A generalized framework unifying image registration and respiratory motion models and incorporating image reconstruction, for partial image data or full images.

ABSTRACT: Surrogate-driven respiratory motion models relate the motion of the internal anatomy to easily acquired respiratory surrogate signals, such as the motion of the skin surface. They are usually built by first using image registration to determine the motion from a number of dynamic images, and then fitting a correspondence model relating the motion to the surrogate signals. In this paper we present a generalized framework that unifies the image registration and correspondence model fitting into a single optimization. This allows the use of 'partial' imaging data, such as individual slices, projections, or k-space data, where it would not be possible to determine the motion from an individual frame of data. Motion compensated image reconstruction can also be incorporated using an iterative ap

SUBMITTER: McClelland JR 

PROVIDER: S-EPMC5763581 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature