CAREER: Estimation Methods for Image Registration

Principal Investigator: Marc Niethammer

Funding Agency: National Science Foundation

Agency Number: ECCS-1148870

Abstract:

One of the most central operations in biomedical image analysis is image alignment: image registration. Substantial efforts over the last decades have produced sophisticated registration approaches for applications ranging from microscopy imaging at the nanometer scale to organ-scale imaging. However, neither the underlying assumptions regarding transformation models have been substantially challenged nor have practical methods to quantify uncertainties for deformable registration results been developed. For example, the existing methods are not appropriately designed for many inter-subject registrations or for registrations involving pathologies which are core requirements for many imaging studies. The goal of this research program is to devise advanced estimation methods for image registration, motivated from a dynamical systems point of view which (1) improve estimation of image deformations, (2) improve image similarity measures to account for image changes (such as tissue growth or degeneration), (3) model subject-specific deformations and population-based deformations jointly, (4) allow for the estimation

of registration uncertainties, (5) provide appropriate validation strategies, and (6) to present the methods in an intuitive way understandable for the non-expert. Better image registration methods will improve image analysis results and hence will help clinical studies and ultimately patient care. Application areas include the study of traumatic brain injury, radiation treatment planning, assessment of tumor progression or population studies of osteoarthritis, normal brain development, Alzheimer?s or Huntington?s disease, to name but a few. We will focus on neuroimaging and the capturing of lung motions to highlight distinct characteristics of image registration problems.

Intellectual Merit The proposed research will significantly advance the state-of-the art, because it will provide much needed ways of using domain knowledge when formulating estimation problems in image registration. The hypothesis is that by adding such information, estimations of space deformations will be improved, hence improving the quality of the analysis results which critically depend on image registration. In particular, the developed methods will address the highly relevant problems of how to deal with images subject to a pathology, how to perform population-based analysis while capturing data-trends, and how to assess estimation uncertainty in registration. The research will have immediate impact on current imaging studies and will form the basis for future applications.

Broader Impact While the research will focus on applications in neuroimaging and lung motion analysis, the developed methods will be generally applicable to any image analysis problem requiring an estimate of spatial correspondence ranging from biology, to animation, to object tracking, and to neuroimaging. In particular, since the methods will explicitly address changes on the subject level (including changes caused by pathology or aging) and the population level, they will lead to better estimates of disease progression, will be able to provide patient-specific information, and are expected to improve results for population-based imaging studies (for example for clinical drug testing). To allow others to create customized image analysis solutions all developed methods will be made available to the community in open-source form.