Multimodality imaging for patient evaluation and guidance of catheter ablation for atrial fibrillation - current status and future perspective.

Left atrial catheter ablation is an established non-pharmacological therapy for the treatment of atrial fibrillation. The importance of a noninvasive multimodality imaging approach is emphasized by the current guidelines for the various phases of the ablation work-up e.g. patient identification, therapy guidance and procedural evaluation. Advances in the capabilities of imaging modalities and the increasing cost of healthcare warrant a review of the multimodality approach. This review discusses the application of cardiac imaging for pulmonary vein and left atrial ablation divided into stages: pre-procedural stage (assessment of left atrial dimensions, left atrial appendage thrombus and pulmonary vein anatomy), peri-procedural stage (integration of anatomical and electrical information) and post-procedural stage (evaluation of efficacy by assessment of tissue properties). Each section is dedicated to one of the subtopics of a stage, allowing a thorough comparison to be made between the strengths and weaknesses of the different imaging modalities and the identification of one that exhibits the potential for a single technique approach.

A statistical model of catheter motion from interventional x-ray images: application to image-based gating.

The motion and deformation of catheters that lie inside cardiac structures can provide valuable information about the motion of the heart. In this paper we describe the formation of a novel statistical model of the motion of a coronary sinus (CS) catheter based on principal component analysis of tracked electrode locations from standard mono-plane x-ray fluoroscopy images. We demonstrate the application of our model for the purposes of retrospective cardiac and respiratory gating of x-ray fluoroscopy images in normal dose x-ray fluoroscopy images, and demonstrate how a modification of the technique allows application to very low dose scenarios. We validated our method on ten mono-plane imaging sequences comprising a total of 610 frames from ten different patients undergoing radiofrequency ablation for the treatment of atrial fibrillation. For normal dose images we established systole, end-inspiration and end-expiration gating with success rates of 100%, 92.1% and 86.9%, respectively. For very low dose applications, the method was tested on the same ten mono-plane x-ray fluoroscopy sequences without noise and 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. The method was able to detect the CS catheter even in the lowest SNR images with median errors not exceeding 2.6 mm per electrode. Furthermore, gating success rates of 100%, 71.4% and 85.7% were achieved at the low SNR value of √2, representing a dose reduction of more than 25 times. Thus, the technique has the potential to extract useful information whilst substantially reducing the radiation exposure.

Benchmarking framework for myocardial tracking and deformation algorithms: an open access database.

In this paper we present a benchmarking framework for the validation of cardiac motion analysis algorithms. The reported methods are the response to an open challenge that was issued to the medical imaging community through a MICCAI workshop. The database included magnetic resonance (MR) and 3D ultrasound (3DUS) datasets from a dynamic phantom and 15 healthy volunteers. Participants processed 3D tagged MR datasets (3DTAG), cine steady state free precession MR datasets (SSFP) and 3DUS datasets, amounting to 1158 image volumes. Ground-truth for motion tracking was based on 12 landmarks (4 walls at 3 ventricular levels). They were manually tracked by two observers in the 3DTAG data over the whole cardiac cycle, using an in-house application with 4D visualization capabilities. The median of the inter-observer variability was computed for the phantom dataset (0.77 mm) and for the volunteer datasets (0.84 mm). The ground-truth was registered to 3DUS coordinates using a point based similarity transform. Four institutions responded to the challenge by providing motion estimates for the data: Fraunhofer MEVIS (MEVIS), Bremen, Germany; Imperial College London - University College London (IUCL), UK; Universitat Pompeu Fabra (UPF), Barcelona, Spain; Inria-Asclepios project (INRIA), France. Details on the implementation and evaluation of the four methodologies are presented in this manuscript. The manually tracked landmarks were used to evaluate tracking accuracy of all methodologies. For 3DTAG, median values were computed over all time frames for the phantom dataset (MEVIS=1.20mm, IUCL=0.73 mm, UPF=1.10mm, INRIA=1.09 mm) and for the volunteer datasets (MEVIS=1.33 mm, IUCL=1.52 mm, UPF=1.09 mm, INRIA=1.32 mm). For 3DUS, median values were computed at end diastole and end systole for the phantom dataset (MEVIS=4.40 mm, UPF=3.48 mm, INRIA=4.78 mm) and for the volunteer datasets (MEVIS=3.51 mm, UPF=3.71 mm, INRIA=4.07 mm). For SSFP, median values were computed at end diastole and end systole for the phantom dataset(UPF=6.18 mm, INRIA=3.93 mm) and for the volunteer datasets (UPF=3.09 mm, INRIA=4.78 mm). Finally, strain curves were generated and qualitatively compared. Good agreement was found between the different modalities and methodologies, except for radial strain that showed a high variability in cases of lower image quality.

