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We present an adaptive algorithm which uses a fast electrophysiological (EP) model to estimate apparent electrical conductivity and local conduction velocity from noncontact mapping of the endocardial surface potential. Development of such functional imaging revealing hidden parameters of the heart can be instrumental for improved diagnosis and planning of therapy for cardiac arrhythmia and heart failure, for example during procedures such as radio-frequency ablation and cardiac resynchronisation therapy. The proposed model is validated on synthetic data and applied to clinical data derived using hybrid X-ray/magnetic resonance imaging. We demonstrate a qualitative match between the estimated conductivity parameter and pathology locations in the human left ventricle. We also present a proof of concept for an electrophysiological model which utilizes the estimated apparent conductivity parameter to simulate the effect of pacing different ventricular sites. This approach opens up possibilities to directly integrate modelling in the cardiac EP laboratory. Index Terms--Cardiac conductivity imaging, conduction velocity, eikonal models, electrophysiology, parameter estimation.

The hybrid magnetic resonance (MR)/X-ray suite (XMR) is a recently introduced imaging solution that provides new possibilities for guidance of cardiovascular catheterization procedures. We have previously described and validated a technique based on optical tracking to register MR and X-ray images obtained from the sliding table XMR configuration. The aim of our recent work was to extend our technique by providing an improved calibration stage, real-time guidance during cardiovascular catheterization procedures, and further off-line analysis for mapping cardiac electrical data to patient anatomy. Specially designed optical trackers and a dedicated calibration object have resulted in a single calibration step that can be efficiently checked and updated before each procedure. All X-ray distortion model has been implemented that allows for distortion correction for arbitrary c-arm orientations. During procedures, the guidance system provides a real-time combined MR/X-ray image display consisting of live X-ray images with registered recently acquired MR derived anatomy. It is also possible to reconstruct the location of catheters seen during X-ray imaging in the MR derived patient anatomy. We have applied our registration technique to 13 cardiovascular catheterization procedures. Our system has been used for the real-time guidance of ten radiofrequency ablations and one aortic stent implantation. We demonstrate the real-time guidance using two exemplar cases. In a further two cases we show how off-line analysis of registered image data, acquired during electrophysiology study procedures, has been used to map cardiac electrical measurements to patient anatomy for two different types of mapping catheters. The cardiologists that have used the guidance system suggest that real-time XMR guidance could have substantial value in difficult interventional and electrophysiological procedures, potentially reducing procedure time and delivered radiation dose. Also, the ability to map measured electrical data to patient specific anatomy provides improved visualization and a path to investigation of cardiac electromechanical models. Index Terms--Cardiovascular intervention, interventional navigation, XMR systems, 2D-3D registration.

We have developed two new algorithms for the measurement of blood flow from dynamic X-ray angiographic images. Both algorithms aim to improve on existing techniques. First, a model-based (MB) algorithm is used to constrain the concentration-distance curve matching approach. Second, a weighted optical flow algorithm (OP) is used to improve on point-based optical flow methods by averaging velocity estimates along a vessel with weighting based on the magnitude of the spatial derivative. The OP algorithm was validated using a computer simulation of pulsatile blood flow. Both the OP and the MB algorithms were validated using a physiological blood flow circuit. Dynamic biplane digital X-ray images were acquired following injection of iodine contrast medium into a variety of simulated arterial vessels. The image data were analyzed using our integrated angiographic analysis software SARA to give blood flow waveforms using the MB and OP algorithms. These waveforms were compared to flow measured using an electromagnetic flow meter (EMF). In total 4935 instantaneous measurements of flow were made and compared to the EMF recordings. It was found that the new algorithms showed low measurement bias and narrow limits of agreement and also outperformed the concentration-distance curve matching algorithm (ORG) and a modification of this algorithm (PA) in all studies. Index Terms--Blood flow measurement, X-ray angiography, X-ray measurements.

