Biomedical subjects

C Hintermüller

Publications and source records attributed to C Hintermüller.

4 recordsLinked to original sources

PubMed2007

AAM-based segmentation for imaging cardiac electrophysiology.

OBJECTIVES: Activation time (AT) imaging from electrocardiographic (ECG) mapping data has been developing for several years. By coupling 4-dimensional volume data (3D + time) the electrical sequence can be computed non-invasively. In this paper an approach for extracting the ventricular and atrial blood masses for structurally normal hearts by using cine-gated short-axis data obtained via magnetic resonance imaging (MRI) is introduced. METHODS: The blood masses are extracted by employing Active Appearance Models (AAMs). The ventricular blood masses are segmented, applying the AAMs after providing apex cordis and base of the heart in the volume data, whereas the more complex geometry of the atria requires a more specific attempt. On account of this the atrium was divided into three divisions of appearance, where the images of the volume data in the related divisions have a maximum affinity. The first division reaches from the base of the heart to initial visibility of the upper and left lower pulmonary vein. The second division up from there to the last occurrence and the third division from there to the end of the visibility of the right upper and lower pulmonary vein. After extracting the cardiac blood masses the result gets triangulated and remeshed for activation time imaging. RESULTS: With this method the cardiac models of eight patients were extracted and the AT imaging approach was applied to single-beat ECG data of atrial and ventricular depolarization. CONCLUSION: The advantage of the proposed AAM approach is that only a few initial parameters have to be set. Therefore, the approach can be integrated into a processing pipeline that works semi-automatically. The extracted models can be used for further investigations.

Atrial Function↗

PubMed2006

Atrial and ventricular myocardium extraction using model-based techniques.

OBJECTIVES: This paper presents an efficient approach for extracting myocardial structures from given atrial and ventricular blood masses to enable non-invasive estimation of electrical excitation in human atria and ventricles. METHODS: Based on given segmented atrial and ventricular blood masses, the approach constructs the myocardial structure directly, in the case that the myocardium can be detected in the volume data, or by using mean model information, in the case that the myocardium cannot be seen in the volume data due to image modalities or artefacts. The approach employs mathematical and gray-value morphology operations. Regulated by the spatial visibility of the myocardial structure in the medical image data especially the atrial myocardium needs to be estimated repeatedly using the a-priori knowledge given by the anatomy. RESULTS: The approach was tested using eight patient data sets. The reconstruction process yielded satisfying results with respect to an efficient generation of a volume conductor model which is essential when trying to implement the estimation of electrical excitation in clinical application. CONCLUSION: The approach yields ventricular and atrial models that qualify for cardiac source imaging in a clinical setting.

Algorithms↗

PubMed2005

A signal processing pipeline for noninvasive imaging of ventricular preexcitation.

OBJECTIVES: Noninvasive imaging of the cardiac activation sequence in humans could guide interventional curative treatment of cardiac arrhythmias by catheter ablation. Highly automated signal processing tools are desirable for clinical acceptance. The developed signal processing pipeline reduces user interactions to a minimum, which eases the operation by the staff in the catheter laboratory and increases the reproducibility of the results. METHODS: A previously described R-peak detector was modified for automatic detection of all possible targets (beats) using the information of all leads in the ECG map. A direct method was applied for signal classification. The algorithm was tuned for distinguishing beats with an adenosine induced AV-nodal block from baseline morphology in Wolff-Parkinson-White (WPW) patients. Furthermore, an automatic identification of the QRS-interval borders was implemented. RESULTS: The software was tested with data from eight patients having overt ventricular preexcitation. The R-peak detector captured all QRS-complexes with no false positive detection. The automatic classification was verified by demonstrating adenosine-induced prolongation of ventricular activation with statistical significance (p <0.001) in all patients. This also demonstrates the performance of the automatic detection of QRS-interval borders. Furthermore, all ectopic or paced beats were automatically separated from sinus rhythm. Computed activation maps are shown for one patient localizing the accessory pathway with an accuracy of 1 cm. CONCLUSIONS: The implemented signal processing pipeline is a powerful tool for selecting target beats for noninvasive activation imaging in WPW patients. It robustly identifies and classifies beats. The small beat to beat variations in the automatic QRS-interval detection indicate accurate identification of the time window of interest.

Action Potentials↗

PubMed2005

Computationally efficient noninvasive cardiac activation time imaging.

OBJECTIVE: The computer model-based computation of the cardiac activation sequence in humans has been recently subject of successful clinical validation. This method is of potential interest for guiding ablation therapy of arrhythmogenic substrates. However, computation times of almost an hour are unattractive in a clinical setting. Thus, the objective is the development of a method which performs the computation in a few minutes run time. METHODS: The computationally most expensive part is the product of the lead field matrix with a matrix containing the source pattern on the cardiac surface. The particular biophysical properties of both matrices are used for speeding up this operation by more than an order of magnitude. A conjugate gradient optimizer was developed using C++ for computing the activation map. RESULTS: The software was tested on synthetic and clinical data. The increase in speed with respect to the previously used Fortran 77 implementation was a factor of 30 at a comparable quality of the results. As an additional finding the coupled regularization strategy, originally introduced for saving computation time, also reduced the sensitivity of the method to the choice of the regularization parameter. CONCLUSIONS: As it was shown for data from a WPWpatient the developed software can deliver diagnostically valuable information at a much shorter span of time than current clinical routine methods. Its main application could be the localization of focal arrhythmogenic substrates.

Arrhythmias, Cardiac↗