High Resolution Medical Models and Geometric Reasoning starting from CT/MR Images

The ubiquitous availability of high power' computers has opened up the possibility of handling large (high resolution) volumetric data to accurately represent medical models, and performing geometric reasoning for various applications. In this paper, we present an efficient protocol to reconstruct accurate medical models from CT/MR images having equal or unequal values of slice thickness, inter slice distance, and pixel size. It involves modifying the slice thickness while leaving the in-slice resolution intact; issues such as slice overlap and inter-slice gap are handled using slice based interpolation. Noise reduction and better delineation of object boundaries and segmentation are performed in voxel space. Geometric analysis of reconstructed volumetric data is performed to generate internal thickness mapping, useful for pre-operative planning and custom implant design. A test case of pelvic model reconstruction from CT slices is described to illustrate the algorithms.