We propose a new method for aligning three-dimensional (3-D) magnetic resonance angiography (MRA) with 2-D X-ray digital subtraction angiograms (DSA). Our method is developed from our algorithm to register computed tomography volumes to X-ray images based on intensity matching of digitally reconstructed radiographs (DRRs). To make the DSA and DRR more similar, we transform the MRA images to images of the vasculature and set to zero the contralateral side of the MRA to that imaged with DSA. We initialize the search for a match on a user defined circular region of interest. We have tested six similarity measures using both unsegmented MRA and three segmentation variants of the MRA. Registrations were carried out on images of a physical neuro-vascular phantom and images obtained during four neuro-vascular interventions. The most accurate and robust registrations were obtained using the pattern intensity, gradient difference, and gradient correlation similarity measures, when used in conjunction with the most sophisticated MRA segmentations. Using these measures, 95 % of the phantom start positions and 82 % of the clinical start positions were successfully registered. The lowest root mean square reprojection errors were 1.3 mm (standard deviation 0.6) for the phantom and 1.5 mm (standard deviation 0.9) for the clinical data sets. Finally, we present a novel method for the comparison of similarity measure performance using a technique borrowed from receiver operator characteristic analysis. Index Terms--2-D--3-D registration, digital subtraction angiography, magnetic resonance angiography, neuro-interventions, similarity measures.

We describe a registration and tracking technique to integrate cardiac X-ray images and cardiac magnetic resonance (MR) images acquired from a combined X-ray and MR interventional suite (XMR). Optical tracking is used to determine the transformation matrices relating MR image coordinates and X-ray image coordinates. Calibration of X-ray projection geometry and tracking of the X-ray C-arm and table enable three-dimensional (3-D) reconstruction of vessel centerlines and catheters from bi-plane X-ray views. We can, therefore, combine single X-ray projection images with registered projection MR images from a volume acquisition, and we can also display 3-D reconstructions of catheters within a 3-D or multi-slice MR volume. Registration errors were assessed using phantom experiments. Errors in the combined projection images (two-dimensional target registration error--TRE) were found to be 2.4 to 4.2 mm, and the errors in the integrated volume representation (3-D TRE) were found to be 4.6 to 5.1 mm. These errors are clinically acceptable for alignment of images of the great vessels and the chambers of the heart. Results are shown for two patients. The first involves overlay of a catheter used for invasive pressure measurements on an MR volume that provides anatomical context. The second involves overlay of invasive electrode catheters (including a basket catheter) on a tagged MR volume in order to relate electrophysiology to myocardial motion in a patient with an arrhythmia. Visual assessment of these results suggests the errors were of a similar magnitude to those obtained in the phantom measurements. Index Terms--2-D-3-D registration, cardiovascular intervention, optical tracking, XMR systems.

Multiscale cardiac modeling has made great advances over the last decade. Highly detailed atrial models were created and used for the investigation of initiation and perpetuation of atrial fibrillation. The next challenge is the use of personalized atrial models in clinical practice. In this study, a framework of simple and robust tools is presented, which enables the generation and validation of patient-specific anatomical and electrophysiological atrial models. Introduction of rule-based atrial fiber orientation produced a realistic excitation sequence and a better correlation to the measured electrocardiograms. Personalization of the global conduction velocity lead to a precise match of the measured P-wave duration. The use of a virtual cohort of nine patient and volunteer models averaged out possible model-specific errors. Intra-atrial excitation conduction was personalized manually from left atrial local activation time maps. Inclusion of LE-MRI data into the simulations revealed possible gaps in ablation lesions. A fast marching level set approach to compute atrial depolarization was extended to incorporate anisotropy and conduction velocity heterogeneities and reproduced the monodomain solution. The presented chain of tools is an important step towards the use of atrial models for the patient-specific AF diagnosis and ablation therapy planing. [ABSTRACT FROM PUBLISHER]

