Artificial neural networks are promissing for separating signals from interference. However, their success depends on the amount of training data and network complexity which is prohibitively high. Pruned neural networks (PNN) are explored in this paper for the specific problem of separating artifacts from EEG signals. It is shown that PNN provides better performance than the fully interconnect...

Background: To assess the impact of metal artifact reduction techniques in 3D printing by evaluating image quality and segmentation time in both phantom and patient studies with dental restorations and/or other metal implants. An acrylic denture apparatus (Kilgore Typodent, Kilgore International, Coldwater, MI) was set in a 20 cm water phantom and scanned on a single-source CT scanner with gant...

Esther Meyer1,2, Rainer Raupach2, Michael Lell3, Bernhard Schmidt2, and Marc Kachelrieß1,4 1Institute of Medical Physics, Friedrich–Alexander University of Erlangen–Nürnberg (FAU), Erlangen, Germany  2Siemens Healthcare, Forchheim, Germany 3Institute of Diagnostic Radiology, Friedrich–Alexander University of Erlangen–Nürnberg (FAU), Erlangen, Germany 4Medical Physics in Radiology, German Cance...

In this Project, we propose an algorithm for block artifact reduction for which Pseudo random noise masking is used. In H.264 Video Compression Standard, a Deblocking filter is implemented to reduce the quantization noise around the block boundaries. But there would still remain artifacts within blocks. These artifacts are known as Contour artifacts. These Contour artifacts further be reduced b...

Transform coding using the discrete cosine transform is one of the most popular techniques for image and video compression. However, at low bit rates, the coded images suffer from severe visual distortions. An innovative approach is proposed that deals with artifacts in JPEG compressed images. Our algorithm addresses all three types of artifacts which are prevalent in JPEG images: blocking, and...

In computed tomography (CT), metal implants increase the inconsistencies between the measured data and the linear assumption made by the analytical CT reconstruction algorithm. The inconsistencies appear in the form of dark and bright bands and streaks in the reconstructed image, collectively called metal artifacts. The standard method for metal artifact reduction (MAR) replaces the inconsisten...

In the analysis of MEG data one often faces the problem that noise from biological or technical origins (e.g. alpha activity or interference from the power line, respectively) is corrupting the measurements. We present a case study where we analyze the effects of artifact removal for a well-known experimental setting: measurements of somatosensory evoked fields (SEF, N20). We compare a classica...

This paper presents a method to reduce artifacts from scalp EEG recordings to facilitate seizure diagnosis/detection for epilepsy patients. The proposed method is primarily based on stationary wavelet transform and takes the spectral band of seizure activities (i.e. 0.5 29 Hz) into account to separate artifacts from seizures. Different artifact templates have been simulated to mimic the most co...

Abstract. We describe an iterative algorithm for reducing EPI ghost artifacts using image-based phase corrections. Ghosts outside the parent object are set to the background level and a model k-space is obtained from the Fourier transform of the resulting image. The phase offsets in each k-space line are estimated by comparison with the corresponding line in the model k-space. We hypothesise th...

UNLABELLED PET studies of cerebral neuroreceptors are often recorded over periods ranging from 1 to 2 h, and head movements during the studies not only lead to blurred images but also may seriously disturb the kinetic analysis. We report the effect of motion on parametric images of the distribution volume ratio (DVR), as well as possible improvements if the dynamic PET data are corrected for he...