Region-driven statistical non rigid registration. Application to model-based segmentation and tracking of the heart in perfusion MRI

Intensity-based Non Rigid Registration (NRR) techniques using statistical similarity measures have been widely used to address mono- and multimodal image alignment problems in a robust and segmentation-free way. In these approaches, registration is achieved by minimizing the discrepancy between luminance distributions. Classical similarity criteria, including mutual information, f-information and correlation ratio, rely on global luminance statistics over the whole image domain and do not incorporate spatial information. This may lead to inaccurate or geometrically inconsistent (though visually satisfying) alignment of homologous image structures, making these criteria unreliable for atlas-based segmentation purposes. This paper addresses these limitations and presents a region-driven approach to statistical NRR based on regional non-parametric estimates of luminance distributions. The latter are derived from a regional segmentation of the target image which is used as a fixed object/scene template and induces regionalized statistical similarity measures. We provide the expressions of these criteria in the case of generalized information measures and correlation ratio, and derive the corresponding gradient flows over parametric and non-parametric transforms spaces. This approach is then applied to the joint non rigid segmentation and registration of short-axis cardiac perfusion MR sequences using a bi-ventricular heart template. In this framework, region-driven NRR allows for compensating for respiratory/cardiac motion artifacts, and fitting a segmental heart model used for quantitatively assessing regional myocardial perfusion. Experiments have been performed on a 15 pathological subjects database, demonstrating the relevance of region-driven NRR over global NRR in terms of computational performance and registration accuracy with respect to an expert reference.