With a steady increase of CT interventions, population dose is increasing. Thus, new approaches must be
developed to reduce the dose. In this paper, we present a means for rapid identification and reconstruction of
objects of interest in reconstructed data. Active shape models are first trained on sets of data obtained from
similar subjects. A reconstruction is performed using a limited number of views. As each view is added, the
reconstruction is evaluated using the active shape models. Once the object of interest is identified, the volume of
interest alone is reconstructed, saving reconstruction time. Note that the data outside of the objects of interest
can be reconstructed using fewer views or lower resolution providing the context of the region of interest data.
An additional feature of our algorithm is that a reliable segmentation of objects of interest is achieved from
a limited set of projections. Evaluations were performed using simulations with Shepp-Logan phantoms and
animal studies. In our evaluations, regions of interest are identified using about 33 projections on average. The
overlap of the identified regions with the true regions of interest is approximately 91%. The identification of the
region of interest requires about 1/5 of the time required for full reconstruction, the time for reconstruction of the
region of interest is currently determined by the fraction of voxels in the region of interest (i.e, voxels in region
of interest/voxels in full volume). The algorithm has several important clinical applications, e.g., rotational
angiography, digital tomosynthesis mammography, and limited view computed tomography.