Understanding the mechanisms amenable to CRT response: from pre-operative multimodal image data to patient-specific computational models.

This manuscript describes our recent developments towards better understanding of the mechanisms amenable to cardiac resynchronization therapy response. We report the results from a full multimodal dataset corresponding to eight patients from the euHeart project. The datasets include echocardiography, MRI and electrophysiological studies. We investigate two aspects. The first one focuses on pre-operative multimodal image data. From 2D echocardiography and 3D tagged MRI images, we compute atlas based dyssynchrony indices. We complement these indices with presence and extent of scar tissue and correlate them with CRT response. The second one focuses on computational models. We use pre-operative imaging to generate a patient-specific computational model. We show results of a fully automatic personalized electromechanical simulation. By case-per-case discussion of the results, we highlight the potential and key issues of this multimodal pipeline for the understanding of the mechanisms of CRT response and a better patient selection.

Evaluation of a real-time hybrid three-dimensional echo and X-ray imaging system for guidance of cardiac catheterisation procedures.

Minimally invasive cardiac surgery is made possible by image guidance technology. X-ray fluoroscopy provides high contrast images of catheters and devices, whereas 3D ultrasound is better for visualising cardiac anatomy. We present a system in which the two modalities are combined, with a trans-esophageal echo volume registered to and overlaid on an X-ray projection image in real-time. We evaluate the accuracy of the system in terms of both temporal synchronisation errors and overlay registration errors. The temporal synchronisation error was found to be 10% of the typical cardiac cycle length. In 11 clinical data sets, we found an average alignment error of 2.9 mm. We conclude that the accuracy result is very encouraging and sufficient for guiding many types of cardiac interventions. The combined information is clinically useful for placing the echo image in a familiar coordinate system and for more easily identifying catheters in the echo volume.

Registering preprocedure volumetric images with intraprocedure 3-D ultrasound using an ultrasound imaging model.

For many image-guided interventions there exists a need to compute the registration between preprocedure image(s) and the physical space of the intervention. Real-time intraprocedure imaging such as ultrasound (US) can be used to image the region of interest directly and provide valuable anatomical information for computing this registration. Unfortunately, real-time US images often have poor signal-to-noise ratio and suffer from imaging artefacts. Therefore, registration using US images can be challenging and significant preprocessing is often required to make the registrations robust. In this paper we present a novel technique for computing the image-to-physical registration for minimally invasive cardiac interventions using 3-D US. Our technique uses knowledge of the physics of the US imaging process to reduce the amount of preprocessing required on the 3-D US images. To account for the fact that clinical US images normally undergo significant image processing before being exported from the US machine our optimization scheme allows the parameters of the US imaging model to vary. We validated our technique by computing rigid registrations for 12 cardiac US/magnetic resonance imaging (MRI) datasets acquired from six volunteers and two patients. The technique had mean registration errors of 2.1-4.4 mm, and 75% capture ranges of 5-30 mm. We also demonstrate how the same approach can be used for respiratory motion correction: on 15 datasets acquired from five volunteers the registration errors due to respiratory motion were reduced by 45%-92%.

Respiratory motion correction for image-guided cardiac interventions using 3-D echocardiography.

In this paper, we investigate the use of 3-D echocardiography (echo) data for respiratory motion correction of roadmaps in image-guided cardiac interventions. This is made possible by tracking and calibrating the echo probe and registering it to the roadmap coordinate system. We compare two techniques. The first uses only echo-echo registration to predict a motion-correction transformation in roadmap coordinates. The second combines echo-echo registration with a model of the respiratory motion of the heart. Using experiments with cardiac MRI and 3-D echo data acquired from eight volunteers, we demonstrate that the second technique is more robust than the first, resulting in motion-correction transformations that were accurate to within 5mm in 60% of cases, compared to 42% for the echo-only technique, based on subjective visual assessments. Objective validation showed that the model-based technique had an accuracy of 3.3 + or - 1.1mm, compared to 4.1 + or - 2.2mm for the echo only technique. The greater errors of the echo-only technique were mostly found away from the area of echo coverage. The model-based technique was more robust away from this area, and also has significant benefits in terms of computational cost.

A subject-specific technique for respiratory motion correction in image-guided cardiac catheterisation procedures.

