysiology explain the previously observed Ta-wave effects. The results mark an important step toward the reliable simulation of the atrial repolarization sequence, give a deeper understanding of the mechanism of atrial repolarization and enable further clinical investigations.