Daniel Toth

Interactive visualization for scar transmurality in cardiac resynchronization therapy

Heart failure is a serious disease affecting about 23 million people worldwide. Cardiac resynchronization therapy is used to treat patients suffering from symptomatic heart failure. However, 30% to 50% of patients have limited clinical benefit. One of the main causes is suboptimal placement of the left ventricular lead. Pacing in areas of myocardial scar correlates with poor clinical outcomes. Therefore precise knowledge of the individual patient’s scar characteristics is critical for delivering tailored treatments capable of improving response rates. Current research methods for scar assessment either map information to an alternative non-anatomical coordinate system or they use the image coordinate system but lose critical information about scar extent and scar distribution. This paper proposes two interactive methods for visualizing relevant scar information. A 2-D slice based approach with a scar mask overlaid on a 16 segment heart model and a 3-D layered mesh visualization which allows physicians to scroll through layers of scar from endocardium to epicardium. These complementary methods enable physicians to evaluate scar location and transmurality during planning and guidance. Six physicians evaluated the proposed system by identifying target regions for lead placement. With the proposed method more target regions could be identified.

Adaption of 3D Models to 2D X-Ray Images during Endovascular Abdominal Aneurysm Repair

Endovascular aneurysm repair EVAR has been gaining popularity over open repair of abdominal aortic aneurysms AAAs in the recent years. This paper describes a distortion correction approach to be applied during the EVAR cases. In a novel workflow, models meshes of the aorta and its branching arteries generated from preoperatively acquired computed tomography CT scans are overlayed with interventionally acquired fluoroscopic images. The overlay provides an arterial roadmap for the operator, with landmarks LMs marking the ostia, which are critical for stent placement. As several endovascular devices, such as angiographic catheters, are inserted, the anatomy may be distorted. The distortion reduces the accuracy of the overlay. To overcome the mismatch, the aortic and the iliac meshes are adapted to a device seen in uncontrasted intraoperative fluoroscopic images using the skeletonbased as-rigid-as-possible ARAP method. The deformation was evaluated by comparing the distance between an ostium and the corresponding LM prior to and after the deformation. The central positions of the ostia were marked in digital subtraction angiography DSA images as ground truth. The mean Euclidean distance in the image plane was reduced from 19.81±17.14mm to 4.56±2.81 mm.

A Planning and Guidance Platform for Cardiac Resynchronization Therapy

Patients with drug-refractory heart failure can greatly benefit from cardiac resynchronization therapy (CRT). A CRT device can resynchronize the contractions of the left ventricle (LV) leading to reduced mortality. Unfortunately, 30%–50% of patients do not respond to treatment when assessed by objective criteria such as cardiac remodeling. A significant contributing factor is the suboptimal placement of the LV lead. It has been shown that placing this lead away from scar and at the point of latest mechanical activation can improve response rates. This paper presents a comprehensive and highly automated system that uses scar and mechanical activation to plan and guide CRT procedures. Standard clinical preoperative magnetic resonance imaging is used to extract scar and mechanical activation information. The data are registered to a single 3-D coordinate system and visualized in novel 2-D and 3-D American Heart Association plots enabling the clinician to select target segments. During the procedure, the planning information is overlaid onto live fluoroscopic images to guide lead deployment. The proposed platform has been used during 14 CRT procedures and validated on synthetic, phantom, volunteer, and patient data.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

The clinical applications and benefits of multi-modal image registration are wide-ranging and well established. Current image based approaches exploit cross-modality information, such as landmarks or anatomical structures, which is visible in both modalities. A lack of cross-modality information can prohibit accurate automatic registration. This paper proposes a novel approach for MR to X-ray image registration which uses prior knowledge of adjacent anatomical structures to enable registration without cross-modality image information. The registration of adjacent structures formulated as a partial surface registration problem which is solved using a globally optimal ICP method. The practical clinical application of the approach is demonstrated on an image guided cardiac resynchronization therapy procedure. The left ventricle (segmented from pre-operative MR) is registered to the coronary vessel tree (extracted from intra-operative fluoroscopic images). The proposed approach is validated on synthetic and phantom data, where the results show a good comparison with the ground truth registrations. The vertex-to-vertex MAE was \(3.28\pm 1.18\) mm for 10 X-ray image pairs of the phantom.

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

Highlights3D/2D registration using adjacent anatomical structures is proposed.Superabundant 3D vessel reconstruction is performed without point correspondences.A globally optimal registration method is extended with dynamic outlier rejection.Novel evaluation framework using previously implanted artificial valves is proposed. Graphical abstract Figure. No caption available. ABSTRACT 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.

Image Integration to Guide Wireless Endocardial LV Electrode Implantation for CRT

Suboptimal left ventricular (LV) lead placement in myocardial scar (fibrosis) is associated with cardiac resynchronization therapy (CRT) nonresponse [(1)][1]. Image guidance (echocardiography and cardiac magnetic resonance [CMR]) avoiding fibrosis and targeting late mechanical activation may improve

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.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.

3D/2D model-to-image registration by imitation learning for cardiac procedures

PurposeIn cardiac interventions, such as cardiac resynchronization therapy (CRT), image guidance can be enhanced by involving preoperative models. Multimodality 3D/2D registration for image guidance, however, remains a significant research challenge for fundamentally different image data, i.e., MR to X-ray. Registration methods must account for differences in intensity, contrast levels, resolution, dimensionality, field of view. Furthermore, same anatomical structures may not be visible in both modalities. Current approaches have focused on developing modality-specific solutions for individual clinical use cases, by introducing constraints, or identifying cross-modality information manually. Machine learning approaches have the potential to create more general registration platforms. However, training image to image methods would require large multimodal datasets and ground truth for each target application.MethodsThis paper proposes a model-to-image registration approach instead, because it is common in image-guided interventions to create anatomical models for diagnosis, planning or guidance prior to procedures. An imitation learning-based method, trained on 702 datasets, is used to register preoperative models to intraoperative X-ray images.ResultsAccuracy is demonstrated on cardiac models and artificial X-rays generated from CTs. The registration error was

3D reconstruction of coronary veins from a single X-ray fluoroscopic image and pre-operative MR

2.92\pm 2.22\,\hbox { mm}