The Challenge on Liver Ultrasound Tracking (CLUST) was held in conjunction with the MICCAI 2014 conference to enable direct comparison of tracking methods for this application. This paper reports the outcome of this challenge, including setup, methods, results and experiences. The database included 54 2D and 3D sequences of the liver of healthy volunteers and tumor patients under free breathing. Participants had to provide the tracking results of 90% of the data (test set) for pre-defined point-landmarks (healthy volunteers) or for tumor segmentations (patient data). In this paper we compare the best six methods which participated in the challenge. Quantitative evaluation was performed by the organizers with respect to manual annotations. Results of all methods showed a mean tracking error ranging between 1.4 mm and 2.1 mm for 2D points, and between 2.6 mm and 4.6 mm for 3D points. Fusing all automatic results by considering the median tracking results, improved the mean error to 1.2 mm (2D) and 2.5 mm (3D). For all methods, the performance is still not comparable to human inter-rater variability, with a mean tracking error of 0.5–0.6 mm (2D) and 1.2–1.8 mm (3D). The segmentation task was fulfilled only by one participant, resulting in a Dice coefficient ranging from 76.7% to 92.3%. The CLUST database continues to be available and the online leader-board will be updated as an ongoing challenge.

The EEG acquired simultaneously with fMRI is distorted by a number of artefacts related to the presence of strong magnetic fields, which must be reduced in order to allow for a useful interpretation and quantification of the EEG data. For the two most prominent artefacts, associated with magnetic field gradient switching and the heart beat, reduction methods have been developed and applied successfully. However, a number of artefacts related to the MR-environment can be found to distort the EEG data acquired even without ongoing fMRI acquisition. In this paper, we investigate the most prominent of those artefacts, caused by the Helium cooling pump, and propose a method for its reduction and respective validation in data collected from epilepsy patients. Since the Helium cooling pump artefact was found to be repetitive, an average template subtraction method was developed for its reduction with appropriate adjustments for minimizing the degradation of the physiological part of the signal. The new methodology was validated in a group of 15 EEG-fMRI datasets collected from six consecutive epilepsy patients, where it successfully reduced the amplitude of the artefact spectral peaks by 95 ± 2 % while the background spectral amplitude within those peaks was reduced by only -5 ± 4 %. Although the Helium cooling pump should ideally be switched off during simultaneous EEG-fMRI acquisitions, we have shown here that in cases where this is not possible the associated artefact can be effectively reduced in post processing.

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