We describe a system for respiratory motion correction of MRI-derived roadmaps for use in X-ray guided cardiac catheterisation procedures. The technique uses a subject-specific affine motion model that is quickly constructed from a short pre-procedure MRI scan. We test a dynamic MRI sequence that acquires a small number of high resolution slices, rather than a single low resolution volume. Additionally, we use prior knowledge of the nature of cardiac respiratory motion by constraining the model to use only the dominant modes of motion. During the procedure the motion of the diaphragm is tracked in X-ray fluoroscopy images, allowing the roadmap to be updated using the motion model. X-ray image acquisition is cardiac gated. Validation is performed on four volunteer datasets and three patient datasets. The accuracy of the model in 3D was within 5mm in 97.6% of volunteer validations. For the patients, 2D accuracy was improved from 5 to 13mm before applying the model to 2-4mm afterwards. For the dynamic MRI sequence comparison, the highest errors were found when using the low resolution volume sequence with an unconstrained model.

Anisotropic wave propagation and apparent conductivity estimation in a fast electrophysiological model: application to XMR interventional imaging.

Cardiac arrhythmias are increasingly being treated using ablation procedures. Development of fast electrophysiological models and estimation of parameters related to conduction pathologies can aid in the investigation of better treatment strategies during Radio-frequency ablations. We present a fast electrophysiological model incorporating anisotropy of the cardiac tissue. A global-local estimation procedure is also outlined to estimate a hidden parameter (apparent electrical conductivity) present in the model. The proposed model is tested on synthetic and real data derived using XMR imaging. We demonstrate a qualitative match between the estimated conductivity parameter and possible pathology locations. This approach opens up possibilities to directly integrate modelling in the intervention room.

Using combined x-ray and MR imaging for prostate I-125 post-implant dosimetry: phantom validation and preliminary patient work.

Post-implantation dosimetry is an important element of permanent prostate brachytherapy. This process relies on accurate localization of implanted seeds relative to the surrounding organs. Localization is commonly achieved using CT images, which provide suboptimal prostate delineation. On MR images, conversely, prostate visualization is excellent but seed localization is imprecise due to distortion and susceptibility artefacts. This paper presents a method based on fused MR and x-ray images acquired consecutively in a combined x-ray and MRI interventional suite. The method does not rely on any explicit registration step but on a combination of system calibration and tracking. A purpose-built phantom was imaged using MRI and x-rays, and the images were successfully registered. The same protocol was applied to three patients where combining soft tissue information from MRI with stereoscopic seed identification from x-ray imaging facilitated post-implant dosimetry. This technique has the potential to improve on dosimetry using either CT or MR alone.

Localization of abnormal conduction pathways for tachyarrhythmia treatment using tagged MRI.

Tachyarrhythmias are pathological fast heart rhythms often caused by abnormally conducting myocardial areas (foci). Treatment by radio-frequency (RF) ablation uses electrode-catheters to monitor and destroy foci. The procedure is normally guided with x-rays (2D), and thus prone to errors in location and excessive radiation exposure. Our main goal is to provide pre- and intra-operative 3D MR guidance in XMR systems by locating the abnormal conduction pathways. We address the inverse electro-mechanical relation by using motion in order to infer electrical propagation. For this purpose we define a probabilistic measure of the onset of regional myocardial activation, derived from 3D motion fields obtained by tracking tagged MR sequences with non-rigid registration. Activation isochrones are then derived to determine activation onset. We also compare regional motion between two different image acquisitions, thus assisting in diagnosing arrhythmia, in follow up of treatment, and in determining whether the ablation was successful. Difference maps of isochrones and other motion descriptors are computed to determine abnormal patterns. Validation was carried out using an electromechanical model of the heart, synthetic data, a cardiac MRI atlas of motion and geometry, MRI data from 6 healthy volunteers (one of them subjected to stress), and an MRI study on a patient with tachyarrhythmia, before and after RF ablation. A pre-operative MRI study on a second patient with tachyarrhythmia was used to test the methodology in a clinical scenario, predicting the abnormally conducting region.

A fast-marching approach to cardiac electrophysiology simulation for XMR interventional imaging.

Cardiac ablation procedures are becoming more routine to treat arrhythmias. The development of electrophysiological models will allow investigation of treatment strategies. However, current models are computationally expensive and often too complex to be adjusted with current clinical data. In this paper, we have proposed a fast algorithm to solve Eikonal-based models on triangular meshes. These models can be used to extract hidden parameters of the cardiac function from clinical data in a very short time, thus could be used during interventions. We propose a first approach to estimate these parameters, and have tested it on synthetic and real data derived using XMR imaging. We demonstrated a qualitative matching between the estimated parameter and XMR data. This novel approach opens up possibilities to directly integrate modelling in the interventional room.