Driving event-related potential-based speller by localized posterior activities: An offline study.

Multi-sensor recordings are normally used in event-related potential (ERP)-based brain computer interfaces (BCIs), for capturing brain activities widely distributed over the cortical surface. However, this may lead to an increased number of sensors for boosting classification performance, as well as a complicated computational effort for optimizing/reducing sensors, limiting the popularization of mobile/wearable BCIs for the end use. The localization of brain activities may help fix this issue by making useful information concentrated on relatively local brain areas, thus greatly reducing the number of sensors required and computational burden arising from the sensor selection.

Nonrigid medical image registration based on mesh deformation constraints.

Regularizing the deformation field is an important aspect in nonrigid medical image registration. By covering the template image with a triangular mesh, this paper proposes a new regularization constraint in terms of connections between mesh vertices. The connection relationship is preserved by the spring analogy method.

Detection and characterization of hepatocellular carcinoma in rats with liver cirrhosis: diagnostic value of combined use of MR positive and negative contrast agents.

Background: Gadolinium-enhanced multi-phase dynamic imaging has improved the accuracy of the diagnosis of hypervascular hepatocellular carcinoma (HCC), but using gadolinium-enhanced dynamic imaging alone is problematic in evaluating hypovascular HCC. This work aimed at evaluating the combined use of superparamagnetic iron oxide (SPIO)-enhanced and gadolinium set in distinguishing HCCs from regenerative nodules (RNs) in a rat model induced by diethylnitrosamine (DEN).

Methods: DEN-induced HCC model rats (n=40) and control rats (n=10) were studied.

Non-rigid registration based segmentation of brain subcortical structures using a priori knowledge.

Segmentation of the brain internal structures is an important and a challenging task due to their complex shapes, partial volume effects, low contrasts and anatomical variability between subjects. In this paper we propose a new non-rigid registration method that automatically segments the deep brain internal structures from brain MRI images. An atlas of the structures is used as a priori knowledge, which is modeled as a shape representation.