M R Guelfi | PubMed Health

PubMed1997

World Wide Web interface for advanced SPECT reconstruction algorithms implemented on a remote massively parallel computer.

Data from Single Photon Emission Computed Tomography (SPECT) studies are blurred by inevitable physical phenomena occurring during data acquisition. These errors may be compensated by means of reconstruction algorithms which take into account accurate physical models of the data acquisition procedure. Unfortunately, this approach involves high memory requirements as well as a high computational burden which cannot be afforded by the computer systems of SPECT acquisition devices. In this work the possibility of accessing High Performance Computing and Networking (HPCN) resources through a World Wide Web interface for the advanced reconstruction of SPECT data in a clinical environment was investigated. An iterative algorithm with an accurate model of the variable system response was ported on the Multiple Instruction Multiple Data (MIMD) parallel architecture of a Cray T3D massively parallel computer. The system was accessible even from low cost PC-based workstations through standard TCP/IP networking. A speedup factor of 148 was predicted by the benchmarks run on the Cray T3D. A complete brain study of 30 (64 x 64) slices was reconstructed from a set of 90 (64 x 64) projections with ten iterations of the conjugate gradients algorithm in 9 s which corresponds to an actual speed-up factor of 135. The technique was extended to a more accurate 3D modeling of the system response for a true 3D reconstruction of SPECT data; the reconstruction time of the same data set with this more accurate model was 5 min. This work demonstrates the possibility of exploiting remote HPCN resources from hospital sites by means of low cost workstations using standard communication protocols and an user-friendly WWW interface without particular problems for routine use.

Algorithms↗

PubMed1994

[A proposal for the use of tridimensional reconstruction in oncology to better assess tumor stage and response to therapy].

Besides radiation therapy, diagnostic imaging has always been considered the main medical application of three-dimensional (3D) reconstruction. On the contrary, our study focused mainly on the use of 3D reconstruction for both spatial characterization and morphometric evaluation of the reconstructed objects. We aimed at assisting physicians to solve clinical and therapeutic problems. In particular, in oncology, 3D reconstruction may allow the objective and accurate quantification of the volume of neoplastic lesions. Therefore, we decided to focus our attention on the spatial characterization and morphometric assessment of the examined neoplastic masses. Volumetric measurements based on 3D reconstruction may be of great value to assess volume changes after irradiation and/or chemotherapy of neoplastic lesions. This might also allow to compare, on the basis of such changes, the role of different treatment protocols on similar neoplastic lesions and, possibly, to lead to a new TNM staging system no longer based on 2D measurements but on volumes. To meet these clinical requirements, we developed a software system for accurate volume measurements. We believed 3D reconstruction to be suited to this purpose and therefore we implemented a software incorporating 3D reconstruction capabilities of abnormal anatomical structures from 2D images, the rotation of the volume of interest for better assessment of spatial relationships, and finally morphometric evaluation, for accurate volume measurements. Instead of calculating the volume of a neoplastic lesion by means of a 3D reconstruction algorithm considering voxels as indivisible (voxel-based approach), we implemented a surface rendering algorithm using a cell-based approach, because it allowed voxels to be represented as small volume units, which could be further divided by means of linear interpolation. Thus, great flexibility was possible in the determination of surfaces, together with a good approximation of the volume of the neoplastic lesions. To assess the reliability of the developed software system, we used a real phantom. Its known actual volume was compared with the one measured by our system and the difference, expressed as a percentage of the actual volume itself, was compared with the one obtained by using reconstruction algorithms with a voxel-based approach (1.4% vs 4.4%). The error produced by the latter is three times greater than the one produced by our algorithm. This is a major result for the physician: better approximation of the actual volume of a neoplastic lesion means better evaluation of the number of neoplastic cells in the lesion. This may be useful for the clinical management of the patient. In the paper, the first clinical applications of our algorithm are reported.

Algorithms↗