We present an approach for fast reconstructing of cardiac myocardium and blood masses of a patient's heart from morphological image data, acquired either MRI or CT, in order to estimate numerically the spread of electrical excitation in the patient's atria and ventricles. The approach can be divided into two main steps. During the first step the ventricular and atrial blood masses are extracted employing Active Appearance Models (AAM). The left and right ventricular blood masses are segmented automatically after providing the positions of the apex cordis and the base of the heart. Because of the complex geometry of the atria the segmentation process of the atrial blood masses requires more information as the ventricular blood mass segmentation process of the ventricles. We divided, for this reason, the left and right atrium into three divisions of
appearance. This proved sufficient for the 2D AAM model to extract
the target blood masses. The base of the heart, the left upper and
left lower pulmonary vein from its first up to its last appearance
in the image stack, and the right upper and lower pulmonary vein
have to be marked. After separating the volume data into these
divisions the 2D AAM search procedure extracts the blood masses
which are the main input for the second and last step in the
myocardium extraction pipeline. This step uses
morphologically-based operations in order to extract the
ventricular and atrial myocardium either directly by detecting the
myocardium in the volume block or by reconstructing the myocardium
using mean model information, in case the algorithm fails to
detect the myocardium.
For a clinical application of the inverse problem of
electrocardiography, a flexible and fast generation of a patient's
volume conductor model is essential. The volume conductor model
includes compartments like chest, lungs, ventricles, atria and the
associated blood masses. It is a challenging task to create an
automatic or semi-automatic segmentation procedure for each
compartment. For the extraction of the lungs, as one example, a
region growing algorithm can be used, to extract the blood masses
of the ventricles Active Appearance Models may succeed, and to
construct the atrial myocardium a multiplicity of operations are
necessary. These examples illustrate that there is no common
method that will succeed for all compartments like a least common
denominator. Another problem is the automatization of combining
different methods and the origination of a segmentation pipeline
in order to extract a compartment and, accordingly, the desired
model - in our case the complete volume conductor model for
estimating the spread of electrical excitation in the patient's
heart. On account of this, we developed a C++ framework and a
special application with the goal of creating tissue-specific
segmentation pipelines. The C++ framework uses different standard
frameworks like DCMTK for handling medical images
(http://dicom.offis.de/dcmtk.php.en), ITK (http://www.itk.org/)
for some segmentation methods, and Qt (http://www.trolltech.com/) for creating user interfaces. Our Medical Segmentation Toolkit (MST) enables to combine different segmentation techniques for each compartment. In addition, the framework enables to create user-defined compartment pipelines.
We present two approaches for reconstructing a patient’s atrial myocardium from morphological image data.
Both approaches are based on a segmentation of the left and right atrial blood masses which mark the inner
border of the atrial myocardium. The outer border of the atrial myocardium is reconstructed differently by the
two approaches. The surface manipulation approach is based on a triangle manipulation procedure while the
label-voxel-field approach adds or deletes label-voxels of the segmented blood mass labelset. Both approaches
yield models of a patient’s atrial myocardium that qualify for further applications. The obtained atrial models
have been implemented many times in the construction of a patient’s volume conductor model needed for solving
the electrocardiographic inverse problem. The label-voxel-field approach has to be favored because of its superior
performance and ability of implementation in a segmentation pipeline.
Noninvasive imaging of electrical function in the human atria is attained by the combination of data from electrocardiographic (ECG) mapping and magnetic resonance imaging (MRI). An anatomical computer model of the individual patient is the basis for our computer-aided diagnosis of cardiac arrhythmias. Three patients suffering from Wolff-Parkinson-White syndrome, from paroxymal atrial fibrillation, and from atrial flutter underwent an electrophysiological study. After successful treatment of the cardiac arrhythmia with invasive catheter technique, pacing protocols with stimuli at several anatomical sites (coronary sinus, left and right pulmonary vein, posterior site of the right atrium, right atrial appendage) were performed. Reconstructed activation time (AT) maps were validated with catheter-based electroanatomical data, with invasively determined pacing sites, and with pacing at anatomical markers. The individual complex anatomical model of the atria of each patient in combination with a high-quality mesh optimization enables accurate AT imaging, resulting in a localization error for the estimated pacing sites within 1 cm. Our findings may have implications for imaging of atrial activity in patients with focal arrhythmias.
Inverse electrocardiography has been developing for several years. By coupling electrocardiographic mapping and 3D+time anatomical data, the electrical excitation sequence can be imaged completely noninvasively in the human heart. In this study, a bidomain theory based surface heart model activation time imaging approach was applied to single beat data of atrial and ventricular depolarization. For sinus and paced rhythms, the sites of early activation and the areas with late activation were estimated with sufficient accuracy. In particular for focal arrhythmias, this model-based imaging approach might allow the guidance and evaluation of antiarrhythmic interventions, for instance, in case of catheter ablation or drug therapy.