Quantification of myocardial T1 relaxation has potential value in the diagnosis of both ischemic and non-ischemic cardiomyopathies. Image acquisition using the Modified Look-Locker Inversion Recovery technique is clinically feasible for T1 mapping. However, respiratory motion limits its applicability and degrades the accuracy of T1 estimation. The robust registration of acquired inversion recovery images is particularly challenging due to the large changes in image contrast, especially for those images acquired near the signal null point of the inversion recovery and other inversion times for which there is little tissue contrast. In this paper, we propose a novel motion correction algorithm. This approach is based on estimating synthetic images presenting contrast changes similar to the acquired images. The estimation of synthetic images is formulated as a variational energy minimization problem. Validation on a consecutive patient data cohort shows that this strategy can perform robust non-rigid registration to align inversion recovery images experiencing significant motion and lead to suppression of motion induced artifacts in the T1 map.

Symmetry and inverse consistency are two important features for deformable image registration in medical imaging analysis. This work presents a novel registration method computing symmetric and inverse-consistent image alignment efficiently while preserving high accuracy and consistency of the mapping. This is achieved by optimizing a symmetric energy functional estimating forward and backward transformations constrained by the transformations being inverse to each other. In other words, this approach uses an interleaved optimization scheme borrowed from multiobjective optimization theory constrained by an inverse-consistency criterium. The new optimization scheme provides an efficient search in the space of diffeomorphisms while solving the symmetric registration problem. Moreover, it is not bound to any specific optimizer or energy functional other than to the requirement of being well-defined. In our experiments on clinical cardiac data, superior performance compared to standard, onedirectional registration is achieved. The resulting inverseconsistency and symmetry errors match previously reported values while being computed more efficiently. This general approach addresses a clinical need for consistent, highly accurate image alignment achieved in a practically accepted timeframe.

No abstract

Abstract. The need for non-rigid multi-modal registration is becoming increasingly common for many clinical applications. To date, however, existing proposed techniques remain as largely academic research effort with very few methods being validated for clinical product use. It has been suggested by Crum et al. [1] that the context-free nature of these methods is one of the main limitations and that moving towards context-specific methods by incorporating prior knowledge of the underlying registration problem is necessary to achieve registration results that are accurate and robust enough for clinical applications. In this paper, we propose a novel non-rigid multi-modal registration method using a variational formulation that incorporates a prior learned joint intensity distribution. The registration is achieved by simultaneously minimizing the Kullback-Leibler divergence between an observed and a learned joint intensity distribution and maximizing the mutual information between reference and alignment images. We have applied our proposed method on both synthetic and real images with encouraging results.

We introduce a novel variational approach for evaluating PTV image pairs and sequences in two and three dimensions. We combine a discrete non-differentiable particle matching term with a continuous regularization term. An advanced mathematical method guarantees convergence to a local minimum. The experimental evaluation shows that our variational method competes with three alternative approaches. We outline the potential of our method for further developments.