In this paper, we present a novel technique based on nonrigid image registration for myocardial motion estimation using both untagged and 3-D tagged MR images. The novel aspect of our technique is its simultaneous usage of complementary information from both untagged and 3-D tagged MR images. To estimate the motion within the myocardium, we register a sequence of tagged and untagged MR images during the cardiac cycle to a set of reference tagged and untagged MR images at end-diastole. The similarity measure is spatially weighted to maximize the utility of information from both images. In addition, the proposed approach integrates a valve plane tracker and adaptive incompressibility into the framework. We have evaluated the proposed approach on 12 subjects. Our results show a clear improvement in terms of accuracy compared to approaches that use either 3-D tagged or untagged MR image information alone. The relative error compared to manually tracked landmarks is less than 15% throughout the cardiac cycle. Finally, we demonstrate the automatic analysis of cardiac function from the myocardial deformation fields. [ABSTRACT FROM AUTHOR]

Magnetic resonance (MR) imaging has become a routine modality for the determination of patient cardiac morphology. The extraction of this information can be important for the development of new clinical applications as well as the planning and guidance of cardiac interventional procedures. To avoid inter- and intra-observer variability of manual delineation, it is highly desirable to develop an automatic technique for whole heart segmentation of cardiac magnetic resonance images. However, automating this process is complicated by the limited quality of acquired images and large shape variation of the heart between subjects. In this paper, we propose a fully automatic whole heart segmentation framework based on two new image registration algorithms: the locally affine registration method (LARM) and the free-form deformations with adaptive control point status (ACPS FFDs). LARM provides the correspondence of anatomical substructures such as the four chambers and great vessels of the heart, while the registration using ACPS FFDs refines the local details using a constrained optimization scheme. We validated our proposed segmentation framework on 37 cardiac MR volumes on the end-diastolic phase, displaying a wide diversity of morphology and pathology, and achieved a mean accuracy of 2.14 ± 0.63 mm (rms surface distance) and a maximal error of 4.31 mm. [ABSTRACT FROM AUTHOR]

This paper describes a predictive and adaptive single parameter motion model for updating roadmaps to correct for respiratory motion in image-guided interventions. The model can adapt its motion estimates to respond to changes in breathing pat- tern, such as deep or fast breathing, which normally would result in a decrease in the accuracy of the motion estimates. The adaptation is made possible by interpolating between the motion estimates of multiple submodels, each of which describes the motion of the target organ during cycles of different amplitudes. We describe a predictive technique which can predict the amplitude of a breathing cycle before it has finished. The predicted amplitude is used to interpolate between the motion estimates of the submodels to tune the adaptive model to the current breathing pattern. The proposed technique is validated on affine motion models formed from cardiac magnetic resonance imaging (MRI) datasets acquired from seven volunteers and one patient. The amplitude prediction technique showed errors of 1.9-6.5 mm. The combined predictive and adaptive technique showed 3-D motion prediction errors of 1.0-2.8 mm, which represents an improvement in modelling performance of up to 40% over a standard nonadaptive single parameter motion model. We also applied the combined technique in a clinical setting to test the feasibility of using it for respiratory motion correction of roadmaps in image-guided cardiac catheterisations. In this clinical case we show that 2-D registration errors due to respiratory motion are reduced from 7.7 to 2.8 mm using the proposed technique. [ABSTRACT FROM AUTHOR]

Magnetic resonance (MR) imaging has become a routine modality for the determination of patient cardiac morphology. The extraction of this information can be important for the development of new clinical applications as well as the planning and guidance of cardiac interventional procedures. To avoid inter- and intra-observer variability of manual delineation, it is highly desirable to develop an automatic technique for whole heart segmentation of cardiac magnetic resonance images. However, automating this process is complicated by the limited quality of acquired images and large shape variation of the heart between subjects. In this paper, we propose a fully automatic whole heart segmentation framework based on two new image registration algorithms: the locally affine registration method (LARM) and the free-form deformations with adaptive control point status (ACPS FFDs). LARM provides the correspondence of anatomical substructures such as the four chambers and great vessels of the heart, while the registration using ACPS FFDs refines the local details using a constrained optimization scheme. We validated our proposed segmentation framework on 37 cardiac MR volumes on the end-diastolic phase, displaying a wide diversity of morphology and pathology, and achieved a mean accuracy of 2.14 +/- 0.63 mm (rms surface distance) and a maximal error of 4.31 mm.