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A fast and robust method for T₁ estimation in MRI is the so-called variable flip angle technique. We introduce a novel family of T₁ reconstruction methods from data acquired with various flip angles and propose a family member which combines the robustness of a nonlinear- with the computational advantages of a linear reconstruction. The constructed family contains the most common approaches for T₁ estimation, namely a linear and a nonlinear approach. A general sensitivity analysis for arbitrary members of the family is established. Advantages of the optimized reconstruction are demonstrated on phantom- as well as real data, showing improvements of up to 24% as compared with the linear method. As a further means to stabilize T₁ estimation, spatial stabilization methods are compared. We demonstrate on phantom and on real data that improved results can be obtained if not only T₁ but also a second unknown M₀ in the reconstruction is stabilized.

26:T532,

Geodesic methods have been widely applied to image analysis. They are particularly efficient to extract a tubular structure, such as a blood vessel, given its two endpoints in a 2D or 3D medical image. We address here a more difficult problem: the extraction of a full vessel tree structure given a single initial root point, by growing a collection of keypoints or new initial source points, connected by minimal geodesic paths. In this article, those keypoints are iteratively added, using a new detection criteria, which utilize the weighted geodesic distances with respect to a radius-lifted Riemannian metric, the standard Euclidean curve length and a path score. Two main weaknesses of classical keypoints searching approach are that the weighted geodesic distance and the Euclidean path length do not take into account the orientation of the tubular structure or object boundaries, due to the use of an isotropic geodesic Riemannian metric, and suffer from a leakage problem. In contrast, we use an anisotropic geodesic Riemannian metric, and develop new criteria for selecting keypoints based on the path score and automatically stopping the tree growth. Experimental results demonstrate that our method can obtain the expected results, which can extract vessel structures at a finer scale, with increased accuracy.

27:T594,