Abstract

Medical image data is usually represented by a uniformly spaced grid of voxels. However, CT scanners for example are capable of producing non-uniformly spaced slice images. This is desirable when for a particular patient some regions (lesions) need to be imaged with a high resolution, while a lower resolution would be su#cient in other areas. Such an adaptive slice spacing can significantly reduce X-ray dose, thus directly benefiting the patient. Unfortunately, computational handling of the resulting volume data is far less e#cient than that of uniformly spaced images. To deal with this problem, the present paper introduces a novel data structure for non-uniformly spaced image coordinates, the so-called virtual uniform axes. By a generalization of Euclid's greatest common divider (GCD) algorithm, a table of virtual voxels on a uniform grid is produced. Each of the uniform voxels in the virtual grid holds a pointer to the corresponding voxel in the original, non-uniform grid. Finding a voxel in the virtual uniform image can be done in constant time as compared to logarithmic time for finding a voxel in a non-uniform image. This is achieved with significantly less additional storage than by resampling the image data itself to a uniform grid. Interpolation artifacts are also completely avoided.

Citations