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A projection-driven pre-correction technique for iterative reconstruction of helical cone-beam cardiac CT images

Modern CT systems have advanced at a dramatic rate. Algebraic iterative reconstruction techniques have shown promising and desirable image characteristics, but are seldom used due to their high computational cost for complete reconstruction of large volumetric datasets. In many cases, however, interest in high resolution reconstructions is restricted to smaller regions of interest within the complete volume. In this paper we present an implementation of a simple and practical method to produce iterative reconstructions of reduced-sized ROI from 3D helical tomographic data. We use the observation that the conventional filtered back-projection reconstruction is generally of high quality throughout the entire volume to predict the contributions to ROI-related projections arising from volumes outside the ROI. These predictions are then used to pre-correct the data to produce a tomographic inversion problem of substantially reduced size and memory demands. Our work expands on those of other researchers who have observed similar potential computational gains by exploiting FBP results. We demonstrate our approach using cardiac CT cone-beam imaging, illustrating our results with both ex vivo and in vivo multi-cycle EKG-gated examples.