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Practical limitations on the duration of individual fMRI scans have led neuroscientist to consider the aggregation of data from multiple subjects. Differences in anatomical structures and functional topographies of brains require aligning data across subjects. Existing functional alignment methods serve as a preprocessing step that allows subsequent statistical methods to learn from the aggregated multi-subject data. Despite their success, current alignment methods do not leverage the labeled data used in the subsequent methods.

In medical imaging, the acquired images are usually analyzed by a human observer and rated with respect to a diagnostic question. However, this procedure is time-demanding and expensive. Furthermore, the lack of a reference image makes this task challenging. In order to support the human observer in assessing image quality and to ensure an objective evaluation, we extend in this paper our previous no-reference magnetic resonance (MR) image quality assessment system with an active learning loop to reduce the amount of necessary labeled training data.

Given a set of training shapes and an input image with a shape similar to some of the elements in the training set, this poster introduces a new implicit kernel sparse model with a twofold goal. First, to obtain an implicit kernel sparse neighbor based combination that best represents the object. Second, to accurately segment the object taking into accounts both the high-level implicit kernel presentation and the low-level image information.

A signal based algorithm resulting in increased depth resolution is presented for medical ultrasound. It relies on multiple foci beamforming that is enabled by current ultrasound imaging systems. The concept stems from optical microscopy and is translated here into ultrasound using the Field II simulation software. A 7 MHz linear transducer is used to scan a single point scatterer phantom that can move in the axial direction.