Incorporation of Axial System Response in Iterative Reconstruction from Axially Compressed Data of a Cylindrical Scanner using On-the-Fly Computing

The quality of images reconstructed by statistical iterative methods depends on an accurate model of the relationship between image space and projection space. Fast reconstruction is also an important aspect in the clinical environment. In previous work, we incorporated measured and modeled system response into iterative reconstruction. Since axial compression (span) was used in projection data, axial blurring was depth-dependent. We use pre-computed and stored axial components for a scanner without translation axial symmetry. For a scanner with translation symmetry, a fast line-of-response (LOR) line integral projector is available. In this case, axial blurring due to both geometrical and physics effects is implemented differently. Forward projection of axially blurred images is performed on-the-fly into LOR space, followed by axial compression of LOR data into scanner projection data formats. Our LOR projector is based on Joseph´s method. The translation symmetry of the scanner is essential for its efficient and fast computing performance. LOR projection and span assembling are performed independently for each azimuthal angle and result in small additional memory allocation. Results show that an acceleration of the computing of the axial blurring is achievable without any component storage but with similar image quality.