Sliding slice: A novel approach for high accuracy and automatic 3D localization of seeds from CT scans

Abstract

We present a conceptually novel principle for 3D reconstruction of prostate seed implants. Unlike existing methods for implant reconstruction, the proposed algorithm uses raw CT data (sinograms) instead of reconstructed CT slices. Using raw CT data solves several inevitable problems related to the reconstruction from CT slices. First, the sinograms are not affected by reconstruction artifacts in the presence of metallic objects and seeds in the patient body. Second, the scanning axis is not undersampled as in the case of CT slices; as a matter of fact the scanning axis is the most densely sampled and each seed is typically represented by several hundred samples. Moreover, the shape of a single seed in a sinogram can be modeled exactly, thus facilitating the detection. All this allows very accurate 3D reconstruction of both position and the orientation of the seeds. Preliminary results indicate that the seed position can be estimated with 0.15 mm accuracy (average), while the orientation estimate accuracy is within 3 deg on average. Although the main contribution of the paper is to present a new principle of reconstruction, a preliminary implementation is also presented as a proof of concept. The implemented algorithm has been tested on a phantom and the obtained results are presented to validate the proposed approach.