Noninvasive reconstruction of cardiac electrical activity: update on current methods, applications and challenges.

Electrical activity at the level of the heart muscle can be noninvasively reconstructed from body-surface electrocardiograms (ECGs) and patient-specific torso-heart geometry. This modality, coined electrocardiographic imaging, could fill the gap between the noninvasive (low-resolution) 12-lead ECG and invasive (high-resolution) electrophysiology studies. Much progress has been made to establish electrocardiographic imaging, and clinical studies appear with increasing frequency. However, many assumptions and model choices are involved in its execution, and only limited validation has been performed. In this article, we will discuss the technical details, clinical applications and current limitations of commonly used methods in electrocardiographic imaging. It is important for clinicians to realise the influence of certain assumptions and model choices for correct and careful interpretation of the results. This, in combination with more extensive validation, will allow for exploitation of the full potential of noninvasive electrocardiographic imaging as a powerful clinical tool to expedite diagnosis, guide therapy and improve risk stratification.

A wavelet approach for unsupervised nystagmus analysis on ENG and VOG recordings.

Several algorithms are available to quantify nystagmus beats in electro nystagmography (ENG) and videooculography (VOG) recordings. These algorithms use parameterized approaches to detect the fast components of nystagmus beats. This paper proposes a wavelet approach to detect fast components of nystagmus beats. The main advantage of this approach compared to alternatives, is the completely unsupervised automated routine. The algorithm is implemented and validated in different clinical experiments. The results are compared to that of an alternative parameterized technique. Results show that the wavelet approach is suitable for automated nystagmus analysis.

Measures for the evaluation of bias field removal procedures in MRI and the design of wavelets for suppression of RF inhomogeneities.

RF inhomogeneities are illumination artifacts in MR images which manifest as a multiplicative bias field. To measure the quality of an MR image with respect to RF inhomogeneities, existing multi-valued criteria are in use. Here we propose a useful conversion of these multi-valued criteria into a single measure of quality which simplifies image quality evaluation and comparison. Next, to remove such a bias field, a novel wavelet based approach is employed, that extends a previous 1D wavelet design methodology to a 2D setting. This method is found to perform well on images with strong small details. The results for brain MR images are subject to improvement, however our results hint to a future scenario for improved image quality.