A Locally Adaptive Registration Technique for High Precision Registration of 3-D MRI Data
Abstract This study demonstrates how the rigid body registration parameters for good registration of serially acquired 3-D magnetic resonance images vary systematically when the registration routine is presented with a series of cropped data sets that are systematically positioned throughout the entire volume. The results of the registration of these subcubes are compared with the results of a single registration of the complete volume for two consecutive 3-D scans of the brain of a normal volunteer, with one scan having optimized shim coil currents and the other having all second-order shim coil currents set to zero. The technique is sensitive and able to reveal subvoxel misregistrations.
Our goal is to characterize the space-varying absorption coefficient from observations of diffuse light propagating through tissue. Sensor array measurements from illumination with several sources leads to an inverse problem which, due to the physics and the restriction of sensors to the surface, is ill-posed and underdetermined. Accurate, reliable solutions require a priori constraints. Here we adopt an admissible solution approach, with convex constraint functions defining admissibility conditions, implemented via the ellipsoid algorithm. The joint use of measurement-model agreement, amplitude, and total variation type constraints produces more accurate reconstructions in a variety of simulation scenarios.