Functional MRI activity characterization using response time shift estimates from curve evolution

Spatio-temporal data fusion for 3D+T image reconstruction in cerebral angiography

This paper provides a framework for generating high resolution time sequences of 3D images that show the dynamics of cerebral blood flow. These sequences have the potential to allow image feedback during medical procedures that facilitate the detection and observation of pathological abnormalities such as stenoses, aneurysms, and blood clots. The 3D time series is constructed by fusing a single static 3D model with two time sequences of 2D projections of the same imaged region. The fusion process utilizes a variational approach that constrains the volumes to have both smoothly varying regions separated by edges and sparse regions of nonzero support. The variational problem is solved using a modified version of the Gauss-Seidel algorithm that exploits the spatio-temporal structure of the angiography problem. The 3D time series results are visualized using time series of isosurfaces, synthetic X-rays from arbitrary perspectives or poses, and 3D surfaces that show arrival times of the contrasted blood front using color coding. The derived visualizations provide physicians with a previously unavailable wealth of information that can lead to safer procedures, including quicker localization of flow altering abnormalities such as blood clots, and lower procedural X-ray exposure. Quantitative SNR and other performance analysis of the algorithm on computational phantom data are also presented.

Model-based variational smoothing and segmentation for diffusion tensor imaging in the brain

This article applies a unified approach to variational smoothing and segmentation to brain diffusion tensor image data along user-selected attributes derived from the tensor, with the aim of extracting detailed brain structure information. The application of this framework simultaneously segments and denoises to produce edges and smoothed regions within the white matter of the brain that are relatively homogeneous with respect to the diffusion tensor attributes of choice. This approach enables the visualization of a smoothed, scale invariant representation of the tensor data field in a variety of diverse forms. In addition to known attributes such as fractional anisotropy, these representations include selected directional tensor components and additionally associated continuous valued edge fields that might be used for further segmentation. A comparison is presented of the results of three different data model selections with respect to their ability to resolve white matter structure. The resulting images are integrated to provide better perspective of the model properties (edges, smoothed image, and so forth) and their relationship to the underlying brain anatomy. The improvement in brain image quality is illustrated both qualitatively and quantitatively, and the robust performance of the algorithm in the presence of added noise is shown. Smoothing occurs without loss of edge features because of the simultaneous segmentation aspect of the variational approach, and the output enables better delineation of tensors representative of local and long-range association, projection, and commissural fiber systems.