Nonrigid image registration using uncertain surface constraints with application to radiation therapy

An important research problem in image-guided adaptive radiation therapy (IGART) is how to accurately deform daily onboard cone-beam CT (CBCT) images to higher quality pretreatment magnetic resonance (MR) or fan-beam CT (FBCT) images, enabling cumulative dose to be evaluated and tumor response to be tracked. Particularly, in the case of IGART for prostate cancer, the question becomes to accurately register the critical organs, such as bladder, prostate and rectum. All are soft tissue and their boundaries can not always be identified using CBCT. As such it is challenging to register these soft organs precisely if the intensity difference serves as the only similarity measure.

Organ surfaces are often contoured as part of the treatment planning phase. We therefore assume that the organ surfaces are provided either by manual or automatic segmentation and can be used to improve the correspondences at structure boundaries. Unfortunately these segmentations are often inaccurate so that the direct inclusion of the surfaces into the registration process may give little improvement.

Originating from this specific problem, this work tries to answer a more generalized question. Given two intensity images and their associated inaccurate object surfaces, can we design a non-rigid registration algorithm with improved registration accuracy? Influenced by the ideas of data assimilation (DA) and smoothing spline regression (SSR), this report provides a solution consisting of three components: statistical shape modeling, spline-based surface estimation, and surface constrained non-rigid image registration.

We surveyed different surface registration algorithms and evaluated their performance on real patient data. The shape models of the pelvic organs were built using training data. For the image registration, the input surface is a combination of the current observed and the one predicted by the shape model. This hybrid surface was validated to be more accurate and therefore the image registration constrained by it produced smaller registration error. Experiments were performed using both the simulated data and real clinical data. Results show that the proposed method achieves satisfactory improvement.