Rapid cardiovascular computed tomography (CT) requires image reconstruction from a limited set of projections. Deterministic methods using convolution back projection algorithms have not been suitable for these limited data cases. An approach is formulated to find the minimum mean squared error image estimate using a Kalman filter with polar pixels for two-dimensional reconstructions of both simulated phantoms and real objects from data obtained on a rotate only CT scanner. Computation time was minimized by limiting the number of pixels to 120 and using a rotationally symmetric (polar) pixel structure. The Kalman filter was compared with Algebraic Reconstruction Technique (ART) for full view, limited view, and missing view measurement sets. The Kalman filter performed with consistently lower mean squared error than ART for both real and simulated data and rapidly converged to the theoretical limit of resolution. Performance of the Kalman filter was optimized only if the system noise (error) was adequately characterized. This study demonstrates the potential utility of Kalman filtering methods using polar pixels for limited data CT image reconstruction.
|Original language||English (US)|
|Number of pages||6|
|Journal||Proceedings of the Society of Photo-Optical Instrumentation Engineers|
|State||Published - 1979|
ASJC Scopus subject areas