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Traumatic brain injury (TBI) represents an enormous public health challenge and is often associated with life long neurobehavioral sequelae in survivors. Several factors including higher percentages of individuals surviving TBI, as well as increasing concern about potential long term sequelae of even relatively mild injuries is changingthe role of neuroimaging in the management of this condition. Historically the role has been the detection and acute management of life-threatening complications requiring surgical intervention. Howeverthere is an emerging need for neuroimaging biomarkers that would facilitate detection of milder injuries, allow recovery trajectory monitoring, and identify those at risk for poor functional outcome and disability. This paper reviews the current status of different neuroimaging techniques in TBI and outlines some of the challenges involved in moving towards an expanded role in these domains.

In this paper, we present a novel approach to rigidly register intraoperative electromagnetically tracked ultrasound (US) with preoperative magnetic resonance (MR) images. The clinical rationale for thiswork is to allow accurate needle placement during thermal ablation of liver metastases using multimodal imaging. We adopt a model-basedapproach that rigidly matches segmented liver surface shapes obtained from the multimodal image volumes. Towards this end, a shape-constrained deformable surface model combining the strengths of both deformable and active shape models is used to segment the liver surfacefrom the MR scan. It incorporates a priori shape information while external forces guide the deformation and adapts the model to a target structure. The liver boundary is extracted from US by merging a dynamic region-growing method with a graph-based segmentation framework anchored on adaptive priors of neighboring surface points. Registration is performed with a weighted ICP algorithm with a physiological penalizing term. The MR segmentation model was trained with 30 datasets and validated on a separate cohort of 10 patients with corresponding ground truth. The accuracy and robustness of the method wereassessed by registering four US/MR datasets, yielding accurate landmark registration errors (3.7 +/- 0.69mm) and high robustness, and isthus acceptable for radiofrequency clinical applications.