  Model-based iterative reconstruction (MBIR) techniques have demonstrated many
advantages in X-ray CT image reconstruction. The MBIR approach is often modeled
as a convex optimization problem including a data fitting function and a
penalty function. The tuning parameter value that regulates the strength of the
penalty function is critical for achieving good reconstruction results but
difficult to choose. In this work, we describe two path seeking algorithms that
are capable of efficiently generating a series of MBIR images with different
strengths of the penalty function. The root-mean-squared-differences of the
proposed path seeking algorithms are below 4 HU throughout the entire
reconstruction path. With the efficient path seeking algorithm, we suggest a
path-based iterative reconstruction (PBIR) to obtain complete information from
the scanned data and reconstruction model.
