Faculty Bibliography

Fast tracking of motion is the key step towards tagged MRI-based quantitative cardiac analysis. Existing motion tracking approaches, including the widely used HARP method, are either time consuming or qualitatively inconsistent, or both. We present in this paper a new fast motion tracking method based on a meshless kernel. For MR image sequences containing multiple image frames, tag intersections are automatically detected in all frames and indexed in the first frame. Then a thin plate spline approach is used to establish a point-to-point correspondence between tag intersections in the initial and the current frame. Lastly, we use a meshless registration kernel to generate a dense displacement map that minimizes the residual of sparse motion at intersections. To further improve the motion tracking, we develop a special technique to preserve tangential angles of tags at tag intersections. We tested our new method on both numerical phantoms and in vivo heart data. The motion tracking results are evaluated against the ground truth and manually drawn tags. Clinical application potential is demonstrated by cardiac strain analysis based on the proposed methodology

We propose a novel Active Volume Model (AVM) which deforms in a free-form manner to minimize energy. Unlike Snakes and level-set active contours which only consider curves or surfaces, the AVM is a deforming object model that has both boundary and an interior area. When applied to object segmentation and tracking, the model alternates between two basic operations: deform according to current object prediction, and predict according to current appearance statistics of the model. The probabilistic object prediction module relies on the Bayesian Decision Rule to separate foreground (i.e., object represented by the model) and background. Optimization of the model is a natural extension of the Snakes model so that region information becomes part of the external forces. The AVM thus has the efficiency of Snakes while having adaptive region-based constraints. Segmentation results, validation, and comparison with GVF Snakes and level set methods are presented for experiments on noisy 2D/3D medical images

Functional neuroimaging studies of depressed patients have converged with functional brain mapping studies of depressed animals in showing that depression is accompanied by a hypoactivity of brain regions involved in positively motivated behavior together with a hyperactivity in regions involved in stress responses. Both sets of changes are reversed by diverse antidepressant treatments. It has been proposed that this neural pattern underlies the symptoms common to most forms of the depression, which are the loss of positively motivated behavior and increased stress. The paper discusses how this framework can organize diverse findings ranging from effects of monoamine neurotransmitters, cytokines, corticosteroids and neurotrophins on depression. The hypothesis leads to new insights concerning the relationship between the prolonged inactivity of the positive motivational network during a depressive episode and the loss of neurotrophic support, the potential antidepressant action of corticosteroid treatment, and to the key question of whether antidepressants act by inhibiting the activity of the stress network or by enhancing the activity of the positive motivational system