4th Annual Mountain West
Biomedical Engineering Conference

Deformable image registration has been proven to be useful in tracking

organ motion for dose calculation using sets of binned 3D

images. However, such methods are challenged in the presence of image

artifacts. We present an alternative method which avoids binning

artifacts by directly estimating organ deformation during the

reconstruction process.

We have developed a maximum a posteriori (MAP) algorithm for tracking

organ motion that uses raw time-stamped data to reconstruct the images

and estimate deformations in anatomy simultaneously. Since the

algorithm does not rely on a binning process, binning artifacts are

avoided. Signal-to-noise ratio (SNR) is also increased since the

algorithm uses all of the collected data. The method is general and

can be applied to data from a number of modalities including fanbeam

or conebeam CT, MRI, and PET. In the case of CT, the increased SNR

provides the opportunity to reduce dose to the patient during

scanning. This framework also facilitates the incorporation of

fundamental physical properties such as the conservation of local

tissue volume during the estimation of organ motion.

In order to validate the accuracy of the 4D reconstruction algorithm,

a phantom study was performed using the CIRS anthropomorphic thorax

phantom in a fanbeam CT scanner. The algorithm accurately estimated

the known motion of the phantom. Additionally, a significant SNR

increase was observed when using 4D reconstruction over binning, even

for a scan with X-ray tube current reduced to 10%. Similar

improvements were observed upon application of the algorithm to

simulated conebeam CT data and to real fanbeam CT data from a patient

undergoing liver radiation therapy.